diff --git a/pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx b/pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx new file mode 100644 index 0000000000000000000000000000000000000000..4781aa1f1884525a316cff689b3db18b9f38d85d --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx @@ -0,0 +1,165 @@ +# # Ensemble Adversarial Inception ResNet v2 + +**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture). + +This particular model was trained for study of adversarial examples (adversarial training). + +The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('ens_adv_inception_resnet_v2', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `ens_adv_inception_resnet_v2`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('ens_adv_inception_resnet_v2', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1804-00097, + author = {Alexey Kurakin and + Ian J. Goodfellow and + Samy Bengio and + Yinpeng Dong and + Fangzhou Liao and + Ming Liang and + Tianyu Pang and + Jun Zhu and + Xiaolin Hu and + Cihang Xie and + Jianyu Wang and + Zhishuai Zhang and + Zhou Ren and + Alan L. Yuille and + Sangxia Huang and + Yao Zhao and + Yuzhe Zhao and + Zhonglin Han and + Junjiajia Long and + Yerkebulan Berdibekov and + Takuya Akiba and + Seiya Tokui and + Motoki Abe}, + title = {Adversarial Attacks and Defences Competition}, + journal = {CoRR}, + volume = {abs/1804.00097}, + year = {2018}, + url = {http://arxiv.org/abs/1804.00097}, + archivePrefix = {arXiv}, + eprint = {1804.00097}, + timestamp = {Thu, 31 Oct 2019 16:31:22 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-1804-00097.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx b/pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..d92a603440502ac7808cd132696ab7ad5d15d428 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx @@ -0,0 +1,159 @@ +# ESE-VoVNet + +**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel. + +Read about [one-shot aggregation here](https://paperswithcode.com/method/one-shot-aggregation). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('ese_vovnet19b_dw', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `ese_vovnet19b_dw`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('ese_vovnet19b_dw', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{lee2019energy, + title={An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection}, + author={Youngwan Lee and Joong-won Hwang and Sangrok Lee and Yuseok Bae and Jongyoul Park}, + year={2019}, + eprint={1904.09730}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx b/pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..dd4cd03364b8c4a78ccc7f4b59352002283b7277 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx @@ -0,0 +1,209 @@ +# (Gluon) ResNeXt + +A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) \\( C \\), as an essential factor in addition to the dimensions of depth and width. + +The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('gluon_resnext101_32x4d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `gluon_resnext101_32x4d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('gluon_resnext101_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/XieGDTH16, + author = {Saining Xie and + Ross B. Girshick and + Piotr Doll{\'{a}}r and + Zhuowen Tu and + Kaiming He}, + title = {Aggregated Residual Transformations for Deep Neural Networks}, + journal = {CoRR}, + volume = {abs/1611.05431}, + year = {2016}, + url = {http://arxiv.org/abs/1611.05431}, + archivePrefix = {arXiv}, + eprint = {1611.05431}, + timestamp = {Mon, 13 Aug 2018 16:45:58 +0200}, + biburl = {https://dblp.org/rec/journals/corr/XieGDTH16.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx b/pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..bb43215549cd20ce3391ad912b122a4830e81b39 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/gloun-seresnext.mdx @@ -0,0 +1,203 @@ +# (Gluon) SE-ResNeXt + +**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration. + +The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('gluon_seresnext101_32x4d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `gluon_seresnext101_32x4d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('gluon_seresnext101_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{hu2019squeezeandexcitation, + title={Squeeze-and-Excitation Networks}, + author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu}, + year={2019}, + eprint={1709.01507}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/gloun-xception.mdx b/pytorch-image-models/hfdocs/source/models/gloun-xception.mdx new file mode 100644 index 0000000000000000000000000000000000000000..0dee376e7cd57d0d9f2a5a8d17e9ef0f1176948d --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/gloun-xception.mdx @@ -0,0 +1,133 @@ +# (Gluon) Xception + +**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution](https://paperswithcode.com/method/depthwise-separable-convolution) layers. + +The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('gluon_xception65', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `gluon_xception65`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('gluon_xception65', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{chollet2017xception, + title={Xception: Deep Learning with Depthwise Separable Convolutions}, + author={François Chollet}, + year={2017}, + eprint={1610.02357}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/hrnet.mdx b/pytorch-image-models/hfdocs/source/models/hrnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..3437b96b79bb84b4e27b3857778950d3000af0e4 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/hrnet.mdx @@ -0,0 +1,425 @@ +# HRNet + +**HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several (\\( 4 \\) in the paper) stages and the \\( n \\)th stage contains \\( n \\) streams corresponding to \\( n \\) resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('hrnet_w18', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `hrnet_w18`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('hrnet_w18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{sun2019highresolution, + title={High-Resolution Representations for Labeling Pixels and Regions}, + author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang}, + year={2019}, + eprint={1904.04514}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/ig-resnext.mdx b/pytorch-image-models/hfdocs/source/models/ig-resnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..d5a035ba63f29bfa1ba729f418df4c5cd035e85e --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/ig-resnext.mdx @@ -0,0 +1,276 @@ +# Instagram ResNeXt WSL + +A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) \\( C \\), as an essential factor in addition to the dimensions of depth and width. + +This model was trained on billions of Instagram images using thousands of distinct hashtags as labels exhibit excellent transfer learning performance. + +Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('ig_resnext101_32x16d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `ig_resnext101_32x16d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('ig_resnext101_32x16d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{mahajan2018exploring, + title={Exploring the Limits of Weakly Supervised Pretraining}, + author={Dhruv Mahajan and Ross Girshick and Vignesh Ramanathan and Kaiming He and Manohar Paluri and Yixuan Li and Ashwin Bharambe and Laurens van der Maaten}, + year={2018}, + eprint={1805.00932}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx b/pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx new file mode 100644 index 0000000000000000000000000000000000000000..c4eb15869d8526ba67c60c0f707c1b81cd7f143f --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx @@ -0,0 +1,139 @@ +# Inception ResNet v2 + +**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('inception_resnet_v2', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `inception_resnet_v2`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('inception_resnet_v2', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{szegedy2016inceptionv4, + title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning}, + author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi}, + year={2016}, + eprint={1602.07261}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/inception-v3.mdx b/pytorch-image-models/hfdocs/source/models/inception-v3.mdx new file mode 100644 index 0000000000000000000000000000000000000000..cd21e5de0f319c70891e1e22ce1180868013113b --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/inception-v3.mdx @@ -0,0 +1,152 @@ +# Inception v3 + +**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('inception_v3', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `inception_v3`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('inception_v3', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/SzegedyVISW15, + author = {Christian Szegedy and + Vincent Vanhoucke and + Sergey Ioffe and + Jonathon Shlens and + Zbigniew Wojna}, + title = {Rethinking the Inception Architecture for Computer Vision}, + journal = {CoRR}, + volume = {abs/1512.00567}, + year = {2015}, + url = {http://arxiv.org/abs/1512.00567}, + archivePrefix = {arXiv}, + eprint = {1512.00567}, + timestamp = {Mon, 13 Aug 2018 16:49:07 +0200}, + biburl = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/inception-v4.mdx b/pytorch-image-models/hfdocs/source/models/inception-v4.mdx new file mode 100644 index 0000000000000000000000000000000000000000..f9b4adf05157b2c87b6630fe932a89338e1ee41a --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/inception-v4.mdx @@ -0,0 +1,138 @@ +# Inception v4 + +**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3). +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('inception_v4', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `inception_v4`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('inception_v4', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{szegedy2016inceptionv4, + title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning}, + author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi}, + year={2016}, + eprint={1602.07261}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx b/pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..68ed5a30b9bf3ce8255b6e0d2a47d9a83dd731f3 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx @@ -0,0 +1,324 @@ +# (Legacy) SE-ResNet + +**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('legacy_seresnet101', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `legacy_seresnet101`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('legacy_seresnet101', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{hu2019squeezeandexcitation, + title={Squeeze-and-Excitation Networks}, + author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu}, + year={2019}, + eprint={1709.01507}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx b/pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..8ff527f3dabb4f3a28b9cd87de02475ab989ad60 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx @@ -0,0 +1,234 @@ +# (Legacy) SE-ResNeXt + +**SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('legacy_seresnext101_32x4d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `legacy_seresnext101_32x4d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('legacy_seresnext101_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{hu2019squeezeandexcitation, + title={Squeeze-and-Excitation Networks}, + author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu}, + year={2019}, + eprint={1709.01507}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/legacy-senet.mdx b/pytorch-image-models/hfdocs/source/models/legacy-senet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..9768d868e07710b55bbdc174cdb766458c6985c9 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/legacy-senet.mdx @@ -0,0 +1,141 @@ +# (Legacy) SENet + +A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration. + +The weights from this model were ported from Gluon. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('legacy_senet154', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `legacy_senet154`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('legacy_senet154', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{hu2019squeezeandexcitation, + title={Squeeze-and-Excitation Networks}, + author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu}, + year={2019}, + eprint={1709.01507}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/mixnet.mdx b/pytorch-image-models/hfdocs/source/models/mixnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..2e058027eaabd2095c80ac6ad2a430d2072bf36e --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/mixnet.mdx @@ -0,0 +1,231 @@ +# MixNet + +**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('mixnet_l', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `mixnet_l`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('mixnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{tan2019mixconv, + title={MixConv: Mixed Depthwise Convolutional Kernels}, + author={Mingxing Tan and Quoc V. Le}, + year={2019}, + eprint={1907.09595}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/mnasnet.mdx b/pytorch-image-models/hfdocs/source/models/mnasnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..e49c06cc73da9d14a9d3b34896aff6a011c3b14d --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/mnasnet.mdx @@ -0,0 +1,176 @@ +# MnasNet + +**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an [inverted residual block](https://paperswithcode.com/method/inverted-residual-block) (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('mnasnet_100', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `mnasnet_100`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('mnasnet_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{tan2019mnasnet, + title={MnasNet: Platform-Aware Neural Architecture Search for Mobile}, + author={Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le}, + year={2019}, + eprint={1807.11626}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx b/pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx new file mode 100644 index 0000000000000000000000000000000000000000..507ec99dd22f55998f5002c45ff929efc414781f --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx @@ -0,0 +1,277 @@ +# MobileNet v2 + +**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. As a whole, the architecture of MobileNetV2 contains the initial fully convolution layer with 32 filters, followed by 19 residual bottleneck layers. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('mobilenetv2_100', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `mobilenetv2_100`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('mobilenetv2_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1801-04381, + author = {Mark Sandler and + Andrew G. Howard and + Menglong Zhu and + Andrey Zhmoginov and + Liang{-}Chieh Chen}, + title = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, + Detection and Segmentation}, + journal = {CoRR}, + volume = {abs/1801.04381}, + year = {2018}, + url = {http://arxiv.org/abs/1801.04381}, + archivePrefix = {arXiv}, + eprint = {1801.04381}, + timestamp = {Tue, 12 Jan 2021 15:30:06 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-1801-04381.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx b/pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx new file mode 100644 index 0000000000000000000000000000000000000000..e1ff317ba2500f30ac46b6e9b265e9f1ace4e28e --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx @@ -0,0 +1,205 @@ +# MobileNet v3 + +**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in the [MBConv blocks](https://paperswithcode.com/method/inverted-residual-block). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `mobilenetv3_large_100`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1905-02244, + author = {Andrew Howard and + Mark Sandler and + Grace Chu and + Liang{-}Chieh Chen and + Bo Chen and + Mingxing Tan and + Weijun Wang and + Yukun Zhu and + Ruoming Pang and + Vijay Vasudevan and + Quoc V. Le and + Hartwig Adam}, + title = {Searching for MobileNetV3}, + journal = {CoRR}, + volume = {abs/1905.02244}, + year = {2019}, + url = {http://arxiv.org/abs/1905.02244}, + archivePrefix = {arXiv}, + eprint = {1905.02244}, + timestamp = {Tue, 12 Jan 2021 15:30:06 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/nasnet.mdx b/pytorch-image-models/hfdocs/source/models/nasnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..2cde9d2d61cca8deb82a093defa9c8dd02c43e7f --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/nasnet.mdx @@ -0,0 +1,137 @@ +# NASNet + +**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('nasnetalarge', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `nasnetalarge`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('nasnetalarge', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{zoph2018learning, + title={Learning Transferable Architectures for Scalable Image Recognition}, + author={Barret Zoph and Vijay Vasudevan and Jonathon Shlens and Quoc V. Le}, + year={2018}, + eprint={1707.07012}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/noisy-student.mdx b/pytorch-image-models/hfdocs/source/models/noisy-student.mdx new file mode 100644 index 0000000000000000000000000000000000000000..2d12e8c870cdcd434a50ba4999251a54d9105f60 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/noisy-student.mdx @@ -0,0 +1,577 @@ +# Noisy Student (EfficientNet) + +**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training +and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps: + +1. train a teacher model on labeled images +2. use the teacher to generate pseudo labels on unlabeled images +3. train a student model on the combination of labeled images and pseudo labeled images. + +The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. + +Noisy Student Training seeks to improve on self-training and distillation in two ways. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Second, it adds noise to the student so the noised student is forced to learn harder from the pseudo labels. To noise the student, it uses input noise such as RandAugment data augmentation, and model noise such as dropout and stochastic depth during training. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b0_ns`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{xie2020selftraining, + title={Self-training with Noisy Student improves ImageNet classification}, + author={Qizhe Xie and Minh-Thang Luong and Eduard Hovy and Quoc V. Le}, + year={2020}, + eprint={1911.04252}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/pnasnet.mdx b/pytorch-image-models/hfdocs/source/models/pnasnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..fff5851cdcb5e64b403dcc7ab463616902c52b35 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/pnasnet.mdx @@ -0,0 +1,138 @@ +# PNASNet + +**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('pnasnet5large', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `pnasnet5large`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('pnasnet5large', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{liu2018progressive, + title={Progressive Neural Architecture Search}, + author={Chenxi Liu and Barret Zoph and Maxim Neumann and Jonathon Shlens and Wei Hua and Li-Jia Li and Li Fei-Fei and Alan Yuille and Jonathan Huang and Kevin Murphy}, + year={2018}, + eprint={1712.00559}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/regnetx.mdx b/pytorch-image-models/hfdocs/source/models/regnetx.mdx new file mode 100644 index 0000000000000000000000000000000000000000..0a9f8e4b2c1724f2702ff77c9784141597f3ccb9 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/regnetx.mdx @@ -0,0 +1,559 @@ +# RegNetX + +**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure): + +\\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\) + +For **RegNetX** we have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('regnetx_002', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `regnetx_002`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('regnetx_002', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{radosavovic2020designing, + title={Designing Network Design Spaces}, + author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, + year={2020}, + eprint={2003.13678}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/regnety.mdx b/pytorch-image-models/hfdocs/source/models/regnety.mdx new file mode 100644 index 0000000000000000000000000000000000000000..be1a5c1c7507e4d99cf6ef00920751b1c304117c --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/regnety.mdx @@ -0,0 +1,573 @@ +# RegNetY + +**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure): + +\\( \\) u\_{j} = w\_{0} + w\_{a}\cdot{j} \\( \\) + +For **RegNetX** authors have additional restrictions: we set \\( b = 1 \\) (the bottleneck ratio), \\( 12 \leq d \leq 28 \\), and \\( w\_{m} \geq 2 \\) (the width multiplier). + +For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('regnety_002', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `regnety_002`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('regnety_002', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{radosavovic2020designing, + title={Designing Network Design Spaces}, + author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, + year={2020}, + eprint={2003.13678}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/res2net.mdx b/pytorch-image-models/hfdocs/source/models/res2net.mdx new file mode 100644 index 0000000000000000000000000000000000000000..751cf35d42fc6c7a67d9b3ca9e126c26f7779f27 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/res2net.mdx @@ -0,0 +1,327 @@ +# Res2Net + +**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('res2net101_26w_4s', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `res2net101_26w_4s`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('res2net101_26w_4s', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{Gao_2021, + title={Res2Net: A New Multi-Scale Backbone Architecture}, + volume={43}, + ISSN={1939-3539}, + url={http://dx.doi.org/10.1109/TPAMI.2019.2938758}, + DOI={10.1109/tpami.2019.2938758}, + number={2}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, + year={2021}, + month={Feb}, + pages={652–662} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/res2next.mdx b/pytorch-image-models/hfdocs/source/models/res2next.mdx new file mode 100644 index 0000000000000000000000000000000000000000..8c883dff7a67eb068a79e942984ffa06c698d62a --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/res2next.mdx @@ -0,0 +1,142 @@ +# Res2NeXt + +**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('res2next50', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `res2next50`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('res2next50', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{Gao_2021, + title={Res2Net: A New Multi-Scale Backbone Architecture}, + volume={43}, + ISSN={1939-3539}, + url={http://dx.doi.org/10.1109/TPAMI.2019.2938758}, + DOI={10.1109/tpami.2019.2938758}, + number={2}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, + year={2021}, + month={Feb}, + pages={652–662} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/resnet.mdx b/pytorch-image-models/hfdocs/source/models/resnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..3f3641f785f5236ba095095c18632b6efc4d4db8 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/resnet.mdx @@ -0,0 +1,445 @@ +# ResNet + +**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('resnet18', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `resnet18`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('resnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/HeZRS15, + author = {Kaiming He and + Xiangyu Zhang and + Shaoqing Ren and + Jian Sun}, + title = {Deep Residual Learning for Image Recognition}, + journal = {CoRR}, + volume = {abs/1512.03385}, + year = {2015}, + url = {http://arxiv.org/abs/1512.03385}, + archivePrefix = {arXiv}, + eprint = {1512.03385}, + timestamp = {Wed, 17 Apr 2019 17:23:45 +0200}, + biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/resnext.mdx b/pytorch-image-models/hfdocs/source/models/resnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..7c5eb166e18d07188e00e276a3e2b4ea4023466f --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/resnext.mdx @@ -0,0 +1,250 @@ +# ResNeXt + +A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) \\( C \\), as an essential factor in addition to the dimensions of depth and width. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('resnext101_32x8d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `resnext101_32x8d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('resnext101_32x8d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/XieGDTH16, + author = {Saining Xie and + Ross B. Girshick and + Piotr Doll{\'{a}}r and + Zhuowen Tu and + Kaiming He}, + title = {Aggregated Residual Transformations for Deep Neural Networks}, + journal = {CoRR}, + volume = {abs/1611.05431}, + year = {2016}, + url = {http://arxiv.org/abs/1611.05431}, + archivePrefix = {arXiv}, + eprint = {1611.05431}, + timestamp = {Mon, 13 Aug 2018 16:45:58 +0200}, + biburl = {https://dblp.org/rec/journals/corr/XieGDTH16.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/se-resnet.mdx b/pytorch-image-models/hfdocs/source/models/se-resnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..ba884bde95599de81b2235a5dc387de659b794eb --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/se-resnet.mdx @@ -0,0 +1,189 @@ +# SE-ResNet + +**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('seresnet152d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `seresnet152d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('seresnet152d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{hu2019squeezeandexcitation, + title={Squeeze-and-Excitation Networks}, + author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu}, + year={2019}, + eprint={1709.01507}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/selecsls.mdx b/pytorch-image-models/hfdocs/source/models/selecsls.mdx new file mode 100644 index 0000000000000000000000000000000000000000..12813ba864d3a8ae8056c26ec8ff83c49d8206c5 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/selecsls.mdx @@ -0,0 +1,203 @@ +# SelecSLS + +**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('selecsls42b', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `selecsls42b`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('selecsls42b', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{Mehta_2020, + title={XNect}, + volume={39}, + ISSN={1557-7368}, + url={http://dx.doi.org/10.1145/3386569.3392410}, + DOI={10.1145/3386569.3392410}, + number={4}, + journal={ACM Transactions on Graphics}, + publisher={Association for Computing Machinery (ACM)}, + author={Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Elgharib, Mohamed and Fua, Pascal and Seidel, Hans-Peter and Rhodin, Helge and Pons-Moll, Gerard and Theobalt, Christian}, + year={2020}, + month={Jul} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/skresnet.mdx b/pytorch-image-models/hfdocs/source/models/skresnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..643a6cbcd86f466939f1cf4881bd4b4851695697 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/skresnet.mdx @@ -0,0 +1,179 @@ +# SK-ResNet + +**SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('skresnet18', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `skresnet18`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('skresnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{li2019selective, + title={Selective Kernel Networks}, + author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang}, + year={2019}, + eprint={1903.06586}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/skresnext.mdx b/pytorch-image-models/hfdocs/source/models/skresnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..ae499b34dad41baa4cdf69fd541279a0c6a82eb6 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/skresnext.mdx @@ -0,0 +1,137 @@ +# SK-ResNeXt + +**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('skresnext50_32x4d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `skresnext50_32x4d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('skresnext50_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{li2019selective, + title={Selective Kernel Networks}, + author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang}, + year={2019}, + eprint={1903.06586}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/spnasnet.mdx b/pytorch-image-models/hfdocs/source/models/spnasnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..a589b0fcdd94ee3b20bc5786f54f9812bec462a5 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/spnasnet.mdx @@ -0,0 +1,129 @@ +# SPNASNet + +**Single-Path NAS** is a novel differentiable NAS method for designing hardware-efficient ConvNets in less than 4 hours. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('spnasnet_100', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `spnasnet_100`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('spnasnet_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{stamoulis2019singlepath, + title={Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours}, + author={Dimitrios Stamoulis and Ruizhou Ding and Di Wang and Dimitrios Lymberopoulos and Bodhi Priyantha and Jie Liu and Diana Marculescu}, + year={2019}, + eprint={1904.02877}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx b/pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..2e1d0a7a3d0f78ccc1b04a31b56317984714f75a --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx @@ -0,0 +1,198 @@ +# SSL ResNet + +**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. + +The model in this collection utilises semi-supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification. + +Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('ssl_resnet18', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `ssl_resnet18`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('ssl_resnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1905-00546, + author = {I. Zeki Yalniz and + Herv{\'{e}} J{\'{e}}gou and + Kan Chen and + Manohar Paluri and + Dhruv Mahajan}, + title = {Billion-scale semi-supervised learning for image classification}, + journal = {CoRR}, + volume = {abs/1905.00546}, + year = {2019}, + url = {http://arxiv.org/abs/1905.00546}, + archivePrefix = {arXiv}, + eprint = {1905.00546}, + timestamp = {Mon, 28 Sep 2020 08:19:37 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx b/pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..2def3c868e06025ebde566b206fd42243a2ec7b1 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx @@ -0,0 +1,198 @@ +# SWSL ResNet + +**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. + +The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification. + +Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('swsl_resnet18', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `swsl_resnet18`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('swsl_resnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1905-00546, + author = {I. Zeki Yalniz and + Herv{\'{e}} J{\'{e}}gou and + Kan Chen and + Manohar Paluri and + Dhruv Mahajan}, + title = {Billion-scale semi-supervised learning for image classification}, + journal = {CoRR}, + volume = {abs/1905.00546}, + year = {2019}, + url = {http://arxiv.org/abs/1905.00546}, + archivePrefix = {arXiv}, + eprint = {1905.00546}, + timestamp = {Mon, 28 Sep 2020 08:19:37 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx b/pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx new file mode 100644 index 0000000000000000000000000000000000000000..66297082f0a1b4b8fc46f8cf33fb46a8ecda94a7 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx @@ -0,0 +1,284 @@ +# SWSL ResNeXt + +A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) \\( C \\), as an essential factor in addition to the dimensions of depth and width. + +The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification. + +Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only. + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('swsl_resnext101_32x16d', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `swsl_resnext101_32x16d`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('swsl_resnext101_32x16d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1905-00546, + author = {I. Zeki Yalniz and + Herv{\'{e}} J{\'{e}}gou and + Kan Chen and + Manohar Paluri and + Dhruv Mahajan}, + title = {Billion-scale semi-supervised learning for image classification}, + journal = {CoRR}, + volume = {abs/1905.00546}, + year = {2019}, + url = {http://arxiv.org/abs/1905.00546}, + archivePrefix = {arXiv}, + eprint = {1905.00546}, + timestamp = {Mon, 28 Sep 2020 08:19:37 +0200}, + biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx b/pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx new file mode 100644 index 0000000000000000000000000000000000000000..f72eaf0c383c151989362bc04853eb91575cd715 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx @@ -0,0 +1,262 @@ +# (Tensorflow) EfficientNet Lite + +**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a principled way. + +The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image. + +The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2). + +EfficientNet-Lite makes EfficientNet more suitable for mobile devices by introducing [ReLU6](https://paperswithcode.com/method/relu6) activation functions and removing [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation). + +The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('tf_efficientnet_lite0', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `tf_efficientnet_lite0`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('tf_efficientnet_lite0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{tan2020efficientnet, + title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, + author={Mingxing Tan and Quoc V. Le}, + year={2020}, + eprint={1905.11946}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx b/pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..16be0a71b69be62bf8a18273feee14c32d30931f --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx @@ -0,0 +1,669 @@ +# (Tensorflow) EfficientNet + +**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use \\( 2^N \\) times more computational resources, then we can simply increase the network depth by \\( \alpha ^ N \\), width by \\( \beta ^ N \\), and image size by \\( \gamma ^ N \\), where \\( \alpha, \beta, \gamma \\) are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient \\( \phi \\) to uniformly scales network width, depth, and resolution in a principled way. + +The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image. + +The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block). + +The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('tf_efficientnet_b0', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b0`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('tf_efficientnet_b0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{tan2020efficientnet, + title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, + author={Mingxing Tan and Quoc V. Le}, + year={2020}, + eprint={1905.11946}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + diff --git a/pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx b/pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..d1ddda09fbab9dea98bc946ec358522691dc5e4d --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx @@ -0,0 +1,200 @@ +# (Tensorflow) MixNet + +**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution). + +The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('tf_mixnet_l', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `tf_mixnet_l`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('tf_mixnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{tan2019mixconv, + title={MixConv: Mixed Depthwise Convolutional Kernels}, + author={Mingxing Tan and Quoc V. Le}, + year={2019}, + eprint={1907.09595}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx b/pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx new file mode 100644 index 0000000000000000000000000000000000000000..3e8c6cba3022102122b4c6788fb1f66e56d660c4 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx @@ -0,0 +1,387 @@ +# (Tensorflow) MobileNet v3 + +**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in the [MBConv blocks](https://paperswithcode.com/method/inverted-residual-block). + +The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('tf_mobilenetv3_large_075', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `tf_mobilenetv3_large_075`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('tf_mobilenetv3_large_075', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/abs-1905-02244, + author = {Andrew Howard and + Mark Sandler and + Grace Chu and + Liang{-}Chieh Chen and + Bo Chen and + Mingxing Tan and + Weijun Wang and + Yukun Zhu and + Ruoming Pang and + Vijay Vasudevan and + Quoc V. Le and + Hartwig Adam}, + title = {Searching for MobileNetV3}, + journal = {CoRR}, + volume = {abs/1905.02244}, + year = {2019}, + url = {http://arxiv.org/abs/1905.02244}, + archivePrefix = {arXiv}, + eprint = {1905.02244}, + timestamp = {Tue, 12 Jan 2021 15:30:06 +0100}, + biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/tresnet.mdx b/pytorch-image-models/hfdocs/source/models/tresnet.mdx new file mode 100644 index 0000000000000000000000000000000000000000..f2d5729d3a9e629d7dea5f0e400d28b4844703bd --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/tresnet.mdx @@ -0,0 +1,358 @@ +# TResNet + +A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and [squeeze-and-excitation layers](https://paperswithcode.com/method/squeeze-and-excitation-block). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('tresnet_l', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `tresnet_l`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('tresnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@misc{ridnik2020tresnet, + title={TResNet: High Performance GPU-Dedicated Architecture}, + author={Tal Ridnik and Hussam Lawen and Asaf Noy and Emanuel Ben Baruch and Gilad Sharir and Itamar Friedman}, + year={2020}, + eprint={2003.13630}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/models/xception.mdx b/pytorch-image-models/hfdocs/source/models/xception.mdx new file mode 100644 index 0000000000000000000000000000000000000000..f67e482c20d0a7f43a8205415bb77a2a071ac372 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/models/xception.mdx @@ -0,0 +1,230 @@ +# Xception + +**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution). + +The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models). + +## How do I use this model on an image? + +To load a pretrained model: + +```py +>>> import timm +>>> model = timm.create_model('xception', pretrained=True) +>>> model.eval() +``` + +To load and preprocess the image: + +```py +>>> import urllib +>>> from PIL import Image +>>> from timm.data import resolve_data_config +>>> from timm.data.transforms_factory import create_transform + +>>> config = resolve_data_config({}, model=model) +>>> transform = create_transform(**config) + +>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") +>>> urllib.request.urlretrieve(url, filename) +>>> img = Image.open(filename).convert('RGB') +>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension +``` + +To get the model predictions: + +```py +>>> import torch +>>> with torch.no_grad(): +... out = model(tensor) +>>> probabilities = torch.nn.functional.softmax(out[0], dim=0) +>>> print(probabilities.shape) +>>> # prints: torch.Size([1000]) +``` + +To get the top-5 predictions class names: + +```py +>>> # Get imagenet class mappings +>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") +>>> urllib.request.urlretrieve(url, filename) +>>> with open("imagenet_classes.txt", "r") as f: +... categories = [s.strip() for s in f.readlines()] + +>>> # Print top categories per image +>>> top5_prob, top5_catid = torch.topk(probabilities, 5) +>>> for i in range(top5_prob.size(0)): +... print(categories[top5_catid[i]], top5_prob[i].item()) +>>> # prints class names and probabilities like: +>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] +``` + +Replace the model name with the variant you want to use, e.g. `xception`. You can find the IDs in the model summaries at the top of this page. + +To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use. + +## How do I finetune this model? + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('xception', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` +To finetune on your own dataset, you have to write a training loop or adapt [timm's training +script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. + +## How do I train this model? + +You can follow the [timm recipe scripts](../scripts) for training a new model afresh. + +## Citation + +```BibTeX +@article{DBLP:journals/corr/ZagoruykoK16, +@misc{chollet2017xception, + title={Xception: Deep Learning with Depthwise Separable Convolutions}, + author={François Chollet}, + year={2017}, + eprint={1610.02357}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + + \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/reference/data.mdx b/pytorch-image-models/hfdocs/source/reference/data.mdx new file mode 100644 index 0000000000000000000000000000000000000000..b50487391e2b3049dfc5386b7deed42c1173c974 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/reference/data.mdx @@ -0,0 +1,9 @@ +# Data + +[[autodoc]] timm.data.create_dataset + +[[autodoc]] timm.data.create_loader + +[[autodoc]] timm.data.create_transform + +[[autodoc]] timm.data.resolve_data_config \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/reference/optimizers.mdx b/pytorch-image-models/hfdocs/source/reference/optimizers.mdx new file mode 100644 index 0000000000000000000000000000000000000000..4957f54f0c17b62fdb8fd57f3e9050f81c5a299b --- /dev/null +++ b/pytorch-image-models/hfdocs/source/reference/optimizers.mdx @@ -0,0 +1,33 @@ +# Optimization + +This page contains the API reference documentation for learning rate optimizers included in `timm`. + +## Optimizers + +### Factory functions + +[[autodoc]] timm.optim.create_optimizer_v2 +[[autodoc]] timm.optim.list_optimizers +[[autodoc]] timm.optim.get_optimizer_class + +### Optimizer Classes + +[[autodoc]] timm.optim.adabelief.AdaBelief +[[autodoc]] timm.optim.adafactor.Adafactor +[[autodoc]] timm.optim.adafactor_bv.AdafactorBigVision +[[autodoc]] timm.optim.adahessian.Adahessian +[[autodoc]] timm.optim.adamp.AdamP +[[autodoc]] timm.optim.adan.Adan +[[autodoc]] timm.optim.adopt.Adopt +[[autodoc]] timm.optim.lamb.Lamb +[[autodoc]] timm.optim.laprop.LaProp +[[autodoc]] timm.optim.lars.Lars +[[autodoc]] timm.optim.lion.Lion +[[autodoc]] timm.optim.lookahead.Lookahead +[[autodoc]] timm.optim.madgrad.MADGRAD +[[autodoc]] timm.optim.mars.Mars +[[autodoc]] timm.optim.nadamw.NAdamW +[[autodoc]] timm.optim.nvnovograd.NvNovoGrad +[[autodoc]] timm.optim.rmsprop_tf.RMSpropTF +[[autodoc]] timm.optim.sgdp.SGDP +[[autodoc]] timm.optim.sgdw.SGDW \ No newline at end of file diff --git a/pytorch-image-models/hfdocs/source/reference/schedulers.mdx b/pytorch-image-models/hfdocs/source/reference/schedulers.mdx new file mode 100644 index 0000000000000000000000000000000000000000..c44577d6a7dc5d09144a865e62b698b8e2413cb1 --- /dev/null +++ b/pytorch-image-models/hfdocs/source/reference/schedulers.mdx @@ -0,0 +1,19 @@ +# Learning Rate Schedulers + +This page contains the API reference documentation for learning rate schedulers included in `timm`. + +## Schedulers + +### Factory functions + +[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler +[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2 + +### Scheduler Classes + +[[autodoc]] timm.scheduler.cosine_lr.CosineLRScheduler +[[autodoc]] timm.scheduler.multistep_lr.MultiStepLRScheduler +[[autodoc]] timm.scheduler.plateau_lr.PlateauLRScheduler +[[autodoc]] timm.scheduler.poly_lr.PolyLRScheduler +[[autodoc]] timm.scheduler.step_lr.StepLRScheduler +[[autodoc]] timm.scheduler.tanh_lr.TanhLRScheduler diff --git a/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..f72204ee1d29b766eef552efe01b4fdab6eba0d0 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv @@ -0,0 +1,933 @@ +model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count +tinynet_e,49277.65,20.77,1024,106,0.03,0.69,2.04 +mobilenetv3_small_050,45562.75,22.464,1024,224,0.03,0.92,1.59 +lcnet_035,41026.68,24.949,1024,224,0.03,1.04,1.64 +lcnet_050,37575.13,27.242,1024,224,0.05,1.26,1.88 +mobilenetv3_small_075,33062.39,30.961,1024,224,0.05,1.3,2.04 +mobilenetv3_small_100,30012.26,34.109,1024,224,0.06,1.42,2.54 +tf_mobilenetv3_small_minimal_100,28698.14,35.672,1024,224,0.06,1.41,2.04 +tf_mobilenetv3_small_075,27407.51,37.352,1024,224,0.05,1.3,2.04 +tinynet_d,27236.47,37.585,1024,152,0.05,1.42,2.34 +tf_mobilenetv3_small_100,25103.65,40.781,1024,224,0.06,1.42,2.54 +lcnet_075,24140.95,42.406,1024,224,0.1,1.99,2.36 +mnasnet_small,20706.43,49.443,1024,224,0.07,2.16,2.03 +levit_128s,20595.72,49.709,1024,224,0.31,1.88,7.78 +lcnet_100,19684.75,52.01,1024,224,0.16,2.52,2.95 +mobilenetv2_035,18358.82,55.767,1024,224,0.07,2.86,1.68 +regnetx_002,18244.04,56.117,1024,224,0.2,2.16,2.68 +ghostnet_050,17564.96,58.287,1024,224,0.05,1.77,2.59 +regnety_002,17006.07,60.202,1024,224,0.2,2.17,3.16 +mnasnet_050,15925.32,64.29,1024,224,0.11,3.07,2.22 +vit_tiny_r_s16_p8_224,15068.38,67.946,1024,224,0.44,2.06,6.34 +mobilenetv2_050,14843.74,68.974,1024,224,0.1,3.64,1.97 +tinynet_c,14634.69,69.959,1024,184,0.11,2.87,2.46 +semnasnet_050,14248.78,71.855,1024,224,0.11,3.44,2.08 +levit_128,14164.26,72.284,1024,224,0.41,2.71,9.21 +vit_small_patch32_224,13811.36,74.131,1024,224,1.15,2.5,22.88 +mixer_s32_224,13352.85,76.677,1024,224,1.0,2.28,19.1 +cs3darknet_focus_s,12798.44,79.999,1024,256,0.69,2.7,3.27 +lcnet_150,12783.12,80.094,1024,224,0.34,3.79,4.5 +cs3darknet_s,12395.11,82.602,1024,256,0.72,2.97,3.28 +regnetx_004,12366.39,82.791,1024,224,0.4,3.14,5.16 +mobilenetv3_large_075,12001.32,85.313,1024,224,0.16,4.0,3.99 +levit_192,11882.81,86.163,1024,224,0.66,3.2,10.95 +resnet10t,11615.84,88.145,1024,224,1.1,2.43,5.44 +ese_vovnet19b_slim_dw,11539.4,88.729,1024,224,0.4,5.28,1.9 +gernet_s,11496.77,89.058,1024,224,0.75,2.65,8.17 +mobilenetv3_rw,10873.77,94.16,1024,224,0.23,4.41,5.48 +mobilenetv3_large_100,10705.06,95.645,1024,224,0.23,4.41,5.48 +hardcorenas_a,10554.34,97.012,1024,224,0.23,4.38,5.26 +tf_mobilenetv3_large_075,10511.12,97.41,1024,224,0.16,4.0,3.99 +tf_mobilenetv3_large_minimal_100,10371.16,98.725,1024,224,0.22,4.4,3.92 +mnasnet_075,10345.17,98.972,1024,224,0.23,4.77,3.17 +hardcorenas_b,9695.74,105.601,1024,224,0.26,5.09,5.18 +regnety_004,9655.22,106.046,1024,224,0.41,3.89,4.34 +ghostnet_100,9483.99,107.96,1024,224,0.15,3.55,5.18 +hardcorenas_c,9481.05,107.994,1024,224,0.28,5.01,5.52 +tf_mobilenetv3_large_100,9456.79,108.271,1024,224,0.23,4.41,5.48 +regnetx_006,9408.22,108.83,1024,224,0.61,3.98,6.2 +mobilenetv2_075,9313.88,109.932,1024,224,0.22,5.86,2.64 +tinynet_b,9291.99,110.191,1024,188,0.21,4.44,3.73 +mnasnet_b1,9286.4,110.258,1024,224,0.33,5.46,4.38 +mnasnet_100,9263.52,110.53,1024,224,0.33,5.46,4.38 +gluon_resnet18_v1b,9078.31,112.785,1024,224,1.82,2.48,11.69 +semnasnet_075,9069.42,112.895,1024,224,0.23,5.54,2.91 +resnet18,9045.63,113.192,1024,224,1.82,2.48,11.69 +ssl_resnet18,9045.4,113.196,1024,224,1.82,2.48,11.69 +swsl_resnet18,9040.4,113.258,1024,224,1.82,2.48,11.69 +levit_256,8921.47,114.768,1024,224,1.13,4.23,18.89 +hardcorenas_d,8879.46,115.311,1024,224,0.3,4.93,7.5 +regnety_006,8666.48,118.144,1024,224,0.61,4.33,6.06 +seresnet18,8542.99,119.851,1024,224,1.82,2.49,11.78 +mobilenetv2_100,8507.29,120.356,1024,224,0.31,6.68,3.5 +spnasnet_100,8342.04,122.741,1024,224,0.35,6.03,4.42 +legacy_seresnet18,8310.8,123.202,1024,224,1.82,2.49,11.78 +semnasnet_100,8284.16,123.599,1024,224,0.32,6.23,3.89 +mnasnet_a1,8283.57,123.607,1024,224,0.32,6.23,3.89 +regnetx_008,7852.75,130.39,1024,224,0.81,5.15,7.26 +hardcorenas_f,7809.07,131.117,1024,224,0.35,5.57,8.2 +hardcorenas_e,7730.97,132.444,1024,224,0.35,5.65,8.07 +efficientnet_lite0,7722.75,132.584,1024,224,0.4,6.74,4.65 +levit_256d,7689.03,133.165,1024,224,1.4,4.93,26.21 +xcit_nano_12_p16_224_dist,7674.8,133.413,1024,224,0.56,4.17,3.05 +xcit_nano_12_p16_224,7670.11,133.492,1024,224,0.56,4.17,3.05 +resnet18d,7636.48,134.082,1024,224,2.06,3.29,11.71 +ghostnet_130,7625.58,134.274,1024,224,0.24,4.6,7.36 +tf_efficientnetv2_b0,7614.25,134.473,1024,224,0.73,4.77,7.14 +ese_vovnet19b_slim,7588.4,134.932,1024,224,1.69,3.52,3.17 +deit_tiny_distilled_patch16_224,7449.3,137.451,1024,224,1.27,6.01,5.91 +deit_tiny_patch16_224,7398.73,138.391,1024,224,1.26,5.97,5.72 +vit_tiny_patch16_224,7390.78,138.538,1024,224,1.26,5.97,5.72 +regnety_008,7366.88,138.989,1024,224,0.81,5.25,6.26 +tinynet_a,7358.6,139.145,1024,192,0.35,5.41,6.19 +dla46_c,7311.64,140.038,1024,224,0.58,4.5,1.3 +fbnetc_100,7303.94,140.187,1024,224,0.4,6.51,5.57 +mobilevitv2_050,7248.37,141.262,1024,256,0.48,8.04,1.37 +tf_efficientnet_lite0,6816.26,150.218,1024,224,0.4,6.74,4.65 +pit_ti_distilled_224,6788.49,150.832,1024,224,0.71,6.23,5.1 +pit_ti_224,6762.99,151.401,1024,224,0.7,6.19,4.85 +efficientnet_b0,6687.26,153.115,1024,224,0.4,6.75,5.29 +visformer_tiny,6618.81,154.698,1024,224,1.27,5.72,10.32 +rexnet_100,6608.65,154.937,1024,224,0.41,7.44,4.8 +mnasnet_140,6580.58,155.597,1024,224,0.6,7.71,7.12 +efficientnet_b1_pruned,6513.48,157.201,1024,240,0.4,6.21,6.33 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+resnet50t,2869.49,356.844,1024,224,4.32,11.82,25.57 +gcresnet33ts,2863.36,357.609,1024,256,4.76,11.68,19.88 +gluon_resnet50_v1d,2853.24,358.879,1024,224,4.35,11.92,25.58 +cspresnet50d,2852.98,358.911,1024,256,4.86,12.55,21.64 +resnet50d,2850.55,359.218,1024,224,4.35,11.92,25.58 +vovnet39a,2845.31,359.878,1024,224,7.09,6.73,22.6 +cspresnet50w,2835.31,361.148,1024,256,5.04,12.19,28.12 +vgg11,2827.53,362.143,1024,224,7.61,7.44,132.86 +tf_efficientnet_em,2826.28,362.303,1024,240,3.04,14.34,6.9 +visformer_small,2818.88,363.251,1024,224,4.88,11.43,40.22 +vit_relpos_small_patch16_224,2792.87,366.637,1024,224,4.59,13.05,21.98 +vit_relpos_base_patch32_plus_rpn_256,2784.26,367.771,1024,256,7.68,8.01,119.42 +vit_srelpos_small_patch16_224,2781.72,368.106,1024,224,4.59,12.16,21.97 +resnest26d,2772.97,369.267,1024,224,3.64,9.97,17.07 +cs3darknet_focus_l,2770.5,369.596,1024,288,5.9,10.16,21.15 +efficientnet_b2a,2767.64,369.979,1024,288,1.12,16.2,9.11 +efficientnet_b2,2766.98,370.065,1024,288,1.12,16.2,9.11 +ese_vovnet39b,2760.12,370.986,1024,224,7.09,6.74,24.57 +legacy_seresnet50,2753.49,371.881,1024,224,3.88,10.6,28.09 +densenet121,2749.79,372.378,1024,224,2.87,6.9,7.98 +tv_densenet121,2747.16,372.735,1024,224,2.87,6.9,7.98 +eca_vovnet39b,2736.53,374.185,1024,224,7.09,6.74,22.6 +coatnet_nano_cc_224,2716.19,376.986,1024,224,2.24,15.02,13.76 +convnextv2_femto,2710.95,377.714,1024,288,1.3,7.56,5.23 +resnetv2_50x1_bit_distilled,2704.93,378.554,1024,224,4.23,11.11,25.55 +selecsls84,2697.2,379.64,1024,224,5.9,7.57,50.95 +flexivit_small,2693.55,380.153,1024,240,5.35,14.18,22.06 +twins_svt_small,2691.25,380.48,1024,224,2.94,13.75,24.06 +mixnet_l,2678.25,382.327,1024,224,0.58,10.84,7.33 +seresnet50,2674.61,382.848,1024,224,4.11,11.13,28.09 +xcit_nano_12_p16_384_dist,2668.39,383.74,1024,384,1.64,12.15,3.05 +cs3darknet_l,2649.93,386.412,1024,288,6.16,10.83,21.16 +coatnet_nano_rw_224,2633.36,388.844,1024,224,2.41,15.41,15.14 +coatnext_nano_rw_224,2627.24,389.75,1024,224,2.47,12.8,14.7 +xcit_tiny_24_p16_224_dist,2617.14,391.253,1024,224,2.34,11.82,12.12 +densenet121d,2616.98,391.278,1024,224,3.11,7.7,8.0 +xcit_tiny_24_p16_224,2614.91,391.584,1024,224,2.34,11.82,12.12 +resnet50_gn,2599.07,393.975,1024,224,4.14,11.11,25.56 +vit_relpos_small_patch16_rpn_224,2596.73,394.33,1024,224,4.59,13.05,21.97 +res2net50_48w_2s,2593.21,394.865,1024,224,4.18,11.72,25.29 +mobilevit_s,2587.93,296.749,768,256,2.03,19.94,5.58 +convnext_nano,2579.36,396.983,1024,288,4.06,13.84,15.59 +tf_mixnet_l,2577.4,397.288,1024,224,0.58,10.84,7.33 +resnetaa50d,2573.35,397.912,1024,224,5.39,12.44,25.58 +vgg11_bn,2556.04,400.607,1024,224,7.62,7.44,132.87 +seresnet50t,2550.33,401.504,1024,224,4.32,11.83,28.1 +ecaresnet50d,2544.16,402.478,1024,224,4.35,11.93,25.58 +gcvit_xxtiny,2518.13,406.639,1024,224,2.14,15.36,12.0 +cs3sedarknet_l,2502.51,409.176,1024,288,6.16,10.83,21.91 +resnetrs50,2497.73,409.96,1024,224,4.48,12.14,35.69 +mobilevitv2_125,2489.87,308.438,768,256,2.86,20.1,7.48 +resnetblur50,2484.87,412.08,1024,224,5.16,12.02,25.56 +cspresnext50,2483.24,412.352,1024,256,4.05,15.86,20.57 +gluon_resnet50_v1s,2459.02,416.413,1024,224,5.47,13.52,25.68 +efficientnet_cc_b1_8e,2458.85,416.443,1024,240,0.75,15.44,39.72 +vit_base_resnet26d_224,2458.01,416.584,1024,224,6.97,13.16,101.4 +densenetblur121d,2444.58,418.873,1024,224,3.11,7.9,8.0 +tv_resnext50_32x4d,2431.41,421.143,1024,224,4.26,14.4,25.03 +ssl_resnext50_32x4d,2431.35,421.155,1024,224,4.26,14.4,25.03 +swsl_resnext50_32x4d,2430.87,421.236,1024,224,4.26,14.4,25.03 +resnext50_32x4d,2429.56,421.462,1024,224,4.26,14.4,25.03 +gluon_resnext50_32x4d,2428.35,421.674,1024,224,4.26,14.4,25.03 +dla60x,2414.82,424.035,1024,224,3.54,13.8,17.35 +efficientnet_lite3,2407.43,212.664,512,300,1.65,21.85,8.2 +regnetx_040,2406.98,425.416,1024,224,3.99,12.2,22.12 +semobilevit_s,2404.63,319.371,768,256,2.03,19.95,5.74 +gcresnext50ts,2402.57,426.196,1024,256,3.75,15.46,15.67 +regnety_040s_gn,2385.11,429.317,1024,224,4.03,12.29,20.65 +resnetblur50d,2367.52,432.507,1024,224,5.4,12.82,25.58 +vovnet57a,2360.79,433.737,1024,224,8.95,7.52,36.64 +tf_efficientnet_cc_b1_8e,2357.71,434.307,1024,240,0.75,15.44,39.72 +resmlp_24_distilled_224,2351.85,435.39,1024,224,5.96,10.91,30.02 +resmlp_24_224,2345.81,436.509,1024,224,5.96,10.91,30.02 +res2net50_14w_8s,2341.48,437.317,1024,224,4.21,13.28,25.06 +coatnet_rmlp_nano_rw_224,2340.53,437.494,1024,224,2.62,20.34,15.15 +sehalonet33ts,2339.44,328.271,768,256,3.55,14.7,13.69 +res2net50_26w_4s,2338.49,437.876,1024,224,4.28,12.61,25.7 +convnext_nano_ols,2328.37,439.779,1024,288,4.38,15.5,15.65 +lambda_resnet26rpt_256,2324.88,165.158,384,256,3.16,11.87,10.99 +gmixer_24_224,2324.82,440.451,1024,224,5.28,14.45,24.72 +gcresnet50t,2321.78,441.028,1024,256,5.42,14.67,25.9 +resnext50d_32x4d,2317.05,441.929,1024,224,4.5,15.2,25.05 +resnest50d_1s4x24d,2309.9,443.296,1024,224,4.43,13.57,25.68 +seresnetaa50d,2309.78,443.319,1024,224,5.4,12.46,28.11 +dla60_res2net,2301.91,444.834,1024,224,4.15,12.34,20.85 +vit_base_r26_s32_224,2301.77,444.864,1024,224,6.81,12.36,101.38 +twins_pcpvt_small,2290.09,447.132,1024,224,3.83,18.08,24.11 +regnetz_b16,2286.62,447.81,1024,288,2.39,16.43,9.72 +ese_vovnet57b,2267.23,451.64,1024,224,8.95,7.52,38.61 +gluon_inception_v3,2265.31,452.024,1024,299,5.73,8.97,23.83 +inception_v3,2260.97,452.888,1024,299,5.73,8.97,23.83 +adv_inception_v3,2258.89,453.305,1024,299,5.73,8.97,23.83 +tf_inception_v3,2255.73,453.943,1024,299,5.73,8.97,23.83 +densenet169,2232.91,458.582,1024,224,3.4,7.3,14.15 +tf_efficientnetv2_b3,2223.64,460.493,1024,300,3.04,15.74,14.36 +nf_ecaresnet50,2211.52,463.019,1024,224,4.21,11.13,25.56 +nf_seresnet50,2207.21,463.921,1024,224,4.21,11.13,28.09 +skresnet50,2206.75,464.017,1024,224,4.11,12.5,25.8 +edgenext_small,2206.31,464.109,1024,320,1.97,14.16,5.59 +seresnext50_32x4d,2197.09,466.058,1024,224,4.26,14.42,27.56 +gluon_seresnext50_32x4d,2196.94,466.091,1024,224,4.26,14.42,27.56 +xcit_small_12_p16_224_dist,2195.81,466.33,1024,224,4.82,12.58,26.25 +legacy_seresnext50_32x4d,2193.34,466.856,1024,224,4.26,14.42,27.56 +xcit_small_12_p16_224,2190.16,467.534,1024,224,4.82,12.58,26.25 +repvgg_b1g4,2188.83,467.817,1024,224,8.15,10.64,39.97 +tf_efficientnet_lite3,2188.37,233.953,512,300,1.65,21.85,8.2 +efficientnetv2_rw_t,2170.03,471.87,1024,288,3.19,16.42,13.65 +gmlp_s16_224,2164.56,473.061,1024,224,4.42,15.1,19.42 +dla60_res2next,2126.26,481.583,1024,224,3.49,13.17,17.03 +gc_efficientnetv2_rw_t,2126.09,481.621,1024,288,3.2,16.45,13.68 +skresnet50d,2112.57,484.703,1024,224,4.36,13.31,25.82 +mobilevitv2_150,2105.0,243.219,512,256,4.09,24.11,10.59 +mobilevitv2_150_in22ft1k,2104.51,243.274,512,256,4.09,24.11,10.59 +convnextv2_pico,2092.16,489.434,1024,288,2.27,10.08,9.07 +poolformer_s24,2090.38,489.851,1024,224,3.41,10.68,21.39 +cs3sedarknet_xdw,2090.04,489.929,1024,256,5.97,17.18,21.6 +res2next50,2085.23,491.055,1024,224,4.2,13.71,24.67 +cspdarknet53,2084.51,491.231,1024,256,6.57,16.81,27.64 +fbnetv3_g,2084.48,491.238,1024,288,1.77,21.09,16.62 +crossvit_small_240,2074.04,493.709,1024,240,5.63,18.17,26.86 +deit3_medium_patch16_224_in21ft1k,2064.27,496.046,1024,224,8.0,15.93,38.85 +deit3_medium_patch16_224,2063.34,496.268,1024,224,8.0,15.93,38.85 +xcit_nano_12_p8_224_dist,2049.01,499.742,1024,224,2.16,15.71,3.05 +xcit_nano_12_p8_224,2044.48,500.848,1024,224,2.16,15.71,3.05 +nf_regnet_b3,2035.39,503.085,1024,320,2.05,14.61,18.59 +cs3darknet_focus_x,2017.73,507.488,1024,256,8.03,10.69,35.02 +vit_relpos_medium_patch16_cls_224,2000.38,511.89,1024,224,8.03,18.24,38.76 +lambda_resnet50ts,1991.21,514.246,1024,256,5.07,17.48,21.54 +swin_tiny_patch4_window7_224,1978.72,517.495,1024,224,4.51,17.06,28.29 +sebotnet33ts_256,1959.75,195.932,384,256,3.89,17.46,13.7 +coatnet_0_rw_224,1957.32,523.148,1024,224,4.43,18.73,27.44 +ecaresnet26t,1953.32,524.224,1024,320,5.24,16.44,16.01 +regnetx_080,1942.5,527.144,1024,224,8.02,14.06,39.57 +gcvit_xtiny,1941.57,527.393,1024,224,2.93,20.26,19.98 +resnetv2_101,1925.46,531.806,1024,224,7.83,16.23,44.54 +regnetx_064,1920.06,533.303,1024,224,6.49,16.37,26.21 +mixnet_xl,1918.85,533.64,1024,224,0.93,14.57,11.9 +edgenext_small_rw,1912.9,535.3,1024,320,2.46,14.85,7.83 +vit_relpos_medium_patch16_224,1907.96,536.687,1024,224,7.97,17.02,38.75 +vit_srelpos_medium_patch16_224,1900.57,538.773,1024,224,7.96,16.21,38.74 +resnest50d,1896.74,539.858,1024,224,5.4,14.36,27.48 +crossvit_15_240,1894.86,540.397,1024,240,5.81,19.77,27.53 +vit_base_resnet50d_224,1892.78,540.989,1024,224,8.73,16.92,110.97 +gluon_resnet101_v1b,1879.26,544.883,1024,224,7.83,16.23,44.55 +tv_resnet101,1878.26,545.172,1024,224,7.83,16.23,44.55 +resnet101,1875.25,546.047,1024,224,7.83,16.23,44.55 +dla102,1873.79,546.472,1024,224,7.19,14.18,33.27 +efficientformer_l3,1868.08,548.142,1024,224,3.93,12.01,31.41 +maxvit_rmlp_pico_rw_256,1866.73,411.402,768,256,1.85,24.86,7.52 +resnetv2_101d,1855.94,551.727,1024,224,8.07,17.04,44.56 +pvt_v2_b2,1835.92,557.745,1024,224,4.05,27.53,25.36 +maxvit_pico_rw_256,1829.44,419.787,768,256,1.83,22.3,7.46 +vgg13,1820.36,562.512,1024,224,11.31,12.25,133.05 +lamhalobotnet50ts_256,1818.57,563.067,1024,256,5.02,18.44,22.57 +crossvit_15_dagger_240,1817.96,563.255,1024,240,6.13,20.43,28.21 +gluon_resnet101_v1c,1816.14,563.82,1024,224,8.08,17.04,44.57 +res2net50_26w_6s,1811.81,565.168,1024,224,6.33,15.28,37.05 +gluon_resnet101_v1d,1808.21,566.295,1024,224,8.08,17.04,44.57 +swin_s3_tiny_224,1803.67,567.72,1024,224,4.64,19.13,28.33 +coatnet_rmlp_0_rw_224,1803.63,567.733,1024,224,4.72,24.89,27.45 +vit_relpos_medium_patch16_rpn_224,1770.72,578.284,1024,224,7.97,17.02,38.73 +halonet50ts,1765.73,579.917,1024,256,5.3,19.2,22.73 +repvgg_b1,1760.92,581.5,1024,224,13.16,10.64,57.42 +coatnet_bn_0_rw_224,1753.99,583.799,1024,224,4.67,22.04,27.44 +wide_resnet50_2,1747.87,585.844,1024,224,11.43,14.4,68.88 +efficientnet_b3,1741.21,294.036,512,320,2.01,26.52,12.23 +efficientnet_b3a,1740.84,294.1,512,320,2.01,26.52,12.23 +densenet201,1738.22,589.096,1024,224,4.34,7.85,20.01 +coatnet_0_224,1727.45,296.376,512,224,4.58,24.01,25.04 +darknetaa53,1721.33,594.876,1024,288,10.08,15.68,36.02 +tf_efficientnet_b3,1720.61,297.558,512,300,1.87,23.83,12.23 +cait_xxs24_224,1720.1,595.301,1024,224,2.53,20.29,11.96 +vit_large_patch32_224,1718.53,595.845,1024,224,15.41,13.32,327.9 +mobilevitv2_175,1697.71,301.572,512,256,5.54,28.13,14.25 +mobilevitv2_175_in22ft1k,1697.51,301.606,512,256,5.54,28.13,14.25 +xcit_tiny_12_p16_384_dist,1694.92,604.145,1024,384,3.64,18.26,6.72 +pvt_v2_b2_li,1694.45,604.311,1024,224,3.91,27.6,22.55 +coat_lite_small,1694.41,604.328,1024,224,3.96,22.09,19.84 +resnetaa101d,1692.59,604.976,1024,224,9.12,17.56,44.57 +legacy_seresnet101,1686.93,607.005,1024,224,7.61,15.74,49.33 +tresnet_v2_l,1685.52,607.515,1024,224,8.81,16.34,46.17 +hrnet_w18,1679.12,609.832,1024,224,4.32,16.31,21.3 +vit_medium_patch16_gap_240,1667.0,614.264,1024,240,9.22,18.81,44.4 +vit_tiny_patch16_384,1660.88,616.528,1024,384,4.7,25.39,5.79 +regnetv_040,1659.81,616.926,1024,288,6.6,20.3,20.64 +convnext_tiny_hnf,1659.73,616.951,1024,288,7.39,22.21,28.59 +seresnet101,1655.13,618.666,1024,224,7.84,16.27,49.33 +vit_base_patch32_384,1651.29,620.109,1024,384,13.06,16.5,88.3 +vit_base_patch32_clip_384,1649.72,620.7,1024,384,13.06,16.5,88.3 +regnety_040,1647.66,621.47,1024,288,6.61,20.3,20.65 +regnety_032,1645.25,622.383,1024,288,5.29,18.61,19.44 +gluon_resnet101_v1s,1642.29,623.505,1024,224,9.19,18.64,44.67 +vgg13_bn,1634.19,626.596,1024,224,11.33,12.25,133.05 +resnetaa50,1631.05,627.803,1024,288,8.52,19.24,25.56 +mixer_b16_224_miil,1628.71,628.706,1024,224,12.62,14.53,59.88 +mixer_b16_224,1627.79,629.061,1024,224,12.62,14.53,59.88 +convnext_tiny,1626.95,629.384,1024,288,7.39,22.21,28.59 +nf_resnet101,1620.77,631.785,1024,224,8.01,16.23,44.55 +swinv2_cr_tiny_224,1618.15,632.807,1024,224,4.66,28.45,28.33 +ecaresnet101d,1609.33,636.276,1024,224,8.08,17.07,44.57 +twins_pcpvt_base,1605.41,637.831,1024,224,6.68,25.25,43.83 +dla102x,1601.78,639.274,1024,224,5.89,19.42,26.31 +ese_vovnet39b_evos,1601.47,639.4,1024,224,7.07,6.74,24.58 +darknet53,1597.03,641.177,1024,288,11.78,15.68,41.61 +resnetblur101d,1596.24,641.494,1024,224,9.12,17.94,44.57 +resnet51q,1592.08,643.172,1024,288,8.07,20.94,35.7 +swinv2_cr_tiny_ns_224,1591.39,643.448,1024,224,4.66,28.45,28.33 +mixer_l32_224,1583.03,646.85,1024,224,11.27,19.86,206.94 +resmlp_36_distilled_224,1577.86,648.967,1024,224,8.91,16.33,44.69 +resmlp_36_224,1577.4,649.158,1024,224,8.91,16.33,44.69 +resnetv2_50d_gn,1561.87,655.61,1024,288,7.24,19.7,25.57 +botnet50ts_256,1556.81,246.643,384,256,5.54,22.23,22.74 +nf_resnet50,1548.83,661.132,1024,288,6.88,18.37,25.56 +resnetv2_50d_frn,1547.35,661.764,1024,224,4.33,11.92,25.59 +halo2botnet50ts_256,1546.64,496.545,768,256,5.02,21.78,22.64 +mvitv2_tiny,1534.63,667.247,1024,224,4.7,21.16,24.17 +gluon_resnext101_32x4d,1505.04,680.366,1024,224,8.01,21.23,44.18 +swsl_resnext101_32x4d,1504.46,680.63,1024,224,8.01,21.23,44.18 +cs3darknet_x,1504.38,680.665,1024,288,10.6,14.36,35.05 +ssl_resnext101_32x4d,1503.93,680.869,1024,224,8.01,21.23,44.18 +resnext101_32x4d,1503.63,681.005,1024,224,8.01,21.23,44.18 +resnest50d_4s2x40d,1497.58,683.755,1024,224,4.4,17.94,30.42 +convnextv2_nano,1488.75,515.858,768,288,4.06,13.84,15.62 +skresnext50_32x4d,1478.83,692.427,1024,224,4.5,17.18,27.48 +mobilevitv2_200,1478.44,519.454,768,256,7.22,32.15,18.45 +tresnet_l,1477.44,693.076,1024,224,10.88,11.9,55.99 +mobilevitv2_200_in22ft1k,1477.37,519.83,768,256,7.22,32.15,18.45 +vgg16,1475.59,693.946,1024,224,15.47,13.56,138.36 +regnetz_c16,1475.58,693.953,1024,320,3.92,25.88,13.46 +resnetv2_50d_evob,1468.61,697.244,1024,224,4.33,11.92,25.59 +vit_medium_patch16_gap_256,1467.03,697.996,1024,256,10.59,22.15,38.86 +res2net50_26w_8s,1466.52,698.239,1024,224,8.37,17.95,48.4 +sequencer2d_s,1465.84,698.562,1024,224,4.96,11.31,27.65 +eca_nfnet_l0,1461.61,700.586,1024,288,7.12,17.29,24.14 +nfnet_l0,1460.27,701.228,1024,288,7.13,17.29,35.07 +cs3sedarknet_x,1435.72,713.217,1024,288,10.6,14.37,35.4 +resnet61q,1434.01,714.068,1024,288,9.87,21.52,36.85 +res2net101_26w_4s,1424.71,718.728,1024,224,8.1,18.45,45.21 +repvgg_b2g4,1415.15,723.581,1024,224,12.63,12.9,61.76 +nest_tiny,1413.2,543.434,768,224,5.83,25.48,17.06 +poolformer_s36,1408.65,726.922,1024,224,5.0,15.82,30.86 +maxvit_rmlp_nano_rw_256,1404.06,546.971,768,256,4.47,31.92,15.5 +convit_small,1397.72,732.608,1024,224,5.76,17.87,27.78 +jx_nest_tiny,1387.89,553.347,768,224,5.83,25.48,17.06 +maxvit_nano_rw_256,1378.18,557.246,768,256,4.46,30.28,15.45 +nf_ecaresnet101,1373.28,745.649,1024,224,8.01,16.27,44.55 +nf_seresnet101,1369.04,747.958,1024,224,8.02,16.27,49.33 +gluon_seresnext101_32x4d,1358.35,753.84,1024,224,8.02,21.26,48.96 +legacy_seresnext101_32x4d,1357.27,754.442,1024,224,8.02,21.26,48.96 +efficientnet_b3_gn,1357.0,282.964,384,320,2.14,28.83,11.73 +nfnet_f0,1356.65,754.786,1024,256,12.62,18.05,71.49 +seresnext101_32x4d,1356.0,755.148,1024,224,8.02,21.26,48.96 +resnetv2_152,1353.28,756.668,1024,224,11.55,22.56,60.19 +xception,1353.17,567.542,768,299,8.4,35.83,22.86 +twins_svt_base,1350.54,758.199,1024,224,8.59,26.33,56.07 +crossvit_18_240,1343.82,761.996,1024,240,9.05,26.26,43.27 +ese_vovnet99b_iabn,1343.72,762.049,1024,224,16.49,11.27,63.2 +maxxvit_rmlp_nano_rw_256,1341.45,763.341,1024,256,4.37,26.05,16.78 +regnetx_120,1339.05,764.708,1024,224,12.13,21.37,46.11 +vgg16_bn,1336.79,765.998,1024,224,15.5,13.56,138.37 +dpn92,1330.6,769.562,1024,224,6.54,18.21,37.67 +tv_resnet152,1329.75,770.054,1024,224,11.56,22.56,60.19 +gcvit_tiny,1328.61,770.718,1024,224,4.79,29.82,28.22 +gluon_resnet152_v1b,1328.2,770.954,1024,224,11.56,22.56,60.19 +resnet152,1327.13,771.578,1024,224,11.56,22.56,60.19 +ese_vovnet99b,1316.93,777.554,1024,224,16.51,11.27,63.2 +pvt_v2_b3,1316.31,777.917,1024,224,6.92,37.7,45.24 +xcit_tiny_12_p8_224_dist,1300.55,787.348,1024,224,4.81,23.6,6.71 +xcit_tiny_12_p8_224,1299.96,787.704,1024,224,4.81,23.6,6.71 +crossvit_18_dagger_240,1298.96,788.312,1024,240,9.5,27.03,44.27 +hrnet_w32,1297.82,789.002,1024,224,8.97,22.02,41.23 +gluon_resnet152_v1c,1296.47,789.825,1024,224,11.8,23.36,60.21 +resnetv2_152d,1296.37,789.881,1024,224,11.8,23.36,60.2 +gluon_resnet152_v1d,1293.21,791.811,1024,224,11.8,23.36,60.21 +vit_small_resnet50d_s16_224,1288.35,794.801,1024,224,13.48,24.82,57.53 +cs3edgenet_x,1281.15,799.266,1024,288,14.59,16.36,47.82 +edgenext_base,1272.74,804.548,1024,320,6.01,24.32,18.51 +regnety_120,1268.38,807.318,1024,224,12.14,21.38,51.82 +dla169,1258.34,813.753,1024,224,11.6,20.2,53.39 +hrnet_w30,1252.2,817.74,1024,224,8.15,21.21,37.71 +xception41p,1249.06,409.896,512,299,9.25,39.86,26.91 +maxxvitv2_nano_rw_256,1248.81,819.967,1024,256,6.26,23.05,23.7 +ecaresnet50t,1243.91,823.198,1024,320,8.82,24.13,25.57 +vgg19,1237.03,827.774,1024,224,19.63,14.86,143.67 +swin_small_patch4_window7_224,1228.67,833.406,1024,224,8.77,27.47,49.61 +efficientnet_el_pruned,1220.93,838.69,1024,300,8.0,30.7,10.59 +densenet161,1220.41,839.05,1024,224,7.79,11.06,28.68 +efficientnet_el,1218.76,840.187,1024,300,8.0,30.7,10.59 +deit_base_distilled_patch16_224,1211.4,845.292,1024,224,17.68,24.05,87.34 +vit_base_patch16_224,1209.0,846.969,1024,224,17.58,23.9,86.57 +vit_base_patch16_224_miil,1208.72,847.163,1024,224,17.59,23.91,94.4 +deit_base_patch16_224,1208.56,847.275,1024,224,17.58,23.9,86.57 +vit_base_patch16_clip_224,1205.77,849.236,1024,224,17.58,23.9,86.57 +gluon_resnet152_v1s,1205.41,849.488,1024,224,12.92,24.96,60.32 +coatnet_rmlp_1_rw_224,1201.89,851.979,1024,224,7.85,35.47,41.69 +maxvit_tiny_rw_224,1200.3,853.107,1024,224,5.11,33.11,29.06 +mixnet_xxl,1193.04,643.721,768,224,2.04,23.43,23.96 +tf_efficientnet_el,1192.11,858.967,1024,300,8.0,30.7,10.59 +swinv2_tiny_window8_256,1191.01,859.761,1024,256,5.96,24.57,28.35 +volo_d1_224,1190.57,860.079,1024,224,6.94,24.43,26.63 +repvgg_b2,1183.91,864.916,1024,224,20.45,12.9,89.02 +legacy_seresnet152,1181.09,866.978,1024,224,11.33,22.08,66.82 +xcit_small_24_p16_224_dist,1175.31,871.245,1024,224,9.1,23.64,47.67 +xcit_small_24_p16_224,1174.76,871.656,1024,224,9.1,23.64,47.67 +inception_v4,1168.76,876.127,1024,299,12.28,15.09,42.68 +seresnet152,1166.02,878.19,1024,224,11.57,22.61,66.82 +twins_pcpvt_large,1163.18,880.331,1024,224,9.84,35.82,60.99 +deit3_base_patch16_224,1159.4,883.201,1024,224,17.58,23.9,86.59 +deit3_base_patch16_224_in21ft1k,1159.14,883.404,1024,224,17.58,23.9,86.59 +cait_xxs36_224,1156.4,885.493,1024,224,3.77,30.34,17.3 +vit_base_patch32_clip_448,1154.9,886.645,1024,448,17.93,23.9,88.34 +regnetx_160,1153.07,888.048,1024,224,15.99,25.52,54.28 +dm_nfnet_f0,1152.75,888.293,1024,256,12.62,18.05,71.49 +sequencer2d_m,1147.71,892.201,1024,224,6.55,14.26,38.31 +repvgg_b3g4,1145.87,893.631,1024,224,17.89,15.1,83.83 +mvitv2_small_cls,1144.7,894.542,1024,224,7.04,28.17,34.87 +mvitv2_small,1143.83,895.224,1024,224,7.0,28.08,34.87 +efficientnet_lite4,1139.64,336.935,384,380,4.04,45.66,13.01 +tnt_s_patch16_224,1135.12,902.091,1024,224,5.24,24.37,23.76 +convmixer_1024_20_ks9_p14,1130.85,905.497,1024,224,5.55,5.51,24.38 +vgg19_bn,1127.16,908.464,1024,224,19.66,14.86,143.68 +vit_relpos_base_patch16_clsgap_224,1124.58,910.547,1024,224,17.6,25.12,86.43 +vit_relpos_base_patch16_cls_224,1122.76,912.026,1024,224,17.6,25.12,86.43 +coatnet_rmlp_1_rw2_224,1119.61,914.591,1024,224,8.11,40.13,41.72 +beit_base_patch16_224,1109.32,923.073,1024,224,17.58,23.9,86.53 +xception41,1107.6,462.251,512,299,9.28,39.86,26.97 +tresnet_xl,1106.51,925.423,1024,224,15.17,15.34,78.44 +beitv2_base_patch16_224,1106.05,925.798,1024,224,17.58,23.9,86.53 +coat_tiny,1099.16,931.604,1024,224,4.35,27.2,5.5 +vit_base_patch16_gap_224,1085.51,943.323,1024,224,17.49,25.59,86.57 +maxvit_tiny_tf_224,1081.57,710.062,768,224,5.6,35.78,30.92 +vit_relpos_base_patch16_224,1078.21,949.713,1024,224,17.51,24.97,86.43 +nf_regnet_b4,1075.82,951.823,1024,384,4.7,28.61,30.21 +coatnet_1_rw_224,1074.48,953.005,1024,224,8.04,34.6,41.72 +dla102x2,1070.83,956.252,1024,224,9.34,29.91,41.28 +pit_b_224,1066.8,479.928,512,224,12.42,32.94,73.76 +pit_b_distilled_224,1063.31,481.504,512,224,12.5,33.07,74.79 +tf_efficientnet_lite4,1058.68,362.703,384,380,4.04,45.66,13.01 +efficientnetv2_s,1057.28,968.508,1024,384,8.44,35.77,21.46 +vit_large_r50_s32_224,1034.79,989.556,1024,224,19.58,24.41,328.99 +vit_small_patch16_36x1_224,1032.1,992.142,1024,224,13.71,35.69,64.67 +efficientnet_b3_g8_gn,1031.26,496.465,512,320,3.2,28.83,14.25 +tf_efficientnetv2_s,1029.13,995.002,1024,384,8.44,35.77,21.46 +flexivit_base,1028.55,995.558,1024,240,20.29,28.36,86.59 +vit_base_patch16_rpn_224,1016.66,1007.208,1024,224,17.49,23.75,86.54 +vit_small_r26_s32_384,1011.11,1012.73,1024,384,10.43,29.85,36.47 +vit_small_patch16_18x2_224,1005.34,1018.547,1024,224,13.71,35.69,64.67 +swinv2_cr_small_224,1000.71,1023.259,1024,224,9.07,50.27,49.7 +efficientnetv2_rw_s,995.91,1028.19,1024,384,8.72,38.03,23.94 +wide_resnet101_2,995.32,1028.801,1024,224,22.8,21.23,126.89 +swinv2_cr_small_ns_224,989.25,1035.114,1024,224,9.08,50.27,49.7 +vit_relpos_base_patch16_rpn_224,986.84,1037.641,1024,224,17.51,24.97,86.41 +coatnet_1_224,984.69,519.944,512,224,8.7,39.0,42.23 +resnet200,983.36,1041.314,1024,224,15.07,32.19,64.67 +dpn98,982.09,1042.657,1024,224,11.73,25.2,61.57 +convnext_small,981.97,1042.782,1024,288,14.39,35.65,50.22 +cs3se_edgenet_x,975.89,1049.279,1024,320,18.01,20.21,50.72 +regnety_080,969.67,1056.01,1024,288,13.22,29.69,39.18 +poolformer_m36,966.97,1058.965,1024,224,8.8,22.02,56.17 +resnest101e,963.69,1062.57,1024,256,13.38,28.66,48.28 +regnetz_b16_evos,955.65,803.632,768,288,2.36,16.43,9.74 +twins_svt_large,954.95,1072.291,1024,224,15.15,35.1,99.27 +pvt_v2_b4,952.02,1075.594,1024,224,10.14,53.74,62.56 +gluon_resnext101_64x4d,944.48,1084.183,1024,224,15.52,31.21,83.46 +regnetv_064,944.32,1084.367,1024,288,10.55,27.11,30.58 +regnety_064,944.18,1084.526,1024,288,10.56,27.11,30.58 +maxvit_rmlp_tiny_rw_256,941.64,815.588,768,256,6.77,46.92,29.15 +regnetz_d8,936.16,1093.814,1024,320,6.19,37.08,23.37 +resnetrs101,936.12,1093.858,1024,288,13.56,28.53,63.62 +regnetz_d32,933.58,1096.833,1024,320,9.33,37.08,27.58 +ig_resnext101_32x8d,930.9,1099.997,1024,224,16.48,31.21,88.79 +swsl_resnext101_32x8d,930.28,1100.725,1024,224,16.48,31.21,88.79 +resnext101_32x8d,929.98,1101.084,1024,224,16.48,31.21,88.79 +ssl_resnext101_32x8d,929.0,1102.24,1024,224,16.48,31.21,88.79 +convnextv2_tiny,925.13,553.423,512,288,7.39,22.21,28.64 +convnextv2_small,924.53,1107.57,1024,224,8.71,21.56,50.32 +maxvit_tiny_rw_256,921.72,833.209,768,256,6.74,44.35,29.07 +inception_resnet_v2,917.69,1115.834,1024,299,13.18,25.06,55.84 +ens_adv_inception_resnet_v2,917.66,1115.871,1024,299,13.18,25.06,55.84 +maxxvit_rmlp_tiny_rw_256,914.74,1119.428,1024,256,6.66,39.76,29.64 +xcit_tiny_24_p16_384_dist,912.61,1122.045,1024,384,6.87,34.29,12.12 +cait_s24_224,908.65,1126.929,1024,224,9.35,40.58,46.92 +pvt_v2_b5,904.89,1131.615,1024,224,11.76,50.92,81.96 +nest_small,902.63,850.834,768,224,10.35,40.04,38.35 +repvgg_b3,901.73,1135.583,1024,224,29.16,15.1,123.09 +maxvit_tiny_pm_256,896.67,1141.994,1024,256,6.61,47.9,30.09 +xception65p,896.53,571.079,512,299,13.91,52.48,39.82 +swin_s3_small_224,896.35,856.792,768,224,9.43,37.84,49.74 +jx_nest_small,892.32,860.663,768,224,10.35,40.04,38.35 +efficientnet_b4,890.89,431.018,384,384,4.51,50.04,19.34 +gmlp_b16_224,885.75,1156.072,1024,224,15.78,30.21,73.08 +gluon_seresnext101_64x4d,885.23,1156.747,1024,224,15.53,31.25,88.23 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a/pytorch-image-models/results/benchmark-infer-amp-nchw-pt210-cu121-rtx3090.csv b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt210-cu121-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..9980b79d8ed492c5e1fdc05ea13392449994b750 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt210-cu121-rtx3090.csv @@ -0,0 +1,1294 @@ +model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count +tinynet_e,106,1024.0,50604.03,20.225,0.03,0.69,2.04 +mobilenetv3_small_050,224,1024.0,46069.42,22.217,0.03,0.92,1.59 +lcnet_035,224,1024.0,41190.64,24.85,0.03,1.04,1.64 +lcnet_050,224,1024.0,37663.82,27.178,0.05,1.26,1.88 +mobilenetv3_small_075,224,1024.0,33398.64,30.649,0.05,1.3,2.04 +efficientvit_m0,224,1024.0,32179.13,31.812,0.08,0.91,2.35 +mobilenetv3_small_100,224,1024.0,29653.41,34.522,0.06,1.42,2.54 +tf_mobilenetv3_small_minimal_100,224,1024.0,28352.57,36.106,0.06,1.41,2.04 +tinynet_d,152,1024.0,27612.87,37.074,0.05,1.42,2.34 +tf_mobilenetv3_small_075,224,1024.0,27505.95,37.218,0.05,1.3,2.04 +tf_mobilenetv3_small_100,224,1024.0,24859.95,41.18,0.06,1.42,2.54 +efficientvit_m1,224,1024.0,24836.87,41.219,0.17,1.33,2.98 +lcnet_075,224,1024.0,24184.78,42.33,0.1,1.99,2.36 +efficientvit_m2,224,1024.0,21907.95,46.731,0.2,1.47,4.19 +mnasnet_small,224,1024.0,20764.95,49.303,0.07,2.16,2.03 +levit_128s,224,1024.0,20669.44,49.531,0.31,1.88,7.78 +lcnet_100,224,1024.0,19774.93,51.772,0.16,2.52,2.95 +regnetx_002,224,1024.0,18945.55,54.04,0.2,2.16,2.68 +resnet10t,176,1024.0,18840.28,54.342,0.7,1.51,5.44 +efficientvit_m3,224,1024.0,18627.14,54.963,0.27,1.62,6.9 +mobilenetv2_035,224,1024.0,18464.78,55.447,0.07,2.86,1.68 +ghostnet_050,224,1024.0,17741.46,57.707,0.05,1.77,2.59 +resnet18,160,1024.0,17592.15,58.198,0.93,1.27,11.69 +regnety_002,224,1024.0,17571.32,58.267,0.2,2.17,3.16 +levit_conv_128s,224,1024.0,17529.9,58.404,0.31,1.88,7.78 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+efficientnet_l2,800,32.0,22.53,1420.616,479.12,1707.39,480.31 +tf_efficientnet_l2,800,32.0,22.12,1446.454,479.12,1707.39,480.31 +vit_giant_patch14_dinov2,518,192.0,17.14,11200.639,1553.56,871.89,1136.48 +vit_giant_patch14_reg4_dinov2,518,128.0,17.05,7505.847,1558.09,874.43,1136.48 +swinv2_cr_giant_384,384,32.0,15.01,2131.256,1450.71,1394.86,2598.76 +eva_giant_patch14_560,560,192.0,15.01,12792.976,1618.04,846.56,1014.45 +cait_m48_448,448,128.0,13.76,9299.464,329.4,1708.21,356.46 +samvit_large_patch16,1024,8.0,10.25,780.237,1317.08,1055.58,308.28 +samvit_huge_patch16,1024,6.0,6.31,950.475,2741.59,1727.57,637.03 +eva02_enormous_patch14_clip_224,224,,,,1132.46,497.58,4350.56 +vit_huge_patch16_gap_448,448,,,,544.7,636.83,631.67 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx3090.csv b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..ed619f8fbcd571e198b2e21cbdc7c3cd8a327707 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx3090.csv @@ -0,0 +1,1444 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_vit,160,109337.21,9.356,1024,0.37,0.04,0.48 +test_byobnet,160,82185.02,12.45,1024,0.46,0.03,0.43 +test_efficientnet,160,76411.59,13.392,1024,0.36,0.06,0.55 +tinynet_e,106,53275.73,19.211,1024,2.04,0.03,0.69 +mobilenetv3_small_050,224,47496.52,21.55,1024,1.59,0.03,0.92 +lcnet_035,224,42719.32,23.961,1024,1.64,0.03,1.04 +lcnet_050,224,38393.43,26.662,1024,1.88,0.05,1.26 +mobilenetv3_small_075,224,34935.91,29.301,1024,2.04,0.05,1.3 +efficientvit_m0,224,32556.1,31.443,1024,2.35,0.08,0.91 +mobilenetv3_small_100,224,31410.96,32.59,1024,2.54,0.06,1.42 +tf_mobilenetv3_small_minimal_100,224,29476.16,34.73,1024,2.04,0.06,1.41 +tinynet_d,152,29431.12,34.783,1024,2.34,0.05,1.42 +tf_mobilenetv3_small_075,224,28685.83,35.688,1024,2.04,0.05,1.3 +tf_mobilenetv3_small_100,224,26229.43,39.03,1024,2.54,0.06,1.42 +efficientvit_m1,224,25342.72,40.397,1024,2.98,0.17,1.33 +lcnet_075,224,24815.53,41.255,1024,2.36,0.1,1.99 +efficientvit_m2,224,22234.8,46.044,1024,4.19,0.2,1.47 +mobilenetv4_conv_small,224,21980.64,46.577,1024,3.77,0.19,1.97 +mnasnet_small,224,21439.71,47.752,1024,2.03,0.07,2.16 +levit_128s,224,21017.47,48.711,1024,7.78,0.31,1.88 +lcnet_100,224,20320.08,50.384,1024,2.95,0.16,2.52 +mobilenetv4_conv_small,256,19758.75,51.816,1024,3.77,0.25,2.57 +regnetx_002,224,19130.47,53.516,1024,2.68,0.2,2.16 +efficientvit_m3,224,19121.62,53.542,1024,6.9,0.27,1.62 +mobilenetv2_035,224,19047.74,53.75,1024,1.68,0.07,2.86 +resnet10t,176,19017.0,53.837,1024,5.44,0.7,1.51 +ghostnet_050,224,18326.71,55.865,1024,2.59,0.05,1.77 +levit_conv_128s,224,17825.2,57.436,1024,7.78,0.31,1.88 +regnety_002,224,17806.29,57.495,1024,3.16,0.2,2.17 +efficientvit_m4,224,17783.18,57.573,1024,8.8,0.3,1.7 +resnet18,160,17690.73,57.874,1024,11.69,0.93,1.27 +repghostnet_050,224,17490.98,58.535,1024,2.31,0.05,2.02 +efficientvit_b0,224,16914.4,60.53,1024,3.41,0.1,2.87 +mnasnet_050,224,16594.59,61.697,1024,2.22,0.11,3.07 +vit_tiny_r_s16_p8_224,224,16372.59,62.534,1024,6.34,0.44,2.06 +tinynet_c,184,15537.22,65.896,1024,2.46,0.11,2.87 +mobilenetv2_050,224,15294.16,66.944,1024,1.97,0.1,3.64 +pit_ti_224,224,14941.65,68.524,1024,4.85,0.7,6.19 +pit_ti_distilled_224,224,14919.02,68.627,1024,5.1,0.71,6.23 +semnasnet_050,224,14881.16,68.802,1024,2.08,0.11,3.44 +levit_128,224,14392.72,71.137,1024,9.21,0.41,2.71 +repghostnet_058,224,13974.19,73.268,1024,2.55,0.07,2.59 +vit_small_patch32_224,224,13132.7,77.963,1024,22.88,1.15,2.5 +lcnet_150,224,13019.52,78.641,1024,4.5,0.34,3.79 +cs3darknet_focus_s,256,12823.07,79.845,1024,3.27,0.69,2.7 +regnetx_004,224,12790.25,80.052,1024,5.16,0.4,3.14 +levit_conv_128,224,12771.21,80.17,1024,9.21,0.41,2.71 +mobilenetv3_large_075,224,12600.27,81.258,1024,3.99,0.16,4.0 +cs3darknet_s,256,12496.47,81.932,1024,3.28,0.72,2.97 +regnetx_004_tv,224,12440.42,82.303,1024,5.5,0.42,3.17 +efficientvit_m5,224,12202.57,83.907,1024,12.47,0.53,2.41 +levit_192,224,12163.42,84.177,1024,10.95,0.66,3.2 +resnet10t,224,12120.7,84.474,1024,5.44,1.1,2.43 +gernet_s,224,11872.38,86.241,1024,8.17,0.75,2.65 +ese_vovnet19b_slim_dw,224,11765.52,87.024,1024,1.9,0.4,5.28 +hardcorenas_a,224,11423.94,89.627,1024,5.26,0.23,4.38 +repghostnet_080,224,11343.56,90.261,1024,3.28,0.1,3.22 +mobilenetv3_rw,224,11309.87,90.531,1024,5.48,0.23,4.41 +mobilenetv3_large_100,224,11135.72,91.947,1024,5.48,0.23,4.41 +tf_mobilenetv3_large_075,224,10970.94,93.328,1024,3.99,0.16,4.0 +mixer_s32_224,224,10941.26,93.581,1024,19.1,1.0,2.28 +mnasnet_075,224,10916.2,93.796,1024,3.17,0.23,4.77 +levit_conv_192,224,10869.06,94.202,1024,10.95,0.66,3.2 +mobilenetv1_100,224,10802.87,94.78,1024,4.23,0.58,5.04 +tf_mobilenetv3_large_minimal_100,224,10586.88,96.713,1024,3.92,0.22,4.4 +resnet14t,176,10568.07,96.886,1024,10.08,1.07,3.61 +mobilenetv1_100h,224,10512.64,97.397,1024,5.28,0.63,5.09 +hardcorenas_b,224,10474.56,97.751,1024,5.18,0.26,5.09 +resnet34,160,10402.15,98.431,1024,21.8,1.87,1.91 +hardcorenas_c,224,10270.04,99.697,1024,5.52,0.28,5.01 +nf_regnet_b0,192,10246.95,99.922,1024,8.76,0.37,3.15 +deit_tiny_patch16_224,224,10236.41,100.025,1024,5.72,1.26,5.97 +vit_tiny_patch16_224,224,10231.54,100.072,1024,5.72,1.26,5.97 +regnety_004,224,10230.97,100.079,1024,4.34,0.41,3.89 +deit_tiny_distilled_patch16_224,224,10185.75,100.523,1024,5.91,1.27,6.01 +regnetx_006,224,10150.71,100.87,1024,6.2,0.61,3.98 +tinynet_b,188,9883.52,103.596,1024,3.73,0.21,4.44 +ghostnet_100,224,9881.78,103.616,1024,5.18,0.15,3.55 +tf_mobilenetv3_large_100,224,9800.29,104.477,1024,5.48,0.23,4.41 +mnasnet_100,224,9763.25,104.873,1024,4.38,0.33,5.46 +repghostnet_100,224,9586.17,106.811,1024,4.07,0.15,3.98 +hardcorenas_d,224,9568.31,107.009,1024,7.5,0.3,4.93 +tf_efficientnetv2_b0,192,9480.34,108.003,1024,7.14,0.54,3.51 +mobilenetv2_075,224,9478.95,108.018,1024,2.64,0.22,5.86 +semnasnet_075,224,9435.3,108.519,1024,2.91,0.23,5.54 +regnety_006,224,9299.07,110.103,1024,6.06,0.61,4.33 +resnet18,224,9260.29,110.57,1024,11.69,1.82,2.48 +pit_xs_224,224,9215.59,111.106,1024,10.62,1.4,7.71 +pit_xs_distilled_224,224,9179.35,111.544,1024,11.0,1.41,7.76 +mobilenet_edgetpu_v2_xs,224,9103.13,112.477,1024,4.46,0.7,4.8 +convnext_atto,224,9094.81,112.582,1024,3.7,0.55,3.81 +vit_xsmall_patch16_clip_224,224,9084.06,112.715,1024,8.28,1.79,6.65 +levit_256,224,9041.44,113.246,1024,18.89,1.13,4.23 +vit_medium_patch32_clip_224,224,9037.75,113.292,1024,39.69,2.0,3.34 +mobilenetv1_100,256,8925.01,114.724,1024,4.23,0.76,6.59 +spnasnet_100,224,8850.63,115.688,1024,4.42,0.35,6.03 +seresnet18,224,8755.08,116.951,1024,11.78,1.82,2.49 +mobilenetv1_100h,256,8716.6,117.468,1024,5.28,0.82,6.65 +repghostnet_111,224,8703.6,117.642,1024,4.54,0.18,4.38 +convnext_atto_ols,224,8634.04,118.591,1024,3.7,0.58,4.11 +mobilenetv2_100,224,8629.73,118.65,1024,3.5,0.31,6.68 +semnasnet_100,224,8576.31,119.389,1024,3.89,0.32,6.23 +legacy_seresnet18,224,8496.1,120.516,1024,11.78,1.82,2.49 +hgnetv2_b0,224,8489.19,120.614,1024,6.0,0.33,2.12 +hardcorenas_f,224,8388.5,122.062,1024,8.2,0.35,5.57 +hardcorenas_e,224,8316.4,123.12,1024,8.07,0.35,5.65 +edgenext_xx_small,256,8267.32,123.851,1024,1.33,0.26,3.33 +repvgg_a0,224,8195.29,124.938,1024,9.11,1.52,3.59 +regnetx_008,224,8145.67,125.701,1024,7.26,0.81,5.15 +levit_conv_256,224,8113.72,126.196,1024,18.89,1.13,4.23 +dla46_c,224,8109.77,126.257,1024,1.3,0.58,4.5 +mobilenetv1_125,224,8097.26,126.453,1024,6.27,0.89,6.3 +efficientnet_lite0,224,7979.97,128.311,1024,4.65,0.4,6.74 +convnext_femto,224,7952.03,128.762,1024,5.22,0.79,4.57 +resnet18d,224,7915.36,129.359,1024,11.71,2.06,3.29 +ghostnet_130,224,7893.46,129.718,1024,7.36,0.24,4.6 +mobilevit_xxs,256,7881.37,129.917,1024,1.27,0.42,8.34 +ese_vovnet19b_slim,224,7874.63,130.029,1024,3.17,1.69,3.52 +levit_256d,224,7779.0,131.627,1024,26.21,1.4,4.93 +mobilenetv4_conv_medium,224,7776.66,131.666,1024,9.72,0.84,5.8 +mobilenet_edgetpu_100,224,7770.98,131.763,1024,4.09,1.0,5.75 +xcit_nano_12_p16_224,224,7759.26,131.961,1024,3.05,0.56,4.17 +repghostnet_130,224,7749.8,132.123,1024,5.48,0.25,5.24 +tinynet_a,192,7745.18,132.201,1024,6.19,0.35,5.41 +regnety_008,224,7721.37,132.605,1024,6.26,0.81,5.25 +tf_efficientnetv2_b0,224,7710.08,132.803,1024,7.14,0.73,4.77 +fbnetc_100,224,7646.21,133.909,1024,5.57,0.4,6.51 +convnext_femto_ols,224,7577.53,135.127,1024,5.23,0.82,4.87 +mobilenetv4_hybrid_medium_075,224,7514.13,136.267,1024,7.31,0.66,5.65 +mobilevitv2_050,256,7395.66,138.45,1024,1.37,0.48,8.04 +tf_efficientnetv2_b1,192,7389.78,138.56,1024,8.14,0.76,4.59 +regnety_008_tv,224,7307.18,140.123,1024,6.43,0.84,5.42 +tf_efficientnet_lite0,224,7042.0,145.403,1024,4.65,0.4,6.74 +efficientnet_b0,224,6924.93,147.857,1024,5.29,0.4,6.75 +mobilenetv4_conv_medium,256,6920.92,147.948,1024,9.72,1.1,7.58 +dla46x_c,224,6863.64,149.181,1024,1.07,0.54,5.66 +mnasnet_140,224,6835.02,149.805,1024,7.12,0.6,7.71 +resnet14t,224,6822.56,150.08,1024,10.08,1.69,5.8 +repghostnet_150,224,6798.46,150.612,1024,6.58,0.32,6.0 +rexnet_100,224,6759.37,151.483,1024,4.8,0.41,7.44 +rexnetr_100,224,6733.45,152.067,1024,4.88,0.43,7.72 +efficientnet_b1_pruned,240,6731.65,152.107,1024,6.33,0.4,6.21 +mobilenetv1_125,256,6707.02,152.666,1024,6.27,1.16,8.23 +visformer_tiny,224,6688.07,153.098,1024,10.32,1.27,5.72 +levit_conv_256d,224,6662.02,153.697,1024,26.21,1.4,4.93 +pvt_v2_b0,224,6589.46,155.389,1024,3.67,0.57,7.99 +efficientvit_b1,224,6579.31,155.629,1024,9.1,0.53,7.25 +fbnetv3_b,224,6563.03,156.015,1024,8.6,0.42,6.97 +repvit_m1,224,6541.47,156.523,1024,5.49,0.83,7.45 +edgenext_xx_small,288,6532.74,156.738,1024,1.33,0.33,4.21 +mobilenet_edgetpu_v2_s,224,6516.09,157.14,1024,5.99,1.21,6.6 +mobilenetv2_110d,224,6504.09,157.429,1024,4.52,0.45,8.71 +vit_betwixt_patch32_clip_224,224,6503.93,157.433,1024,61.41,3.09,4.17 +regnetz_005,224,6484.34,157.909,1024,7.12,0.52,5.86 +ese_vovnet19b_dw,224,6451.87,158.704,1024,6.54,1.34,8.25 +dla60x_c,224,6417.77,159.546,1024,1.32,0.59,6.01 +hgnetv2_b1,224,6310.48,162.26,1024,6.34,0.49,2.73 +repvit_m0_9,224,6253.66,163.733,1024,5.49,0.83,7.45 +crossvit_tiny_240,240,6253.35,163.742,1024,7.01,1.57,9.08 +cs3darknet_focus_m,256,6210.25,164.879,1024,9.3,1.98,4.89 +tf_efficientnet_b0,224,6210.04,164.879,1024,5.29,0.4,6.75 +convnext_pico,224,6200.96,165.126,1024,9.05,1.37,6.1 +nf_regnet_b0,256,6157.62,166.288,1024,8.76,0.64,5.58 +semnasnet_140,224,6069.35,168.703,1024,6.11,0.6,8.87 +crossvit_9_dagger_240,240,6067.95,168.745,1024,8.78,1.99,9.97 +resnet50,160,6033.87,169.699,1024,25.56,2.1,5.67 +repvgg_a1,224,5998.64,170.696,1024,14.09,2.64,4.74 +resnetblur18,224,5978.13,171.282,1024,11.69,2.34,3.39 +cs3darknet_m,256,5943.51,172.279,1024,9.31,2.08,5.28 +mobilenetv2_140,224,5943.08,172.292,1024,6.11,0.6,9.57 +convnext_pico_ols,224,5910.37,173.245,1024,9.06,1.43,6.5 +mobilenetv4_hybrid_medium,224,5890.59,173.827,1024,11.07,0.98,6.84 +efficientnet_b0,256,5798.8,176.577,1024,5.29,0.52,8.81 +tf_efficientnetv2_b2,208,5794.22,176.717,1024,10.1,1.06,6.0 +hrnet_w18_small,224,5781.49,177.103,1024,13.19,1.61,5.72 +crossvit_9_240,240,5771.83,177.403,1024,8.55,1.85,9.52 +skresnet18,224,5765.67,177.593,1024,11.96,1.82,3.24 +resnet50d,160,5707.52,179.403,1024,25.58,2.22,6.08 +resnet18,288,5648.23,181.286,1024,11.69,3.01,4.11 +vit_tiny_r_s16_p8_384,384,5625.7,182.012,1024,6.36,1.34,6.49 +efficientnet_b0_gn,224,5621.97,182.132,1024,5.29,0.42,6.75 +efficientnet_lite1,240,5579.58,183.516,1024,5.42,0.62,10.14 +ghostnetv2_100,224,5564.84,184.002,1024,6.16,0.18,4.55 +fbnetv3_d,224,5563.09,184.06,1024,10.31,0.52,8.5 +convnext_atto,288,5527.85,185.234,1024,3.7,0.91,6.3 +fbnetv3_b,256,5497.24,186.265,1024,8.6,0.55,9.1 +selecsls42,224,5467.25,187.287,1024,30.35,2.94,4.62 +efficientnet_blur_b0,224,5455.02,187.706,1024,5.29,0.43,8.72 +resnet34,224,5454.89,187.712,1024,21.8,3.67,3.74 +efficientvit_b1,256,5439.39,188.246,1024,9.1,0.69,9.46 +tiny_vit_5m_224,224,5432.07,188.5,1024,12.08,1.28,11.25 +selecsls42b,224,5418.51,188.972,1024,32.46,2.98,4.62 +levit_384,224,5395.45,189.779,1024,39.13,2.36,6.26 +tf_efficientnetv2_b1,240,5369.96,190.68,1024,8.14,1.21,7.34 +seresnet18,288,5366.44,190.805,1024,11.78,3.01,4.11 +repvit_m1_0,224,5361.91,190.966,1024,7.3,1.13,8.69 +convnextv2_atto,224,5357.95,191.108,1024,3.71,0.55,3.81 +mixnet_s,224,5315.69,192.627,1024,4.13,0.25,6.25 +repghostnet_200,224,5315.68,192.627,1024,9.8,0.54,7.96 +seresnet50,160,5298.11,193.266,1024,28.09,2.1,5.69 +edgenext_x_small,256,5274.46,194.132,1024,2.34,0.54,5.93 +rexnetr_130,224,5269.16,194.329,1024,7.61,0.68,9.81 +convnext_atto_ols,288,5250.22,195.03,1024,3.7,0.96,6.8 +repvit_m2,224,5242.24,195.319,1024,8.8,1.36,9.43 +gernet_m,224,5225.82,195.94,1024,21.14,3.02,5.24 +hgnetv2_b0,288,5205.77,196.695,1024,6.0,0.54,3.51 +seresnet34,224,5135.96,199.368,1024,21.96,3.67,3.74 +mobilenetv4_hybrid_medium,256,5134.61,199.421,1024,11.07,1.29,9.01 +resnet26,224,5121.5,199.932,1024,16.0,2.36,7.35 +vit_base_patch32_224,224,5098.65,200.828,1024,88.22,4.41,5.01 +mobilenetv3_large_150d,224,5094.2,201.003,1024,14.62,, +vit_base_patch32_clip_224,224,5090.11,201.165,1024,88.22,4.41,5.01 +ecaresnet50t,160,5084.11,201.402,1024,25.57,2.21,6.04 +mobilenetv4_conv_blur_medium,224,5057.66,202.456,1024,9.72,1.22,8.58 +mobilenet_edgetpu_v2_m,224,5045.14,202.956,1024,8.46,1.85,8.15 +tf_efficientnet_lite1,240,5038.81,203.213,1024,5.42,0.62,10.14 +resnet50,176,5019.75,203.984,1024,25.56,2.62,6.92 +repvit_m1_1,224,5014.8,204.185,1024,8.8,1.36,9.43 +legacy_seresnet34,224,4973.36,205.887,1024,21.96,3.67,3.74 +resnet34d,224,4971.07,205.982,1024,21.82,3.91,4.54 +tf_mixnet_s,224,4958.21,206.516,1024,4.13,0.25,6.25 +resnetrs50,160,4955.95,206.604,1024,35.69,2.29,6.2 +xcit_tiny_12_p16_224,224,4922.03,208.034,1024,6.72,1.24,6.29 +eva02_tiny_patch14_224,224,4913.38,208.399,1024,5.5,1.7,9.14 +mobilevitv2_075,256,4906.85,208.678,1024,2.87,1.05,12.06 +pit_s_224,224,4895.76,209.15,1024,23.46,2.88,11.56 +pit_s_distilled_224,224,4881.41,209.765,1024,24.04,2.9,11.64 +efficientnet_es_pruned,224,4880.76,209.792,1024,5.44,1.81,8.73 +efficientnet_es,224,4880.24,209.815,1024,5.44,1.81,8.73 +mobilenetv2_120d,224,4862.72,210.571,1024,5.83,0.69,11.97 +resnet18d,288,4851.45,211.06,1024,11.71,3.41,5.43 +resnext50_32x4d,160,4849.69,211.137,1024,25.03,2.17,7.35 +efficientnet_b1,224,4846.84,211.261,1024,7.79,0.59,9.36 +levit_conv_384,224,4839.13,211.598,1024,39.13,2.36,6.26 +rexnet_130,224,4831.59,211.926,1024,7.56,0.68,9.71 +cs3darknet_focus_m,288,4831.24,211.944,1024,9.3,2.51,6.19 +convnext_femto,288,4824.54,212.238,1024,5.22,1.3,7.56 +dla34,224,4805.9,213.06,1024,15.74,3.07,5.02 +efficientnet_b0_g16_evos,224,4800.56,213.298,1024,8.11,1.01,7.42 +resnet26d,224,4679.82,218.802,1024,16.01,2.6,8.15 +tf_efficientnet_es,224,4673.69,219.089,1024,5.44,1.81,8.73 +resmlp_12_224,224,4653.37,220.046,1024,15.35,3.01,5.5 +cs3darknet_m,288,4646.1,220.39,1024,9.31,2.63,6.69 +fbnetv3_d,256,4637.02,220.821,1024,10.31,0.68,11.1 +mobilenetv4_conv_aa_medium,256,4630.5,221.132,1024,9.72,1.58,10.3 +selecsls60,224,4624.54,221.418,1024,30.67,3.59,5.52 +rexnetr_150,224,4617.37,221.761,1024,9.78,0.89,11.13 +convnext_femto_ols,288,4613.88,221.929,1024,5.23,1.35,8.06 +nf_regnet_b1,256,4603.23,222.442,1024,10.22,0.82,7.27 +vit_base_patch32_clip_quickgelu_224,224,4601.51,222.526,1024,87.85,4.41,5.01 +selecsls60b,224,4600.54,222.572,1024,32.77,3.63,5.52 +convnextv2_femto,224,4593.84,222.896,1024,5.23,0.79,4.57 +regnetx_016,224,4589.73,223.096,1024,9.19,1.62,7.93 +deit_small_patch16_224,224,4586.09,223.273,1024,22.05,4.61,11.95 +vit_small_patch16_224,224,4584.86,223.334,1024,22.05,4.61,11.95 +gmixer_12_224,224,4571.76,223.974,1024,12.7,2.67,7.26 +gmlp_ti16_224,224,4565.24,224.293,1024,5.87,1.34,7.55 +repvgg_b0,224,4553.06,224.894,1024,15.82,3.41,6.15 +deit_small_distilled_patch16_224,224,4543.3,225.376,1024,22.44,4.63,12.02 +mixer_s16_224,224,4530.96,225.99,1024,18.53,3.79,5.97 +vit_small_patch32_384,384,4513.63,226.858,1024,22.92,3.45,8.25 +efficientnet_cc_b0_4e,224,4507.68,227.158,1024,13.31,0.41,9.42 +efficientnet_cc_b0_8e,224,4491.22,227.99,1024,24.01,0.42,9.42 +mixer_b32_224,224,4485.82,228.265,1024,60.29,3.24,6.29 +tiny_vit_11m_224,224,4481.54,228.483,1024,20.35,2.04,13.49 +mobilenetv4_conv_medium,320,4477.38,228.695,1024,9.72,1.71,11.84 +nf_resnet26,224,4466.75,229.24,1024,16.0,2.41,7.35 +mobilenet_edgetpu_v2_l,224,4462.15,229.476,1024,10.92,2.55,9.05 +efficientnet_b2_pruned,260,4421.73,231.573,1024,8.31,0.73,9.13 +efficientformer_l1,224,4415.57,231.896,1024,12.29,1.3,5.53 +resnetaa34d,224,4392.45,233.118,1024,21.82,4.43,5.07 +darknet17,256,4386.8,233.416,1024,14.3,3.26,7.18 +ghostnetv2_130,224,4382.43,233.65,1024,8.96,0.28,5.9 +rexnet_150,224,4375.57,234.017,1024,9.73,0.9,11.21 +convnext_nano,224,4355.17,235.113,1024,15.59,2.46,8.37 +ecaresnet50d_pruned,224,4352.57,235.254,1024,19.94,2.53,6.43 +efficientnet_b1,240,4341.0,235.879,1024,7.79,0.71,10.88 +nf_regnet_b2,240,4323.87,236.815,1024,14.31,0.97,7.23 +poolformer_s12,224,4258.87,240.428,1024,11.92,1.82,5.53 +regnety_016,224,4256.18,240.571,1024,11.2,1.63,8.04 +mobilenetv4_conv_blur_medium,256,4255.16,180.477,768,9.72,1.59,11.2 +vit_wee_patch16_reg1_gap_256,256,4234.72,241.8,1024,13.42,3.83,13.9 +mobilenet_edgetpu_v2_m,256,4230.57,242.038,1024,8.46,2.42,10.65 +vit_pwee_patch16_reg1_gap_256,256,4211.58,243.129,1024,15.25,4.37,15.87 +deit3_small_patch16_224,224,4202.98,243.625,1024,22.06,4.61,11.95 +hgnetv2_b2,224,4202.72,243.64,1024,11.22,1.15,4.12 +edgenext_x_small,288,4195.02,244.088,1024,2.34,0.68,7.5 +tf_efficientnet_cc_b0_4e,224,4189.01,244.439,1024,13.31,0.41,9.42 +efficientnet_lite2,260,4184.61,244.696,1024,6.09,0.89,12.9 +tf_efficientnet_cc_b0_8e,224,4158.98,246.204,1024,24.01,0.42,9.42 +regnetz_005,288,4139.21,247.38,1024,7.12,0.86,9.68 +hgnetv2_b4,224,4136.11,247.566,1024,19.8,2.75,6.7 +efficientvit_b1,288,4112.03,249.015,1024,9.1,0.87,11.96 +resnest14d,224,4103.54,249.531,1024,10.61,2.76,7.33 +resnext26ts,256,4101.49,249.654,1024,10.3,2.43,10.52 +efficientnet_b0_g8_gn,224,4095.29,250.033,1024,6.56,0.66,6.75 +efficientnet_b1,256,4062.7,252.039,1024,7.79,0.77,12.22 +tf_efficientnet_b1,240,4009.73,255.369,1024,7.79,0.71,10.88 +edgenext_small,256,3998.75,256.069,1024,5.59,1.26,9.07 +eca_resnext26ts,256,3985.65,256.911,1024,10.3,2.43,10.52 +darknet21,256,3985.31,256.933,1024,20.86,3.93,7.47 +seresnext26ts,256,3983.57,257.043,1024,10.39,2.43,10.52 +regnetz_b16,224,3982.17,257.134,1024,9.72,1.45,9.95 +resnext50_32x4d,176,3977.56,257.434,1024,25.03,2.71,8.97 +convnext_nano_ols,224,3963.35,258.357,1024,15.65,2.65,9.38 +vit_base_patch32_clip_256,256,3950.74,259.181,1024,87.86,5.76,6.65 +flexivit_small,240,3949.57,259.258,1024,22.06,5.35,14.18 +gcresnext26ts,256,3942.19,259.744,1024,10.48,2.43,10.53 +mobileone_s1,224,3939.35,259.93,1024,4.83,0.86,9.67 +hgnetv2_b1,288,3863.09,265.063,1024,6.34,0.82,4.51 +sedarknet21,256,3855.83,265.558,1024,20.95,3.93,7.47 +tf_efficientnetv2_b2,260,3852.83,265.768,1024,10.1,1.72,9.84 +nf_ecaresnet26,224,3841.08,266.581,1024,16.0,2.41,7.36 +efficientnet_b2,256,3835.68,266.957,1024,9.11,0.89,12.81 +nf_seresnet26,224,3835.46,266.972,1024,17.4,2.41,7.36 +mobilevit_xs,256,3825.9,200.727,768,2.32,1.05,16.33 +dpn48b,224,3821.05,267.978,1024,9.13,1.69,8.92 +mobilenetv4_conv_large,256,3819.0,268.122,1024,32.59,2.86,12.14 +vit_relpos_small_patch16_224,224,3815.41,268.375,1024,21.98,4.59,13.05 +tf_efficientnet_lite2,260,3814.92,268.409,1024,6.09,0.89,12.9 +pvt_v2_b1,224,3812.97,268.546,1024,14.01,2.12,15.39 +resnet26t,256,3801.26,269.374,1024,16.01,3.35,10.52 +vit_srelpos_small_patch16_224,224,3792.64,269.986,1024,21.97,4.59,12.16 +legacy_seresnext26_32x4d,224,3774.18,271.304,1024,16.79,2.49,9.39 +ese_vovnet19b_dw,288,3768.37,271.725,1024,6.54,2.22,13.63 +convnext_pico,288,3762.1,272.178,1024,9.05,2.27,10.08 +gernet_l,256,3741.79,273.656,1024,31.08,4.57,8.0 +mobilenetv4_hybrid_large_075,256,3731.28,274.426,1024,22.75,2.06,11.64 +resnet101,160,3660.13,279.761,1024,44.55,4.0,8.28 +edgenext_small_rw,256,3647.4,280.737,1024,7.83,1.58,9.51 +resnetblur18,288,3644.87,280.933,1024,11.69,3.87,5.6 +tf_efficientnetv2_b3,240,3640.22,281.291,1024,14.36,1.93,9.95 +cs3darknet_focus_l,256,3628.94,282.166,1024,21.15,4.66,8.03 +efficientnetv2_rw_t,224,3628.38,282.209,1024,13.65,1.93,9.94 +repvit_m3,224,3609.67,283.663,1024,10.68,1.89,13.94 +mixnet_m,224,3606.44,283.926,1024,5.01,0.36,8.19 +coatnet_pico_rw_224,224,3590.64,285.176,1024,10.85,2.05,14.62 +ghostnetv2_160,224,3589.83,285.24,1024,12.39,0.42,7.23 +gc_efficientnetv2_rw_t,224,3589.63,285.255,1024,13.68,1.94,9.97 +convnext_pico_ols,288,3587.72,285.408,1024,9.06,2.37,10.74 +ecaresnext50t_32x4d,224,3560.67,287.576,1024,15.41,2.7,10.09 +ecaresnext26t_32x4d,224,3560.44,287.594,1024,15.41,2.7,10.09 +seresnext26t_32x4d,224,3558.26,287.767,1024,16.81,2.7,10.09 +eca_botnext26ts_256,256,3542.14,289.08,1024,10.59,2.46,11.6 +efficientnet_b3_pruned,300,3528.65,290.185,1024,9.86,1.04,11.86 +seresnext26d_32x4d,224,3528.55,290.194,1024,16.81,2.73,10.19 +nf_regnet_b1,288,3527.3,290.296,1024,10.22,1.02,9.2 +coat_lite_tiny,224,3515.07,291.307,1024,5.72,1.6,11.65 +convnextv2_pico,224,3514.61,291.344,1024,9.07,1.37,6.1 +cs3darknet_l,256,3497.24,292.792,1024,21.16,4.86,8.55 +repvgg_a2,224,3492.05,293.227,1024,28.21,5.7,6.26 +tf_mixnet_m,224,3485.45,293.782,1024,5.01,0.36,8.19 +vit_relpos_small_patch16_rpn_224,224,3481.46,294.119,1024,21.97,4.59,13.05 +hgnet_tiny,224,3480.86,294.17,1024,14.74,4.54,6.36 +eca_halonext26ts,256,3471.15,294.993,1024,10.76,2.44,11.46 +mobilevitv2_100,256,3470.14,221.307,768,4.9,1.84,16.08 +ecaresnet101d_pruned,224,3466.94,295.35,1024,24.88,3.48,7.69 +ecaresnet26t,256,3417.04,299.664,1024,16.01,3.35,10.53 +hgnetv2_b3,224,3369.26,303.913,1024,16.29,1.78,5.07 +resnetv2_50,224,3355.79,305.133,1024,25.55,4.11,11.11 +botnet26t_256,256,3355.2,305.187,1024,12.49,3.32,11.98 +nf_regnet_b2,272,3353.24,305.366,1024,14.31,1.22,9.27 +bat_resnext26ts,256,3346.65,305.963,1024,10.73,2.53,12.51 +coatnext_nano_rw_224,224,3342.84,306.315,1024,14.7,2.47,12.8 +ecaresnetlight,224,3334.95,307.041,1024,30.16,4.11,8.42 +resnet34,288,3329.62,307.532,1024,21.8,6.07,6.18 +rexnetr_200,224,3328.94,230.694,768,16.52,1.59,15.11 +skresnet34,224,3313.88,308.994,1024,22.28,3.67,5.13 +fastvit_t8,256,3313.63,309.016,1024,4.03,0.7,8.63 +halonet26t,256,3312.1,309.159,1024,12.48,3.19,11.69 +vit_small_r26_s32_224,224,3304.01,309.916,1024,36.43,3.56,9.85 +cs3sedarknet_l,256,3303.09,310.003,1024,21.91,4.86,8.56 +coatnet_nano_cc_224,224,3289.43,311.29,1024,13.76,2.24,15.02 +coat_lite_mini,224,3284.89,311.72,1024,11.01,2.0,12.25 +lambda_resnet26t,256,3270.07,313.133,1024,10.96,3.02,11.87 +mobilenetv4_hybrid_medium,320,3262.62,313.848,1024,11.07,2.05,14.36 +convnextv2_atto,288,3253.42,314.736,1024,3.71,0.91,6.3 +vit_small_resnet26d_224,224,3246.31,315.424,1024,63.61,5.07,11.12 +resnet32ts,256,3243.82,315.666,1024,17.96,4.63,11.58 +vit_tiny_patch16_384,384,3237.39,316.294,1024,5.79,4.7,25.39 +convit_tiny,224,3231.06,316.913,1024,5.71,1.26,7.94 +resnet50,224,3219.06,318.095,1024,25.56,4.11,11.11 +coatnet_nano_rw_224,224,3215.75,318.422,1024,15.14,2.41,15.41 +rexnet_200,224,3200.93,239.92,768,16.37,1.56,14.91 +resnet33ts,256,3195.24,320.467,1024,19.68,4.76,11.66 +resnetv2_50t,224,3185.32,321.463,1024,25.57,4.32,11.82 +mobileone_s2,224,3179.36,322.067,1024,7.88,1.34,11.55 +sam2_hiera_tiny,224,3178.9,322.114,1024,26.85,4.91,17.12 +resnetv2_50d,224,3167.44,323.278,1024,25.57,4.35,11.92 +cspresnet50,256,3155.9,324.462,1024,21.62,4.54,11.5 +seresnet34,288,3147.55,325.319,1024,21.96,6.07,6.18 +efficientvit_b2,224,3143.3,325.761,1024,24.33,1.6,14.62 +resnext26ts,288,3127.83,327.374,1024,10.3,3.07,13.31 +fbnetv3_g,240,3119.5,328.234,1024,16.62,1.28,14.87 +hrnet_w18_small_v2,224,3113.69,328.86,1024,15.6,2.62,9.65 +efficientnet_b1,288,3113.4,328.891,1024,7.79,0.97,15.46 +resnet26,288,3110.68,329.178,1024,16.0,3.9,12.15 +tresnet_m,224,3098.68,330.452,1024,31.39,5.75,7.31 +resnet101,176,3098.41,330.482,1024,44.55,4.92,10.08 +seresnet33ts,256,3096.38,330.698,1024,19.78,4.76,11.66 +eca_resnet33ts,256,3095.76,330.765,1024,19.68,4.76,11.66 +convnext_tiny,224,3090.39,331.339,1024,28.59,4.47,13.44 +dpn68b,224,3087.27,331.673,1024,12.61,2.35,10.47 +dpn68,224,3074.12,333.092,1024,12.61,2.35,10.47 +gcresnet33ts,256,3071.03,333.428,1024,19.88,4.76,11.68 +resnet50t,224,3051.2,335.595,1024,25.57,4.32,11.82 +resnet50c,224,3048.9,335.849,1024,25.58,4.35,11.92 +seresnext26ts,288,3040.9,336.731,1024,10.39,3.07,13.32 +eca_resnext26ts,288,3040.18,336.812,1024,10.3,3.07,13.32 +tf_efficientnet_b2,260,3038.71,336.975,1024,9.11,1.02,13.83 +resnet34d,288,3032.3,337.687,1024,21.82,6.47,7.51 +regnetx_032,224,3027.88,338.176,1024,15.3,3.2,11.37 +resnet50d,224,3025.46,338.448,1024,25.58,4.35,11.92 +dla60,224,3019.09,339.163,1024,22.04,4.26,10.16 +gcresnext26ts,288,3012.63,339.891,1024,10.48,3.07,13.33 +efficientnet_em,240,3000.85,341.226,1024,6.9,3.04,14.34 +vit_medium_patch16_clip_224,224,2993.15,342.104,1024,38.59,8.0,15.93 +levit_512,224,2990.53,342.404,1024,95.17,5.64,10.22 +resnest26d,224,2990.32,342.428,1024,17.07,3.64,9.97 +vit_base_patch32_plus_256,256,2981.28,343.466,1024,119.48,7.79,7.76 +crossvit_small_240,240,2972.85,344.44,1024,26.86,5.63,18.17 +repvit_m1_5,224,2972.23,344.512,1024,14.64,2.31,15.7 +mobileone_s0,224,2964.67,345.389,1024,5.29,1.09,15.48 +cspresnet50d,256,2953.53,346.693,1024,21.64,4.86,12.55 +efficientnet_b2,288,2950.49,347.05,1024,9.11,1.12,16.2 +haloregnetz_b,224,2936.32,348.725,1024,11.68,1.97,11.94 +mobilevit_s,256,2931.67,261.956,768,5.58,2.03,19.94 +cspresnet50w,256,2926.16,349.933,1024,28.12,5.04,12.19 +legacy_seresnet50,224,2915.2,351.251,1024,28.09,3.88,10.6 +tf_efficientnet_em,240,2908.61,352.048,1024,6.9,3.04,14.34 +vgg11,224,2887.76,354.59,1024,132.86,7.61,7.44 +resnetv2_50x1_bit,224,2880.89,355.434,1024,25.55,4.23,11.11 +vit_little_patch16_reg1_gap_256,256,2872.75,356.442,1024,22.52,6.27,18.06 +hiera_tiny_224,224,2872.44,356.48,1024,27.91,4.91,17.13 +resnetaa50,224,2865.28,357.372,1024,25.56,5.15,11.64 +regnetv_040,224,2864.51,357.468,1024,20.64,4.0,12.29 +efficientnet_cc_b1_8e,240,2860.03,358.028,1024,39.72,0.75,15.44 +selecsls84,224,2852.61,358.96,1024,50.95,5.9,7.57 +regnety_032,224,2852.43,358.981,1024,19.44,3.2,11.26 +vit_little_patch16_reg4_gap_256,256,2849.96,359.293,1024,22.52,6.35,18.33 +regnety_040,224,2844.62,359.967,1024,20.65,4.0,12.29 +vovnet39a,224,2844.48,359.985,1024,22.6,7.09,6.73 +coatnet_rmlp_nano_rw_224,224,2836.87,360.951,1024,15.15,2.62,20.34 +seresnet50,224,2831.5,361.636,1024,28.09,4.11,11.13 +resnet26d,288,2828.9,361.967,1024,16.01,4.29,13.48 +wide_resnet50_2,176,2817.33,363.453,1024,68.88,7.29,8.97 +vit_relpos_base_patch32_plus_rpn_256,256,2816.85,363.51,1024,119.42,7.68,8.01 +mixnet_l,224,2808.18,364.638,1024,7.33,0.58,10.84 +cs3darknet_focus_l,288,2803.64,365.228,1024,21.15,5.9,10.16 +levit_512d,224,2803.3,365.271,1024,92.5,5.85,11.3 +convnextv2_femto,288,2792.8,366.646,1024,5.23,1.3,7.56 +deit3_medium_patch16_224,224,2781.17,368.18,1024,38.85,8.0,15.93 +crossvit_15_240,240,2780.78,368.231,1024,27.53,5.81,19.77 +res2net50_48w_2s,224,2776.63,368.782,1024,25.29,4.18,11.72 +resnet50_gn,224,2765.5,370.266,1024,25.56,4.14,11.11 +convnext_tiny_hnf,224,2765.22,370.304,1024,28.59,4.47,13.44 +densenet121,224,2764.41,370.412,1024,7.98,2.87,6.9 +levit_conv_512,224,2753.82,371.836,1024,95.17,5.64,10.22 +resnetv2_50d_gn,224,2752.94,371.954,1024,25.57,4.38,11.92 +visformer_small,224,2752.56,372.007,1024,40.22,4.88,11.43 +ese_vovnet39b,224,2750.44,372.293,1024,24.57,7.09,6.74 +mobilevitv2_125,256,2748.02,279.464,768,7.48,2.86,20.1 +vit_relpos_medium_patch16_cls_224,224,2744.95,373.038,1024,38.76,8.03,18.24 +eca_vovnet39b,224,2742.08,373.43,1024,22.6,7.09,6.74 +tiny_vit_21m_224,224,2740.23,373.681,1024,33.22,4.29,20.08 +twins_svt_small,224,2734.38,374.479,1024,24.06,2.94,13.75 +gcvit_xxtiny,224,2725.34,375.722,1024,12.0,2.14,15.36 +twins_pcpvt_small,224,2723.83,375.93,1024,24.11,3.83,18.08 +resnet50_clip_gap,224,2722.78,376.075,1024,23.53,5.39,12.44 +resnetaa50d,224,2721.94,376.192,1024,25.58,5.39,12.44 +tf_mixnet_l,224,2713.4,377.376,1024,7.33,0.58,10.84 +crossvit_15_dagger_240,240,2708.78,378.02,1024,28.21,6.13,20.43 +ecaresnet50t,224,2707.73,378.166,1024,25.57,4.32,11.83 +seresnet50t,224,2705.39,378.493,1024,28.1,4.32,11.83 +cs3darknet_l,288,2703.4,378.772,1024,21.16,6.16,10.83 +tf_efficientnet_cc_b1_8e,240,2701.66,379.016,1024,39.72,0.75,15.44 +davit_tiny,224,2698.07,284.638,768,28.36,4.54,18.89 +xcit_nano_12_p16_384,384,2693.55,380.156,1024,3.05,1.64,12.15 +resnetaa34d,288,2691.25,380.482,1024,21.82,7.33,8.38 +ecaresnet50d,224,2687.82,380.967,1024,25.58,4.35,11.93 +ecaresnet50d_pruned,288,2675.98,382.653,1024,19.94,4.19,10.61 +vit_base_resnet26d_224,224,2655.81,385.559,1024,101.4,6.97,13.16 +convnext_nano,288,2652.03,386.11,1024,15.59,4.06,13.84 +resnetrs50,224,2651.0,386.252,1024,35.69,4.48,12.14 +nf_regnet_b3,288,2644.82,387.161,1024,18.59,1.67,11.84 +xcit_tiny_24_p16_224,224,2635.92,388.469,1024,12.12,2.34,11.82 +gcresnext50ts,256,2630.41,389.282,1024,15.67,3.75,15.46 +efficientvit_b2,256,2610.75,392.214,1024,24.33,2.09,19.03 +resnetblur50,224,2609.83,392.352,1024,25.56,5.16,12.02 +vgg11_bn,224,2600.05,393.829,1024,132.87,7.62,7.44 +resnet50s,224,2592.0,395.051,1024,25.68,5.47,13.52 +mobileone_s3,224,2581.04,396.729,1024,10.17,1.94,13.85 +resnext50_32x4d,224,2577.31,397.304,1024,25.03,4.26,14.4 +resnet152,160,2576.63,397.407,1024,60.19,5.9,11.51 +hgnetv2_b2,288,2574.93,397.67,1024,11.22,1.89,6.8 +inception_next_tiny,224,2572.75,398.008,1024,28.06,4.19,11.98 +eca_nfnet_l0,224,2572.54,398.04,1024,24.14,4.35,10.47 +poolformerv2_s12,224,2568.37,398.686,1024,11.89,1.83,5.53 +edgenext_small,320,2567.9,398.756,1024,5.59,1.97,14.16 +nfnet_l0,224,2566.01,399.053,1024,35.07,4.36,10.47 +cs3sedarknet_l,288,2552.9,401.1,1024,21.91,6.16,10.83 +cspresnext50,256,2550.51,401.475,1024,20.57,4.05,15.86 +hgnetv2_b4,288,2544.67,402.4,1024,19.8,4.54,11.08 +vit_relpos_medium_patch16_224,224,2533.85,404.118,1024,38.75,7.97,17.02 +resnet50_clip,224,2532.71,404.3,1024,38.32,6.14,12.98 +levit_conv_512d,224,2532.59,404.319,1024,92.5,5.85,11.3 +efficientnet_lite3,300,2522.45,202.967,512,8.2,1.65,21.85 +convnextv2_nano,224,2515.35,407.09,1024,15.62,2.46,8.37 +res2net50_26w_4s,224,2515.04,407.14,1024,25.7,4.28,12.61 +vit_srelpos_medium_patch16_224,224,2513.47,407.394,1024,38.74,7.96,16.21 +gcresnet50t,256,2508.19,408.251,1024,25.9,5.42,14.67 +dla60x,224,2506.41,408.541,1024,17.35,3.54,13.8 +coatnet_0_rw_224,224,2498.58,409.821,1024,27.44,4.43,18.73 +resnetblur50d,224,2484.69,412.114,1024,25.58,5.4,12.82 +resnest50d_1s4x24d,224,2468.01,414.899,1024,25.68,4.43,13.57 +regnetx_040,224,2467.33,415.012,1024,22.12,3.99,12.2 +densenetblur121d,224,2464.18,415.544,1024,8.0,3.11,7.9 +maxvit_pico_rw_256,256,2458.1,312.426,768,7.46,1.83,22.3 +resnext50d_32x4d,224,2457.94,416.598,1024,25.05,4.5,15.2 +res2net50_14w_8s,224,2455.12,417.077,1024,25.06,4.21,13.28 +maxvit_rmlp_pico_rw_256,256,2451.7,313.241,768,7.52,1.85,24.86 +vit_base_r26_s32_224,224,2446.42,418.56,1024,101.38,6.81,12.36 +regnetz_c16,256,2443.63,419.039,1024,13.46,2.51,16.57 +seresnetaa50d,224,2442.34,419.253,1024,28.11,5.4,12.46 +dla60_res2net,224,2435.63,420.413,1024,20.85,4.15,12.34 +mobilenetv4_conv_large,320,2431.25,421.172,1024,32.59,4.47,18.97 +regnety_040_sgn,224,2430.55,421.294,1024,20.65,4.03,12.29 +resnet32ts,288,2428.67,421.62,1024,17.96,5.86,14.65 +regnetz_b16,288,2414.87,424.028,1024,9.72,2.39,16.43 +convnext_nano_ols,288,2407.94,425.249,1024,15.65,4.38,15.5 +res2net50d,224,2403.99,425.948,1024,25.72,4.52,13.41 +res2next50,224,2396.84,427.213,1024,24.67,4.2,13.71 +resnet33ts,288,2391.23,428.221,1024,19.68,6.02,14.75 +resnet26t,320,2387.91,428.817,1024,16.01,5.24,16.44 +focalnet_tiny_srf,224,2383.46,429.616,1024,28.43,4.42,16.32 +lambda_resnet26rpt_256,256,2377.83,322.974,768,10.99,3.16,11.87 +resmlp_24_224,224,2376.96,430.792,1024,30.02,5.96,10.91 +efficientnetv2_rw_t,288,2366.54,432.688,1024,13.65,3.19,16.42 +sehalonet33ts,256,2360.24,433.843,1024,13.69,3.55,14.7 +vovnet57a,224,2356.05,434.611,1024,36.64,8.95,7.52 +inception_v3,299,2349.22,435.874,1024,23.83,5.73,8.97 +edgenext_base,256,2342.04,437.215,1024,18.51,3.85,15.58 +gmixer_24_224,224,2339.36,437.716,1024,24.72,5.28,14.45 +tf_efficientnetv2_b3,300,2333.45,438.824,1024,14.36,3.04,15.74 +dla60_res2next,224,2330.49,439.381,1024,17.03,3.49,13.17 +hiera_small_224,224,2327.86,439.879,1024,35.01,6.42,20.75 +seresnext50_32x4d,224,2326.69,440.099,1024,27.56,4.26,14.42 +nf_ecaresnet50,224,2326.39,440.157,1024,25.56,4.21,11.13 +nf_seresnet50,224,2322.81,440.835,1024,28.09,4.21,11.13 +seresnet33ts,288,2321.27,441.126,1024,19.78,6.02,14.76 +eca_resnet33ts,288,2320.92,441.194,1024,19.68,6.02,14.76 +skresnet50,224,2319.56,441.453,1024,25.8,4.11,12.5 +legacy_seresnext50_32x4d,224,2319.29,441.503,1024,27.56,4.26,14.42 +gc_efficientnetv2_rw_t,288,2314.22,442.471,1024,13.68,3.2,16.45 +vit_relpos_medium_patch16_rpn_224,224,2310.7,443.143,1024,38.73,7.97,17.02 +hgnetv2_b5,224,2308.27,443.612,1024,39.57,6.56,11.19 +nfnet_f0,192,2304.96,444.248,1024,71.49,7.21,10.16 +gcresnet33ts,288,2301.68,444.881,1024,19.88,6.02,14.78 +resnet51q,256,2298.43,445.511,1024,35.7,6.38,16.55 +tf_efficientnet_lite3,300,2290.61,223.511,512,8.2,1.65,21.85 +fbnetv3_g,288,2283.27,448.453,1024,16.62,1.77,21.09 +ese_vovnet57b,224,2280.74,448.967,1024,38.61,8.95,7.52 +vit_medium_patch16_gap_240,240,2270.51,450.989,1024,44.4,9.22,18.81 +hgnet_small,224,2266.61,451.766,1024,24.36,8.53,8.79 +fastvit_t12,256,2264.32,452.222,1024,7.55,1.42,12.42 +pvt_v2_b2,224,2256.22,453.841,1024,25.36,4.05,27.53 +edgenext_small_rw,320,2251.75,454.745,1024,7.83,2.46,14.85 +rdnet_tiny,224,2248.51,455.402,1024,23.86,5.06,15.98 +densenet169,224,2245.68,455.974,1024,14.15,3.4,7.3 +cs3darknet_focus_x,256,2244.78,456.159,1024,35.02,8.03,10.69 +coatnet_rmlp_0_rw_224,224,2240.35,457.061,1024,27.45,4.72,24.89 +darknetaa53,256,2230.29,459.123,1024,36.02,7.97,12.39 +focalnet_tiny_lrf,224,2229.47,459.291,1024,28.65,4.49,17.76 +repvgg_b1g4,224,2228.31,459.531,1024,39.97,8.15,10.64 +efficientvit_l1,224,2227.63,459.67,1024,52.65,5.27,15.85 +skresnet50d,224,2225.51,460.109,1024,25.82,4.36,13.31 +xcit_small_12_p16_224,224,2223.72,460.479,1024,26.25,4.82,12.58 +nf_resnet50,256,2213.33,462.642,1024,25.56,5.46,14.52 +nextvit_small,224,2207.92,463.775,1024,31.76,5.81,18.44 +mobilenetv4_hybrid_medium,384,2197.46,465.981,1024,11.07,3.01,21.18 +poolformer_s24,224,2197.0,466.076,1024,21.39,3.41,10.68 +coatnet_bn_0_rw_224,224,2195.15,466.473,1024,27.44,4.67,22.04 +resnet152,176,2193.45,466.834,1024,60.19,7.22,13.99 +resnet50_mlp,256,2191.94,467.155,1024,26.65,7.05,16.25 +ecaresnet50t,256,2191.4,467.27,1024,25.57,5.64,15.45 +nf_regnet_b3,320,2189.28,467.722,1024,18.59,2.05,14.61 +efficientnet_b3,288,2184.31,234.387,512,12.23,1.63,21.49 +seresnext26t_32x4d,288,2172.28,471.381,1024,16.81,4.46,16.68 +fastvit_s12,256,2167.43,472.438,1024,9.47,1.82,13.67 +resnetrs101,192,2164.92,472.987,1024,63.62,6.04,12.7 +cs3darknet_x,256,2164.39,473.101,1024,35.05,8.38,11.35 +fastvit_sa12,256,2158.11,474.477,1024,11.58,1.96,14.03 +eva02_small_patch14_224,224,2155.47,475.059,1024,21.62,6.14,18.28 +cs3sedarknet_xdw,256,2153.33,475.532,1024,21.6,5.97,17.18 +seresnext26d_32x4d,288,2151.74,475.88,1024,16.81,4.51,16.85 +ecaresnet26t,320,2151.68,475.897,1024,16.01,5.24,16.44 +rexnetr_300,224,2147.85,476.744,1024,34.81,3.39,22.16 +eva02_tiny_patch14_336,336,2147.15,476.899,1024,5.76,4.68,27.16 +convnextv2_pico,288,2134.41,479.746,1024,9.07,2.27,10.08 +ecaresnet101d_pruned,288,2128.71,481.03,1024,24.88,5.75,12.71 +gcvit_xtiny,224,2125.89,481.67,1024,19.98,2.93,20.26 +gmlp_s16_224,224,2125.3,481.803,1024,19.42,4.42,15.1 +lambda_resnet50ts,256,2124.77,481.923,1024,21.54,5.07,17.48 +mobilevitv2_150,256,2105.23,243.194,512,10.59,4.09,24.11 +coatnet_0_224,224,2091.24,244.821,512,25.04,4.58,24.01 +xcit_nano_12_p8_224,224,2072.91,493.98,1024,3.05,2.16,15.71 +darknet53,256,2072.88,493.984,1024,41.61,9.31,12.39 +cs3sedarknet_x,256,2064.75,495.935,1024,35.4,8.38,11.35 +hgnet_tiny,288,2057.8,497.609,1024,14.74,7.51,10.51 +hieradet_small,256,2057.47,373.263,768,34.72,8.51,27.76 +vit_medium_patch16_reg1_gap_256,256,2056.16,498.005,1024,38.88,10.63,22.26 +hgnetv2_b3,288,2054.95,498.299,1024,16.29,2.94,8.38 +rexnetr_200,288,2048.59,249.918,512,16.52,2.62,24.96 +vit_medium_patch16_reg4_gap_256,256,2044.7,500.797,1024,38.88,10.76,22.6 +resnet61q,256,2040.64,501.793,1024,36.85,7.8,17.01 +vit_base_resnet50d_224,224,2033.13,503.646,1024,110.97,8.73,16.92 +resnest50d,224,2025.99,505.42,1024,27.48,5.4,14.36 +regnetx_080,224,2024.58,505.775,1024,39.57,8.02,14.06 +rexnet_300,224,2023.97,505.926,1024,34.71,3.44,22.4 +mixnet_xl,224,2021.99,506.421,1024,11.9,0.93,14.57 +resnetv2_50,288,2021.6,506.519,1024,25.55,6.79,18.37 +vit_medium_patch16_gap_256,256,2021.46,506.555,1024,38.86,10.59,22.15 +pvt_v2_b2_li,224,2015.57,508.034,1024,22.55,3.91,27.6 +resnetv2_101,224,2011.15,509.149,1024,44.54,7.83,16.23 +ecaresnetlight,288,2010.28,509.37,1024,30.16,6.79,13.91 +sebotnet33ts_256,256,2002.67,255.649,512,13.7,3.89,17.46 +swin_tiny_patch4_window7_224,224,1994.25,513.466,1024,28.29,4.51,17.06 +cspdarknet53,256,1989.31,514.74,1024,27.64,6.57,16.81 +maxvit_nano_rw_256,256,1987.74,386.357,768,15.45,4.46,30.28 +maxvit_rmlp_nano_rw_256,256,1983.0,387.281,768,15.5,4.47,31.92 +maxxvit_rmlp_nano_rw_256,256,1975.69,388.707,768,16.78,4.37,26.05 +dm_nfnet_f0,192,1969.57,519.898,1024,71.49,7.21,10.16 +gcresnext50ts,288,1969.42,519.94,1024,15.67,4.75,19.57 +nest_tiny,224,1965.87,520.878,1024,17.06,5.83,25.48 +dla102,224,1956.25,523.44,1024,33.27,7.19,14.18 +resnet101,224,1956.19,523.457,1024,44.55,7.83,16.23 +efficientvit_b2,288,1950.05,525.102,1024,24.33,2.64,24.03 +nest_tiny_jx,224,1941.61,527.385,1024,17.06,5.83,25.48 +efficientformer_l3,224,1940.81,527.604,1024,31.41,3.93,12.01 +resnet50,288,1939.79,527.881,1024,25.56,6.8,18.37 +resnetv2_101d,224,1936.61,528.747,1024,44.56,8.07,17.04 +crossvit_18_240,240,1935.28,529.112,1024,43.27,9.05,26.26 +lamhalobotnet50ts_256,256,1924.13,532.179,1024,22.57,5.02,18.44 +convnext_tiny,288,1920.23,533.259,1024,28.59,7.39,22.21 +res2net50_26w_6s,224,1912.13,535.518,1024,37.05,6.33,15.28 +resnet101c,224,1894.68,540.45,1024,44.57,8.08,17.04 +mobileone_s4,224,1894.21,540.586,1024,14.95,3.04,17.74 +crossvit_18_dagger_240,240,1889.12,542.039,1024,44.27,9.5,27.03 +resnet101d,224,1886.88,542.683,1024,44.57,8.08,17.04 +coat_lite_small,224,1882.69,543.892,1024,19.84,3.96,22.09 +gcresnet50t,288,1876.91,545.567,1024,25.9,6.86,18.57 +twins_pcpvt_base,224,1875.57,545.957,1024,43.83,6.68,25.25 +vgg13,224,1872.83,546.756,1024,133.05,11.31,12.25 +convnext_small,224,1866.18,548.704,1024,50.22,8.71,21.56 +dpn68b,288,1861.61,550.049,1024,12.61,3.89,17.3 +regnetx_064,224,1854.58,552.136,1024,26.21,6.49,16.37 +mobilevitv2_175,256,1853.4,276.238,512,14.25,5.54,28.13 +halonet50ts,256,1851.66,553.006,1024,22.73,5.3,19.2 +efficientnet_b3,320,1851.55,276.514,512,12.23,2.01,26.52 +resnet50t,288,1843.63,555.416,1024,25.57,7.14,19.53 +efficientnetv2_s,288,1830.25,559.474,1024,21.46,4.75,20.13 +resnet50d,288,1828.73,559.94,1024,25.58,7.19,19.7 +wide_resnet50_2,224,1825.62,560.893,1024,68.88,11.43,14.4 +swin_s3_tiny_224,224,1817.27,563.471,1024,28.33,4.64,19.13 +tf_efficientnet_b3,300,1797.7,284.798,512,12.23,1.87,23.83 +hrnet_w18_ssld,224,1790.44,571.914,1024,21.3,4.32,16.31 +tresnet_v2_l,224,1789.04,572.364,1024,46.17,8.85,16.34 +hrnet_w18,224,1781.62,574.732,1024,21.3,4.32,16.31 +repvgg_b1,224,1778.73,575.68,1024,57.42,13.16,10.64 +maxxvitv2_nano_rw_256,256,1768.07,434.362,768,23.7,6.26,23.05 +cs3edgenet_x,256,1767.31,579.4,1024,47.82,11.53,12.92 +resnetaa101d,224,1761.72,581.24,1024,44.57,9.12,17.56 +resnet101_clip_gap,224,1761.35,581.36,1024,42.52,9.11,17.56 +efficientvit_l2,224,1761.03,581.466,1024,63.71,6.97,19.58 +vit_large_patch32_224,224,1759.2,582.072,1024,305.51,15.39,13.3 +legacy_seresnet101,224,1752.85,584.182,1024,49.33,7.61,15.74 +vit_base_patch32_clip_384,384,1750.87,584.842,1024,88.3,13.06,16.5 +densenet201,224,1750.03,585.121,1024,20.01,4.34,7.85 +vit_base_patch32_384,384,1749.95,585.149,1024,88.3,13.06,16.5 +efficientnetv2_rw_s,288,1749.21,585.397,1024,23.94,4.91,21.41 +pit_b_distilled_224,224,1743.25,587.399,1024,74.79,12.5,33.07 +darknetaa53,288,1734.96,590.204,1024,36.02,10.08,15.68 +efficientnet_b3_gn,288,1734.87,295.112,512,11.73,1.74,23.35 +resnetv2_101x1_bit,224,1730.79,591.627,1024,44.54,8.04,16.23 +resnetaa50,288,1729.69,592.003,1024,25.56,8.52,19.24 +seresnet101,224,1724.67,593.727,1024,49.33,7.84,16.27 +regnety_032,288,1721.01,594.988,1024,19.44,5.29,18.61 +seresnet50,288,1715.52,596.892,1024,28.09,6.8,18.39 +regnetv_040,288,1714.23,597.341,1024,20.64,6.6,20.3 +pit_b_224,224,1713.92,597.451,1024,73.76,12.42,32.94 +xcit_tiny_12_p16_384,384,1713.1,597.736,1024,6.72,3.64,18.26 +resnet101s,224,1707.16,599.813,1024,44.67,9.19,18.64 +regnety_040,288,1706.68,599.986,1024,20.65,6.61,20.3 +maxvit_tiny_rw_224,224,1699.84,451.796,768,29.06,5.11,33.11 +regnetv_064,224,1694.43,604.321,1024,30.58,6.39,16.41 +cait_xxs24_224,224,1694.07,604.451,1024,11.96,2.53,20.29 +regnety_064,224,1688.52,606.436,1024,30.58,6.39,16.41 +densenet121,288,1688.36,606.494,1024,7.98,4.74,11.41 +resnet50_gn,288,1685.84,607.404,1024,25.56,6.85,18.37 +resnet51q,288,1684.11,608.026,1024,35.7,8.07,20.94 +resnet101_clip,224,1683.83,608.126,1024,56.26,9.81,18.08 +nf_resnet101,224,1683.1,608.389,1024,44.55,8.01,16.23 +convnext_tiny_hnf,288,1681.41,608.997,1024,28.59,7.39,22.21 +ese_vovnet39b,288,1678.63,457.506,768,24.57,11.71,11.13 +repvit_m2_3,224,1677.4,610.445,1024,23.69,4.57,26.21 +resnetv2_50d_gn,288,1676.39,610.823,1024,25.57,7.24,19.7 +ecaresnet101d,224,1674.43,611.54,1024,44.57,8.08,17.07 +cs3darknet_x,288,1672.69,612.175,1024,35.05,10.6,14.36 +vitamin_small_224,224,1669.96,613.177,1024,22.03,5.92,26.38 +convnextv2_tiny,224,1666.61,614.408,1024,28.64,4.47,13.44 +cs3se_edgenet_x,256,1659.92,616.886,1024,50.72,11.53,12.94 +resnetblur101d,224,1659.81,616.926,1024,44.57,9.12,17.94 +dla102x,224,1657.53,617.775,1024,26.31,5.89,19.42 +regnetz_d32,256,1655.18,618.654,1024,27.58,5.98,23.74 +nf_resnet50,288,1650.24,620.505,1024,25.56,6.88,18.37 +efficientvit_b3,224,1649.16,620.91,1024,48.65,3.99,26.9 +mobilenetv4_conv_large,384,1643.43,623.074,1024,32.59,6.43,27.31 +resnetaa50d,288,1643.41,623.082,1024,25.58,8.92,20.57 +regnetz_d8,256,1642.29,623.507,1024,23.37,3.97,23.74 +hiera_small_abswin_256,256,1642.12,623.574,1024,34.36,8.29,26.38 +ecaresnet50t,288,1641.73,623.721,1024,25.57,7.14,19.55 +seresnet50t,288,1641.04,623.984,1024,28.1,7.14,19.55 +regnetz_b16_evos,224,1637.18,625.455,1024,9.74,1.43,9.95 +nextvit_base,224,1636.37,625.762,1024,44.82,8.29,23.71 +davit_small,224,1634.36,469.897,768,49.75,8.8,30.49 +mixer_b16_224,224,1632.43,627.274,1024,59.88,12.62,14.53 +ecaresnet50d,288,1630.52,628.009,1024,25.58,7.19,19.72 +swinv2_cr_tiny_224,224,1629.93,628.235,1024,28.33,4.66,28.45 +mobilenetv4_hybrid_medium,448,1629.05,471.43,768,11.07,4.2,29.64 +regnety_080,224,1624.53,630.326,1024,39.18,8.0,17.97 +nf_regnet_b4,320,1623.35,630.784,1024,30.21,3.29,19.88 +regnetz_040,256,1621.31,631.576,1024,27.12,4.06,24.19 +volo_d1_224,224,1620.03,632.078,1024,26.63,6.94,24.43 +ese_vovnet39b_evos,224,1614.15,634.378,1024,24.58,7.07,6.74 +darknet53,288,1612.47,635.036,1024,41.61,11.78,15.68 +regnetz_040_h,256,1612.19,635.152,1024,28.94,4.12,24.29 +resnetv2_50d_frn,224,1608.23,636.713,1024,25.59,4.33,11.92 +tf_efficientnetv2_s,300,1604.9,638.035,1024,21.46,5.35,22.73 +swinv2_cr_tiny_ns_224,224,1602.17,639.123,1024,28.33,4.66,28.45 +botnet50ts_256,256,1595.84,320.823,512,22.74,5.54,22.23 +resmlp_36_224,224,1595.2,641.917,1024,44.69,8.91,16.33 +cs3sedarknet_x,288,1594.16,642.334,1024,35.4,10.6,14.37 +pvt_v2_b3,224,1594.09,642.356,1024,45.24,6.92,37.7 +wide_resnet101_2,176,1589.86,644.071,1024,126.89,14.31,13.18 +hiera_base_224,224,1588.68,644.549,1024,51.52,9.4,30.42 +mvitv2_tiny,224,1580.5,647.884,1024,24.17,4.7,21.16 +sequencer2d_s,224,1577.41,649.155,1024,27.65,4.96,11.31 +resnetblur50,288,1577.39,649.165,1024,25.56,8.52,19.87 +resnet101d,256,1576.21,649.647,1024,44.57,10.55,22.25 +mobilevitv2_200,256,1575.87,324.89,512,18.45,7.22,32.15 +resnest50d_4s2x40d,224,1573.08,650.942,1024,30.42,4.4,17.94 +vit_base_patch16_224_miil,224,1571.45,651.616,1024,94.4,17.59,23.91 +vit_base_patch16_224,224,1570.66,651.945,1024,86.57,17.58,23.9 +resnext50_32x4d,288,1565.78,653.979,1024,25.03,7.04,23.81 +deit_base_patch16_224,224,1564.98,654.312,1024,86.57,17.58,23.9 +vit_base_patch16_clip_224,224,1564.55,654.489,1024,86.57,17.58,23.9 +deit_base_distilled_patch16_224,224,1562.81,655.219,1024,87.34,17.68,24.05 +resnext101_32x4d,224,1562.81,655.22,1024,44.18,8.01,21.23 +halo2botnet50ts_256,256,1560.7,656.107,1024,22.64,5.02,21.78 +skresnext50_32x4d,224,1546.56,662.103,1024,27.48,4.5,17.18 +caformer_s18,224,1545.75,662.45,1024,26.34,4.13,19.39 +vit_base_mci_224,224,1545.28,662.65,1024,86.35,17.73,24.65 +eca_nfnet_l0,288,1542.25,663.956,1024,24.14,7.12,17.29 +tresnet_l,224,1541.09,664.452,1024,55.99,10.9,11.9 +nfnet_l0,288,1540.89,664.54,1024,35.07,7.13,17.29 +regnetz_c16,320,1536.02,666.649,1024,13.46,3.92,25.88 +vit_medium_patch16_rope_reg1_gap_256,256,1527.11,670.537,1024,38.74,10.63,22.26 +rdnet_small,224,1526.64,670.743,1024,50.44,8.74,22.55 +convnextv2_nano,288,1526.13,503.224,768,15.62,4.06,13.84 +beit_base_patch16_224,224,1524.53,671.669,1024,86.53,17.58,23.9 +coatnet_rmlp_1_rw_224,224,1520.15,673.605,1024,41.69,7.85,35.47 +mixer_l32_224,224,1519.29,673.986,1024,206.94,11.27,19.86 +res2net50_26w_8s,224,1519.14,674.046,1024,48.4,8.37,17.95 +vit_small_resnet50d_s16_224,224,1515.04,675.876,1024,57.53,13.48,24.82 +regnety_080_tv,224,1514.62,676.066,1024,39.38,8.51,19.73 +res2net101_26w_4s,224,1512.48,677.014,1024,45.21,8.1,18.45 +beitv2_base_patch16_224,224,1510.67,677.835,1024,86.53,17.58,23.9 +resnet61q,288,1508.28,678.909,1024,36.85,9.87,21.52 +resnetblur50d,288,1502.22,681.649,1024,25.58,8.92,21.19 +densenetblur121d,288,1501.15,682.13,1024,8.0,5.14,13.06 +resnext101_32x8d,176,1496.15,684.414,1024,88.79,10.33,19.37 +edgenext_base,320,1496.01,684.475,1024,18.51,6.01,24.32 +resnext50d_32x4d,288,1492.98,685.867,1024,25.05,7.44,25.13 +repvgg_b2g4,224,1487.15,688.554,1024,61.76,12.63,12.9 +deit3_base_patch16_224,224,1485.67,689.239,1024,86.59,17.58,23.9 +fastvit_mci0,256,1480.55,691.623,1024,11.41,2.42,18.29 +poolformer_s36,224,1480.43,691.678,1024,30.86,5.0,15.82 +regnety_040_sgn,288,1480.21,691.781,1024,20.65,6.67,20.3 +seresnetaa50d,288,1478.73,692.475,1024,28.11,8.92,20.59 +res2net101d,224,1471.66,695.8,1024,45.23,8.35,19.25 +resnetv2_50d_evos,224,1466.16,698.411,1024,25.59,4.33,11.92 +vit_relpos_base_patch16_clsgap_224,224,1464.16,699.363,1024,86.43,17.6,25.12 +vit_relpos_base_patch16_cls_224,224,1462.1,700.351,1024,86.43,17.6,25.12 +vit_small_patch16_36x1_224,224,1460.83,700.959,1024,64.67,13.71,35.69 +vit_small_patch16_384,384,1459.09,701.796,1024,22.2,15.52,50.78 +efficientnet_b3_gn,320,1457.71,263.416,384,11.73,2.14,28.83 +inception_next_small,224,1454.59,703.968,1024,49.37,8.36,19.27 +efficientvit_l2,256,1451.75,705.344,1024,63.71,9.09,25.49 +convformer_s18,224,1450.71,705.851,1024,26.77,3.96,15.82 +vit_base_patch16_siglip_gap_224,224,1449.9,706.247,1024,85.8,17.49,23.75 +dpn92,224,1447.04,707.638,1024,37.67,6.54,18.21 +gcvit_tiny,224,1443.56,709.345,1024,28.22,4.79,29.82 +convit_small,224,1442.31,709.962,1024,27.78,5.76,17.87 +focalnet_small_srf,224,1440.19,711.006,1024,49.89,8.62,26.26 +vit_betwixt_patch16_reg1_gap_256,256,1438.93,711.629,1024,60.4,16.32,27.83 +vit_base_patch16_siglip_224,224,1434.62,713.769,1024,92.88,17.73,24.06 +vit_betwixt_patch16_reg4_gap_256,256,1427.38,717.384,1024,60.4,16.52,28.24 +vit_base_patch16_gap_224,224,1426.06,718.052,1024,86.57,17.49,25.59 +maxvit_tiny_tf_224,224,1425.39,538.79,768,30.92,5.6,35.78 +nf_ecaresnet101,224,1424.0,719.089,1024,44.55,8.01,16.27 +coatnet_1_rw_224,224,1423.83,719.174,1024,41.72,8.04,34.6 +nf_seresnet101,224,1422.7,719.746,1024,49.33,8.02,16.27 +coatnet_rmlp_1_rw2_224,224,1422.67,719.763,1024,41.72,8.11,40.13 +seresnext50_32x4d,288,1414.64,723.843,1024,27.56,7.04,23.82 +seresnext101_32x4d,224,1413.69,724.336,1024,48.96,8.02,21.26 +legacy_xception,299,1413.65,543.263,768,22.86,8.4,35.83 +legacy_seresnext101_32x4d,224,1412.48,724.957,1024,48.96,8.02,21.26 +hgnetv2_b5,288,1407.43,727.559,1024,39.57,10.84,18.5 +vit_small_patch16_18x2_224,224,1406.97,727.793,1024,64.67,13.71,35.69 +resnetv2_152,224,1397.01,732.984,1024,60.19,11.55,22.56 +efficientnet_b4,320,1389.63,368.432,512,19.34,3.13,34.76 +vit_base_patch16_clip_quickgelu_224,224,1387.46,738.029,1024,86.19,17.58,23.9 +nfnet_f0,256,1379.83,742.109,1024,71.49,12.62,18.05 +resnet152,224,1373.51,745.522,1024,60.19,11.56,22.56 +flexivit_base,240,1371.79,746.461,1024,86.59,20.29,28.36 +ecaresnet50t,320,1370.27,747.287,1024,25.57,8.82,24.13 +efficientvit_b3,256,1369.93,560.6,768,48.65,5.2,35.01 +vit_relpos_base_patch16_224,224,1369.33,747.799,1024,86.43,17.51,24.97 +cs3edgenet_x,288,1368.22,748.405,1024,47.82,14.59,16.36 +vgg16_bn,224,1364.4,750.503,1024,138.37,15.5,13.56 +resnetv2_152d,224,1363.32,751.094,1024,60.2,11.8,23.36 +mobilenetv4_conv_aa_large,384,1359.23,753.356,1024,32.59,7.07,32.29 +efficientformerv2_s0,224,1356.88,754.66,1024,3.6,0.41,5.3 +regnetx_120,224,1352.8,756.936,1024,46.11,12.13,21.37 +focalnet_small_lrf,224,1350.27,758.334,1024,50.34,8.74,28.61 +twins_pcpvt_large,224,1348.04,759.609,1024,60.99,9.84,35.82 +deit3_small_patch16_384,384,1344.15,761.807,1024,22.21,15.52,50.78 +resnet152c,224,1340.85,763.681,1024,60.21,11.8,23.36 +rexnetr_300,288,1339.94,382.097,512,34.81,5.59,36.61 +maxxvit_rmlp_tiny_rw_256,256,1339.27,573.433,768,29.64,6.66,39.76 +maxvit_tiny_rw_256,256,1338.26,573.871,768,29.07,6.74,44.35 +resnet152d,224,1336.77,766.012,1024,60.21,11.8,23.36 +maxvit_rmlp_tiny_rw_256,256,1336.57,574.593,768,29.15,6.77,46.92 +ese_vovnet99b,224,1332.86,768.26,1024,63.2,16.51,11.27 +poolformerv2_s24,224,1332.11,768.696,1024,21.34,3.42,10.68 +xcit_tiny_12_p8_224,224,1314.83,778.795,1024,6.71,4.81,23.6 +xception41p,299,1314.22,389.574,512,26.91,9.25,39.86 +vit_base_patch32_clip_448,448,1306.81,783.576,1024,88.34,17.93,23.9 +convnext_base,224,1301.11,787.008,1024,88.59,15.38,28.75 +efficientnet_el,300,1300.79,787.203,1024,10.59,8.0,30.7 +nextvit_large,224,1299.51,787.975,1024,57.87,10.78,28.99 +efficientnet_el_pruned,300,1297.09,789.448,1024,10.59,8.0,30.7 +vit_base_patch16_xp_224,224,1295.73,790.276,1024,86.51,17.56,23.9 +dla169,224,1285.68,796.451,1024,53.39,11.6,20.2 +regnety_120,224,1281.03,799.347,1024,51.82,12.14,21.38 +hrnet_w32,224,1280.36,799.766,1024,41.23,8.97,22.02 +coatnet_1_224,224,1275.86,401.286,512,42.23,8.7,39.0 +tf_efficientnet_el,300,1270.05,806.254,1024,10.59,8.0,30.7 +hrnet_w30,224,1268.85,807.019,1024,37.71,8.15,21.21 +vgg19,224,1266.93,808.24,1024,143.67,19.63,14.86 +mixnet_xxl,224,1264.71,607.242,768,23.96,2.04,23.43 +maxvit_tiny_pm_256,256,1264.25,607.461,768,30.09,6.61,47.9 +hiera_base_plus_224,224,1260.02,812.675,1024,69.9,12.67,37.98 +mobilenetv4_conv_large,448,1258.67,610.158,768,32.59,8.75,37.17 +twins_svt_base,224,1256.07,815.231,1024,56.07,8.59,26.33 +vit_base_patch16_rpn_224,224,1255.52,815.59,1024,86.54,17.49,23.75 +nest_small,224,1253.67,816.789,1024,38.35,10.35,40.04 +hgnet_small,288,1251.61,613.599,768,24.36,14.09,14.53 +efficientformerv2_s1,224,1251.14,818.442,1024,6.19,0.67,7.66 +densenet161,224,1249.24,819.689,1024,28.68,7.79,11.06 +resnet152s,224,1245.59,822.091,1024,60.32,12.92,24.96 +vit_mediumd_patch16_reg4_gap_256,256,1243.97,823.162,1024,64.11,17.87,37.57 +nest_small_jx,224,1243.46,823.5,1024,38.35,10.35,40.04 +sequencer2d_m,224,1232.89,830.559,1024,38.31,6.55,14.26 +vit_relpos_base_patch16_rpn_224,224,1232.22,831.004,1024,86.41,17.51,24.97 +repvgg_b2,224,1228.43,833.576,1024,89.02,20.45,12.9 +swin_small_patch4_window7_224,224,1226.82,834.669,1024,49.61,8.77,27.47 +legacy_seresnet152,224,1220.81,838.774,1024,66.82,11.33,22.08 +efficientnet_b3_g8_gn,288,1217.35,630.868,768,14.25,2.59,23.35 +eca_nfnet_l1,256,1214.94,842.831,1024,41.41,9.62,22.04 +mobilenetv4_hybrid_large,384,1210.73,845.759,1024,37.76,7.77,34.52 +swinv2_tiny_window8_256,256,1208.62,847.237,1024,28.35,5.96,24.57 +seresnet152,224,1205.5,849.426,1024,66.82,11.57,22.61 +inception_v4,299,1197.2,855.32,1024,42.68,12.28,15.09 +repvgg_b3g4,224,1194.99,856.9,1024,83.83,17.89,15.1 +fastvit_sa24,256,1192.29,858.839,1024,21.55,3.8,24.32 +resnetv2_101,288,1191.07,859.716,1024,44.54,12.94,26.83 +efficientnet_lite4,380,1188.14,323.185,384,13.01,4.04,45.66 +xcit_small_24_p16_224,224,1187.59,862.241,1024,47.67,9.1,23.64 +mvitv2_small_cls,224,1178.05,869.223,1024,34.87,7.04,28.17 +dm_nfnet_f0,256,1174.19,872.081,1024,71.49,12.62,18.05 +tnt_s_patch16_224,224,1171.01,874.446,1024,23.76,5.24,24.37 +regnetx_160,224,1167.74,876.901,1024,54.28,15.99,25.52 +mvitv2_small,224,1166.57,877.775,1024,34.87,7.0,28.08 +resnet101,288,1162.74,880.669,1024,44.55,12.95,26.83 +xception41,299,1160.72,441.096,512,26.97,9.28,39.86 +davit_base,224,1158.29,663.034,768,87.95,15.51,40.66 +vgg19_bn,224,1153.27,887.9,1024,143.68,19.66,14.86 +convnext_small,288,1152.13,888.774,1024,50.22,14.39,35.65 +vit_base_patch16_reg4_gap_256,256,1148.57,891.534,1024,86.62,23.5,33.89 +coat_tiny,224,1146.04,893.499,1024,5.5,4.35,27.2 +pvt_v2_b4,224,1144.8,894.466,1024,62.56,10.14,53.74 +cait_xxs36_224,224,1141.88,896.758,1024,17.3,3.77,30.34 +nf_regnet_b4,384,1139.48,898.646,1024,30.21,4.7,28.61 +tresnet_xl,224,1139.37,898.735,1024,78.44,15.2,15.34 +crossvit_base_240,240,1129.38,906.68,1024,105.03,21.22,36.33 +vit_small_r26_s32_384,384,1128.5,907.387,1024,36.47,10.43,29.85 +vit_base_patch16_siglip_gap_256,256,1126.79,908.765,1024,85.84,23.13,33.23 +dla102x2,224,1125.59,909.733,1024,41.28,9.34,29.91 +resnet152d,256,1122.78,912.013,1024,60.21,15.41,30.51 +vit_base_patch16_siglip_256,256,1115.06,918.328,1024,92.93,23.44,33.63 +hiera_base_abswin_256,256,1108.56,923.708,1024,51.27,12.46,40.7 +wide_resnet50_2,288,1108.54,923.726,1024,68.88,18.89,23.81 +eva02_base_patch16_clip_224,224,1105.74,926.062,1024,86.26,17.62,26.32 +tf_efficientnet_lite4,380,1103.18,348.074,384,13.01,4.04,45.66 +vit_large_r50_s32_224,224,1102.83,928.511,1024,328.99,19.58,24.41 +efficientnetv2_s,384,1096.79,933.624,1024,21.46,8.44,35.77 +vit_betwixt_patch16_rope_reg4_gap_256,256,1096.33,934.014,1024,60.23,16.52,28.24 +vgg13_bn,224,1093.1,936.772,1024,133.05,11.33,12.25 +efficientvit_l2,288,1092.38,937.387,1024,63.71,11.51,32.19 +hrnet_w18_ssld,288,1090.28,939.198,1024,21.3,7.14,26.96 +convnext_tiny,384,1087.48,706.209,768,28.59,13.14,39.48 +pvt_v2_b5,224,1085.62,943.223,1024,81.96,11.76,50.92 +samvit_base_patch16_224,224,1070.47,956.58,1024,86.46,17.54,24.54 +regnety_160,224,1070.01,956.986,1024,83.59,15.96,23.04 +tf_efficientnetv2_s,384,1069.37,957.558,1024,21.46,8.44,35.77 +cs3se_edgenet_x,320,1057.79,968.048,1024,50.72,18.01,20.21 +resnetaa101d,288,1046.05,978.908,1024,44.57,15.07,29.03 +regnetz_d32,320,1039.68,984.905,1024,27.58,9.33,37.08 +efficientnetv2_rw_s,384,1037.06,987.392,1024,23.94,8.72,38.03 +mobilenetv4_conv_aa_large,448,1034.18,742.609,768,32.59,9.63,43.94 +regnetz_d8,320,1031.64,992.584,1024,23.37,6.19,37.08 +seresnet101,288,1031.63,992.588,1024,49.33,12.95,26.87 +vit_small_patch8_224,224,1031.53,992.686,1024,21.67,22.44,80.84 +efficientvit_b3,288,1028.85,746.455,768,48.65,6.58,44.2 +regnetv_064,288,1024.98,999.029,1024,30.58,10.55,27.11 +regnety_064,288,1024.29,999.702,1024,30.58,10.56,27.11 +poolformer_m36,224,1023.88,1000.107,1024,56.17,8.8,22.02 +vgg16,224,1021.7,1002.242,1024,138.36,15.47,13.56 +wide_resnet101_2,224,1020.62,1003.3,1024,126.89,22.8,21.23 +rdnet_base,224,1017.24,754.976,768,87.45,15.4,31.14 +dpn98,224,1014.12,1009.734,1024,61.57,11.73,25.2 +resnet200,224,1013.79,1010.054,1024,64.67,15.07,32.19 +convnextv2_small,224,1013.29,1010.559,1024,50.32,8.71,21.56 +convnextv2_tiny,288,1011.72,759.092,768,28.64,7.39,22.21 +regnetz_040,320,1006.41,508.726,512,27.12,6.35,37.78 +convmixer_1024_20_ks9_p14,224,1004.68,1019.218,1024,24.38,5.55,5.51 +vit_base_patch16_plus_240,240,1004.42,1019.485,1024,117.56,27.41,33.08 +hgnetv2_b6,224,1002.06,1021.881,1024,75.26,16.88,21.23 +regnety_080,288,1001.83,1022.121,1024,39.18,13.22,29.69 +ecaresnet101d,288,1000.75,1023.221,1024,44.57,13.35,28.19 +swinv2_cr_small_224,224,1000.55,1023.43,1024,49.7,9.07,50.27 +regnetz_040_h,320,1000.36,511.803,512,28.94,6.43,37.94 +resnest101e,256,997.21,1026.848,1024,48.28,13.38,28.66 +convnext_base,256,996.66,1027.418,1024,88.59,20.09,37.55 +vit_base_r50_s16_224,224,993.23,1030.967,1024,97.89,21.66,35.28 +resnetrs101,288,992.71,1031.506,1024,63.62,13.56,28.53 +resnetblur101d,288,992.25,1031.989,1024,44.57,15.07,29.65 +efficientnet_b3_g8_gn,320,990.46,775.388,768,14.25,3.2,28.83 +focalnet_base_srf,224,989.98,1034.35,1024,88.15,15.28,35.01 +swinv2_cr_small_ns_224,224,989.16,1035.213,1024,49.7,9.08,50.27 +regnetz_b16_evos,288,988.59,776.854,768,9.74,2.36,16.43 +regnetz_c16_evos,256,986.94,778.15,768,13.49,2.48,16.57 +maxvit_rmlp_small_rw_224,224,986.14,778.781,768,64.9,10.75,49.3 +inception_next_base,224,985.08,1039.5,1024,86.67,14.85,25.69 +seresnet152d,256,984.13,1040.502,1024,66.84,15.42,30.56 +resnetrs152,256,977.43,1047.637,1024,86.62,15.59,30.83 +resnet101d,320,975.08,1050.156,1024,44.57,16.48,34.77 +inception_resnet_v2,299,965.48,1060.586,1024,55.84,13.18,25.06 +mobilevitv2_150,384,965.44,265.153,256,10.59,9.2,54.25 +resnext101_64x4d,224,959.27,1067.461,1024,83.46,15.52,31.21 +resnext101_32x8d,224,954.55,1072.743,1024,88.79,16.48,31.21 +nfnet_f1,224,951.44,1076.249,1024,132.63,17.87,22.94 +xception65p,299,950.53,538.627,512,39.82,13.91,52.48 +eva02_small_patch14_336,336,946.95,1081.358,1024,22.13,15.48,54.33 +resnext101_32x4d,288,940.09,1089.251,1024,44.18,13.24,35.09 +efficientnet_b4,384,937.43,409.62,384,19.34,4.51,50.04 +coat_lite_medium,224,936.83,1093.037,1024,44.57,9.81,40.06 +focalnet_base_lrf,224,931.65,1099.111,1024,88.75,15.43,38.13 +vit_mediumd_patch16_rope_reg1_gap_256,256,927.19,1104.398,1024,63.95,17.65,37.02 +repvgg_b3,224,927.07,1104.549,1024,123.09,29.16,15.1 +vit_relpos_base_patch16_plus_240,240,923.53,1108.778,1024,117.38,27.3,34.33 +efficientformer_l7,224,921.36,1111.391,1024,82.23,10.17,24.45 +xcit_tiny_24_p16_384,384,918.06,1115.38,1024,12.12,6.87,34.29 +coatnet_2_rw_224,224,914.51,559.852,512,73.87,15.09,49.22 +hrnet_w40,224,911.04,1123.981,1024,57.56,12.75,25.29 +efficientnetv2_m,320,906.73,1129.323,1024,54.14,11.01,39.97 +cait_s24_224,224,906.1,1130.105,1024,46.92,9.35,40.58 +maxvit_small_tf_224,224,903.75,566.52,512,68.93,11.66,53.17 +coat_mini,224,901.59,1135.767,1024,10.34,6.82,33.68 +swin_s3_small_224,224,900.95,852.422,768,49.74,9.43,37.84 +seresnext101_64x4d,224,899.36,1138.577,1024,88.23,15.53,31.25 +poolformerv2_s36,224,899.23,1138.739,1024,30.79,5.01,15.82 +volo_d2_224,224,898.78,1139.307,1024,58.68,14.34,41.34 +seresnext101_32x8d,224,896.76,1141.857,1024,93.57,16.48,31.25 +mobilenetv4_conv_aa_large,480,895.59,857.521,768,32.59,11.05,50.45 +gmlp_b16_224,224,892.77,1146.973,1024,73.08,15.78,30.21 +mobilenetv4_hybrid_large,448,892.61,860.386,768,37.76,10.74,48.61 +nest_base,224,891.48,1148.639,1024,67.72,17.96,53.39 +regnetz_e8,256,885.16,1156.837,1024,57.7,9.91,40.94 +nest_base_jx,224,884.72,1157.412,1024,67.72,17.96,53.39 +resnetv2_50d_evos,288,884.63,1157.531,1024,25.59,7.15,19.7 +seresnext101d_32x8d,224,880.67,1162.73,1024,93.59,16.72,32.05 +swin_base_patch4_window7_224,224,877.35,1167.139,1024,87.77,15.47,36.63 +coatnet_rmlp_2_rw_224,224,872.68,586.689,512,73.88,15.18,54.78 +tf_efficientnet_b4,380,871.25,440.737,384,19.34,4.49,49.49 +levit_384_s8,224,867.41,590.255,512,39.12,9.98,35.86 +vit_base_patch16_rope_reg1_gap_256,256,867.28,1180.694,1024,86.43,23.22,33.39 +tiny_vit_21m_384,384,864.15,592.481,512,21.23,13.77,77.83 +gcvit_small,224,861.28,1188.917,1024,51.09,8.57,41.61 +convnextv2_nano,384,858.36,596.473,512,15.62,7.22,24.61 +crossvit_15_dagger_408,408,856.8,1195.136,1024,28.5,21.45,95.05 +seresnext101_32x4d,288,853.89,1199.198,1024,48.96,13.25,35.12 +coatnet_2_224,224,853.24,600.055,512,74.68,16.5,52.67 +xception65,299,843.97,606.646,512,39.92,13.96,52.48 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+vit_giant_patch14_dinov2,518,17.71,7227.529,128,1136.48,1784.2,2757.89 +vit_giant_patch14_reg4_dinov2,518,17.61,7266.646,128,1136.48,1790.08,2771.21 +eva_giant_patch14_560,560,16.99,7533.399,128,1014.45,1906.76,2577.17 +swinv2_cr_giant_384,384,14.93,2142.906,32,2598.76,1450.71,1394.86 +cait_m48_448,448,14.06,9102.618,128,356.46,329.41,1708.23 +samvit_large_patch16,1024,10.4,769.501,8,308.28,1493.86,2553.78 +vit_so400m_patch14_siglip_gap_896,896,10.27,9344.729,96,416.87,2731.49,8492.88 +samvit_huge_patch16,1024,6.35,944.29,6,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx4090-dynamo.csv b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx4090-dynamo.csv new file mode 100644 index 0000000000000000000000000000000000000000..a0ab261ea686758135f168d7c05392eab2194c02 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx4090-dynamo.csv @@ -0,0 +1,1444 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_efficientnet,160,188911.31,1.347,256,0.36,0.06,0.55 +test_byobnet,160,178532.03,1.426,256,0.46,0.03,0.43 +test_vit,160,155871.09,1.635,256,0.37,0.04,0.48 +lcnet_035,224,97850.77,2.608,256,1.64,0.03,1.04 +tf_mobilenetv3_small_minimal_100,224,93614.74,2.726,256,2.04,0.06,1.41 +lcnet_050,224,86660.68,2.946,256,1.88,0.05,1.26 +mobilenetv3_small_050,224,83744.91,3.049,256,1.59,0.03,0.92 +tinynet_e,106,83059.88,3.074,256,2.04,0.03,0.69 +mobilenetv3_small_075,224,82620.81,3.091,256,2.04,0.05,1.3 +mobilenetv3_small_100,224,79752.52,3.202,256,2.54,0.06,1.42 +mobilenetv4_conv_small,224,74963.65,3.407,256,3.77,0.19,1.97 +tf_mobilenetv3_small_075,224,68793.66,3.712,256,2.04,0.05,1.3 +tf_mobilenetv3_small_100,224,63377.08,4.031,256,2.54,0.06,1.42 +tinynet_d,152,62921.33,4.061,256,2.34,0.05,1.42 +lcnet_075,224,59549.33,4.29,256,2.36,0.1,1.99 +mnasnet_small,224,58424.52,4.373,256,2.03,0.07,2.16 +levit_conv_128s,224,58296.58,4.383,256,7.78,0.31,1.88 +mobilenetv4_conv_small,256,54925.26,4.652,256,3.77,0.25,2.57 +levit_128s,224,53157.92,4.807,256,7.78,0.31,1.88 +resnet10t,176,52732.02,4.846,256,5.44,0.7,1.51 +ghostnet_050,224,52648.58,4.854,256,2.59,0.05,1.77 +regnetx_002,224,50885.47,5.023,256,2.68,0.2,2.16 +repghostnet_050,224,49844.85,5.127,256,2.31,0.05,2.02 +resnet18,160,49720.41,5.14,256,11.69,0.93,1.27 +mobilenetv2_035,224,47284.89,5.404,256,1.68,0.07,2.86 +regnety_002,224,47175.78,5.418,256,3.16,0.2,2.17 +lcnet_100,224,46545.58,5.491,256,2.95,0.16,2.52 +mnasnet_050,224,45318.19,5.64,256,2.22,0.11,3.07 +repghostnet_058,224,44344.16,5.764,256,2.55,0.07,2.59 +levit_conv_128,224,43034.18,5.94,256,9.21,0.41,2.71 +vit_tiny_r_s16_p8_224,224,40905.75,6.25,256,6.34,0.44,2.06 +efficientvit_b0,224,40487.21,6.315,256,3.41,0.1,2.87 +regnetx_004,224,40146.51,6.368,256,5.16,0.4,3.14 +levit_128,224,39948.51,6.4,256,9.21,0.41,2.71 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+resnet18,224,25948.79,9.857,256,11.69,1.82,2.48 +tf_efficientnetv2_b0,192,25767.89,9.926,256,7.14,0.54,3.51 +tf_mobilenetv3_large_075,224,25675.44,9.962,256,3.99,0.16,4.0 +ghostnet_100,224,25628.25,9.98,256,5.18,0.15,3.55 +levit_conv_256,224,25595.46,9.993,256,18.89,1.13,4.23 +convnextv2_atto,224,25550.99,10.011,256,3.71,0.55,3.81 +convnext_atto_ols,224,25437.32,10.056,256,3.7,0.58,4.11 +repghostnet_111,224,25146.64,10.171,256,4.54,0.18,4.38 +mobilenetv3_rw,224,24721.37,10.347,256,5.48,0.23,4.41 +mobilenetv3_large_100,224,24474.85,10.451,256,5.48,0.23,4.41 +deit_tiny_patch16_224,224,24312.15,10.521,256,5.72,1.26,5.97 +vit_tiny_patch16_224,224,24266.11,10.54,256,5.72,1.26,5.97 +repvgg_a0,224,24061.2,10.63,256,9.11,1.52,3.59 +seresnet18,224,24041.64,10.639,256,11.78,1.82,2.49 +legacy_seresnet18,224,24022.17,10.648,256,11.78,1.82,2.49 +hardcorenas_b,224,23830.5,10.734,256,5.18,0.26,5.09 +deit_tiny_distilled_patch16_224,224,23769.88,10.761,256,5.91,1.27,6.01 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+tf_efficientnetv2_b1,192,19947.06,12.825,256,8.14,0.76,4.59 +levit_256d,224,19733.29,12.964,256,26.21,1.4,4.93 +semnasnet_100,224,19697.19,12.988,256,3.89,0.32,6.23 +ese_vovnet19b_slim,224,19687.27,12.994,256,3.17,1.69,3.52 +ghostnet_130,224,19375.99,13.203,256,7.36,0.24,4.6 +regnetx_006,224,19266.74,13.277,256,6.2,0.61,3.98 +mobilenetv2_100,224,18995.91,13.468,256,3.5,0.31,6.68 +tinynet_b,188,18989.86,13.472,256,3.73,0.21,4.44 +hrnet_w18_small,224,18828.83,13.587,256,13.19,1.61,5.72 +repghostnet_150,224,18415.28,13.892,256,6.58,0.32,6.0 +efficientnet_lite0,224,17985.67,14.225,256,4.65,0.4,6.74 +skresnet18,224,17812.46,14.363,256,11.96,1.82,3.24 +resnetblur18,224,17704.62,14.451,256,11.69,2.34,3.39 +tf_efficientnet_lite0,224,17685.01,14.466,256,4.65,0.4,6.74 +gmlp_ti16_224,224,17642.28,14.501,256,5.87,1.34,7.55 +mobilevit_xxs,256,17607.31,14.53,256,1.27,0.42,8.34 +edgenext_x_small,256,17443.49,14.667,256,2.34,0.54,5.93 +resnet14t,224,17428.63,14.68,256,10.08,1.69,5.8 +hardcorenas_e,224,17401.15,14.702,256,8.07,0.35,5.65 +tf_efficientnetv2_b0,224,17323.92,14.768,256,7.14,0.73,4.77 +mobilenetv4_hybrid_medium,224,17322.11,14.769,256,11.07,0.98,6.84 +repvit_m1,224,17266.08,14.817,256,5.49,0.83,7.45 +pvt_v2_b0,224,17238.77,14.841,256,3.67,0.57,7.99 +xcit_tiny_12_p16_224,224,17236.25,14.843,256,6.72,1.24,6.29 +mobilenetv1_125,224,17195.51,14.879,256,6.27,0.89,6.3 +hardcorenas_f,224,17171.48,14.9,256,8.2,0.35,5.57 +hgnetv2_b2,224,17062.45,14.995,256,11.22,1.15,4.12 +repvgg_a1,224,17061.07,14.996,256,14.09,2.64,4.74 +nf_regnet_b0,256,16940.55,15.102,256,8.76,0.64,5.58 +mobilenetv1_100,256,16680.51,15.338,256,4.23,0.76,6.59 +mobilenetv1_100h,256,16453.67,15.55,256,5.28,0.82,6.65 +resnet50,160,16423.72,15.578,256,25.56,2.1,5.67 +repvit_m0_9,224,16304.29,15.692,256,5.49,0.83,7.45 +hgnetv2_b1,288,16122.96,15.869,256,6.34,0.82,4.51 +mobilenetv4_conv_medium,256,16108.18,15.883,256,9.72,1.1,7.58 +crossvit_tiny_240,240,16046.42,15.945,256,7.01,1.57,9.08 +convnext_pico,224,16034.63,15.957,256,9.05,1.37,6.1 +convnext_atto,288,15915.6,16.076,256,3.7,0.91,6.3 +gernet_m,224,15764.43,16.23,256,21.14,3.02,5.24 +vit_betwixt_patch32_clip_224,224,15727.09,16.268,256,61.41,3.09,4.17 +efficientvit_b1,224,15699.71,16.296,256,9.1,0.53,7.25 +resnet18,288,15598.12,16.402,256,11.69,3.01,4.11 +crossvit_9_240,240,15301.84,16.72,256,8.55,1.85,9.52 +resnet50d,160,15291.83,16.732,256,25.58,2.22,6.08 +resnet34,224,15291.59,16.732,256,21.8,3.67,3.74 +convnext_pico_ols,224,15194.28,16.84,256,9.06,1.43,6.5 +tinynet_a,192,15162.99,16.873,256,6.19,0.35,5.41 +mobilenet_edgetpu_v2_s,224,15100.58,16.944,256,5.99,1.21,6.6 +mnasnet_140,224,15010.98,17.045,256,7.12,0.6,7.71 +convnextv2_pico,224,14927.0,17.141,256,9.07,1.37,6.1 +levit_conv_384,224,14906.88,17.164,256,39.13,2.36,6.26 +convnext_atto_ols,288,14901.48,17.17,256,3.7,0.96,6.8 +fbnetv3_b,224,14619.25,17.502,256,8.6,0.42,6.97 +convnextv2_atto,288,14583.28,17.546,256,3.71,0.91,6.3 +regnetz_005,224,14395.17,17.774,256,7.12,0.52,5.86 +mobilenetv4_conv_blur_medium,224,14300.06,17.893,256,9.72,1.22,8.58 +seresnet18,288,14286.39,17.91,256,11.78,3.01,4.11 +efficientformerv2_s1,224,14252.25,17.953,256,6.19,0.67,7.66 +seresnet34,224,14239.31,17.969,256,21.96,3.67,3.74 +mobilevitv2_050,256,14216.39,17.998,256,1.37,0.48,8.04 +legacy_seresnet34,224,14186.47,18.036,256,21.96,3.67,3.74 +mobilenetv2_110d,224,14184.27,18.039,256,4.52,0.45,8.71 +crossvit_9_dagger_240,240,14117.97,18.123,256,8.78,1.99,9.97 +efficientformer_l1,224,14110.06,18.133,256,12.29,1.3,5.53 +rexnetr_100,224,14090.03,18.159,256,4.88,0.43,7.72 +cs3darknet_focus_m,256,14044.33,18.219,256,9.3,1.98,4.89 +resnet34d,224,13891.67,18.42,256,21.82,3.91,4.54 +eva02_tiny_patch14_224,224,13809.61,18.529,256,5.5,1.7,9.14 +tf_efficientnetv2_b2,208,13703.38,18.673,256,10.1,1.06,6.0 +resnext50_32x4d,160,13655.32,18.739,256,25.03,2.17,7.35 +vit_tiny_r_s16_p8_384,384,13521.23,18.924,256,6.36,1.34,6.49 +dla34,224,13514.0,18.934,256,15.74,3.07,5.02 +cs3darknet_m,256,13508.36,18.942,256,9.31,2.08,5.28 +repghostnet_200,224,13466.37,19.001,256,9.8,0.54,7.96 +selecsls42,224,13463.14,19.006,256,30.35,2.94,4.62 +rexnet_100,224,13432.99,19.048,256,4.8,0.41,7.44 +ghostnetv2_100,224,13426.18,19.057,256,6.16,0.18,4.55 +selecsls42b,224,13399.31,19.096,256,32.46,2.98,4.62 +resnet18d,288,13378.17,19.127,256,11.71,3.41,5.43 +seresnet50,160,13373.47,19.133,256,28.09,2.1,5.69 +repvgg_b0,224,13287.11,19.257,256,15.82,3.41,6.15 +hgnetv2_b3,224,13275.88,19.274,256,16.29,1.78,5.07 +resnet26,224,13255.66,19.304,256,16.0,2.36,7.35 +convnext_femto,288,13199.45,19.386,256,5.22,1.3,7.56 +edgenext_x_small,288,13195.3,19.392,256,2.34,0.68,7.5 +resnet50,176,13189.38,19.401,256,25.56,2.62,6.92 +nf_regnet_b2,240,13112.1,19.515,256,14.31,0.97,7.23 +repvit_m1_0,224,13097.62,19.537,256,7.3,1.13,8.69 +levit_384,224,13010.17,19.668,256,39.13,2.36,6.26 +mobilenetv4_hybrid_medium,256,12899.45,19.837,256,11.07,1.29,9.01 +mobilenetv1_125,256,12862.06,19.894,256,6.27,1.16,8.23 +ecaresnet50t,160,12731.21,20.1,256,25.57,2.21,6.04 +semnasnet_140,224,12618.94,20.277,256,6.11,0.6,8.87 +nf_regnet_b1,256,12525.77,20.429,256,10.22,0.82,7.27 +repvit_m2,224,12475.42,20.511,256,8.8,1.36,9.43 +resnetrs50,160,12428.45,20.588,256,35.69,2.29,6.2 +convnext_femto_ols,288,12403.21,20.631,256,5.23,1.35,8.06 +gmixer_12_224,224,12369.96,20.686,256,12.7,2.67,7.26 +mobilenetv2_140,224,12263.51,20.865,256,6.11,0.6,9.57 +pit_s_distilled_224,224,12210.42,20.957,256,24.04,2.9,11.64 +resnetaa34d,224,12190.8,20.991,256,21.82,4.43,5.07 +convnextv2_femto,288,12161.51,21.041,256,5.23,1.3,7.56 +fbnetv3_d,224,12090.69,21.164,256,10.31,0.52,8.5 +nf_resnet26,224,12044.22,21.246,256,16.0,2.41,7.35 +visformer_tiny,224,11983.41,21.354,256,10.32,1.27,5.72 +poolformerv2_s12,224,11968.43,21.381,256,11.89,1.83,5.53 +pit_s_224,224,11936.79,21.437,256,23.46,2.88,11.56 +efficientnet_es_pruned,224,11927.45,21.454,256,5.44,1.81,8.73 +vit_base_patch32_224,224,11925.14,21.458,256,88.22,4.41,5.01 +efficientnet_es,224,11898.4,21.507,256,5.44,1.81,8.73 +vit_base_patch32_clip_224,224,11887.61,21.526,256,88.22,4.41,5.01 +vit_base_patch32_clip_quickgelu_224,224,11884.79,21.532,256,87.85,4.41,5.01 +efficientnet_lite1,240,11883.98,21.533,256,5.42,0.62,10.14 +tiny_vit_5m_224,224,11858.98,21.578,256,12.08,1.28,11.25 +selecsls60,224,11842.43,21.608,256,30.67,3.59,5.52 +tf_efficientnet_es,224,11819.88,21.649,256,5.44,1.81,8.73 +ese_vovnet19b_dw,224,11813.91,21.66,256,6.54,1.34,8.25 +repvit_m1_1,224,11786.2,21.711,256,8.8,1.36,9.43 +selecsls60b,224,11784.48,21.714,256,32.77,3.63,5.52 +resnet26d,224,11759.24,21.761,256,16.01,2.6,8.15 +tf_efficientnet_lite1,240,11696.2,21.877,256,5.42,0.62,10.14 +efficientnet_b0,224,11670.69,21.926,256,5.29,0.4,6.75 +efficientvit_b1,256,11622.24,22.018,256,9.1,0.69,9.46 +mobilenet_edgetpu_v2_m,224,11435.95,22.377,256,8.46,1.85,8.15 +hgnetv2_b4,224,11396.46,22.454,256,19.8,2.75,6.7 +resmlp_12_224,224,11392.33,22.463,256,15.35,3.01,5.5 +darknet17,256,11376.06,22.495,256,14.3,3.26,7.18 +nf_ecaresnet26,224,11300.75,22.644,256,16.0,2.41,7.36 +nf_seresnet26,224,11261.33,22.724,256,17.4,2.41,7.36 +convnext_nano,224,11259.0,22.727,256,15.59,2.46,8.37 +vit_small_patch32_384,384,11156.92,22.936,256,22.92,3.45,8.25 +efficientnet_b1_pruned,240,11085.12,23.084,256,6.33,0.4,6.21 +resnext50_32x4d,176,11026.93,23.206,256,25.03,2.71,8.97 +tf_efficientnetv2_b1,240,11002.33,23.259,256,8.14,1.21,7.34 +dla46x_c,224,10979.44,23.307,256,1.07,0.54,5.66 +mobilenetv4_conv_aa_medium,256,10961.85,23.344,256,9.72,1.58,10.3 +cs3darknet_focus_m,288,10880.75,23.519,256,9.3,2.51,6.19 +mixer_s16_224,224,10874.1,23.533,256,18.53,3.79,5.97 +edgenext_small,256,10831.26,23.626,256,5.59,1.26,9.07 +dla60x_c,224,10780.61,23.737,256,1.32,0.59,6.01 +mobilenetv4_conv_blur_medium,256,10753.26,23.797,256,9.72,1.59,11.2 +mixer_b32_224,224,10749.19,23.807,256,60.29,3.24,6.29 +resnetblur18,288,10709.95,23.894,256,11.69,3.87,5.6 +rexnetr_130,224,10680.75,23.959,256,7.61,0.68,9.81 +poolformer_s12,224,10614.31,24.108,256,11.92,1.82,5.53 +cs3darknet_m,288,10519.8,24.326,256,9.31,2.63,6.69 +fbnetv3_b,256,10482.22,24.413,256,8.6,0.55,9.1 +resnet101,160,10466.64,24.45,256,44.55,4.0,8.28 +skresnet34,224,10457.92,24.469,256,22.28,3.67,5.13 +convnextv2_nano,224,10414.74,24.572,256,15.62,2.46,8.37 +darknet21,256,10326.92,24.78,256,20.86,3.93,7.47 +ghostnetv2_130,224,10285.16,24.881,256,8.96,0.28,5.9 +gernet_l,256,10282.94,24.887,256,31.08,4.57,8.0 +hgnetv2_b2,288,10247.93,24.971,256,11.22,1.89,6.8 +mobilenetv2_120d,224,10245.9,24.977,256,5.83,0.69,11.97 +convnext_nano_ols,224,10137.55,25.244,256,15.65,2.65,9.38 +dpn48b,224,10002.5,25.584,256,9.13,1.69,8.92 +nf_regnet_b2,272,9981.6,25.638,256,14.31,1.22,9.27 +ecaresnet50d_pruned,224,9963.17,25.685,256,19.94,2.53,6.43 +mobilenetv4_conv_medium,320,9962.23,25.687,256,9.72,1.71,11.84 +mobileone_s1,224,9945.03,25.732,256,4.83,0.86,9.67 +mobilenet_edgetpu_v2_l,224,9929.94,25.772,256,10.92,2.55,9.05 +efficientnet_b0_gn,224,9920.37,25.796,256,5.29,0.42,6.75 +tiny_vit_11m_224,224,9911.24,25.819,256,20.35,2.04,13.49 +convnext_pico,288,9741.71,26.27,256,9.05,2.27,10.08 +resnext26ts,256,9713.54,26.346,256,10.3,2.43,10.52 +nf_regnet_b1,288,9675.15,26.45,256,10.22,1.02,9.2 +vit_small_patch16_224,224,9665.97,26.475,256,22.05,4.61,11.95 +repvgg_a2,224,9665.68,26.476,256,28.21,5.7,6.26 +deit_small_patch16_224,224,9661.41,26.487,256,22.05,4.61,11.95 +deit3_small_patch16_224,224,9645.46,26.532,256,22.06,4.61,11.95 +tf_efficientnet_b0,224,9637.3,26.554,256,5.29,0.4,6.75 +tf_mixnet_s,224,9581.92,26.707,256,4.13,0.25,6.25 +rexnet_130,224,9568.98,26.744,256,7.56,0.68,9.71 +deit_small_distilled_patch16_224,224,9556.61,26.778,256,22.44,4.63,12.02 +vit_wee_patch16_reg1_gap_256,256,9553.41,26.788,256,13.42,3.83,13.9 +xcit_tiny_24_p16_224,224,9465.47,27.036,256,12.12,2.34,11.82 +mixnet_s,224,9398.83,27.228,256,4.13,0.25,6.25 +vit_relpos_small_patch16_224,224,9365.71,27.325,256,21.98,4.59,13.05 +vit_srelpos_small_patch16_224,224,9347.26,27.377,256,21.97,4.59,12.16 +gc_efficientnetv2_rw_t,224,9344.17,27.386,256,13.68,1.94,9.97 +vit_pwee_patch16_reg1_gap_256,256,9263.81,27.625,256,15.25,4.37,15.87 +rexnetr_150,224,9243.8,27.684,256,9.78,0.89,11.13 +hrnet_w18_small_v2,224,9242.43,27.688,256,15.6,2.62,9.65 +vit_base_patch32_clip_256,256,9241.91,27.69,256,87.86,5.76,6.65 +convnext_pico_ols,288,9219.94,27.757,256,9.06,2.37,10.74 +mobilevitv2_075,256,9169.72,27.908,256,2.87,1.05,12.06 +resnet26t,256,9168.78,27.912,256,16.01,3.35,10.52 +resnet34,288,9144.8,27.985,256,21.8,6.07,6.18 +gcresnext26ts,256,9136.94,28.009,256,10.48,2.43,10.53 +legacy_seresnext26_32x4d,224,9127.31,28.037,256,16.79,2.49,9.39 +efficientformerv2_s2,224,9079.95,28.184,256,12.71,1.27,11.77 +regnetx_016,224,9048.99,28.281,256,9.19,1.62,7.93 +sedarknet21,256,9044.06,28.297,256,20.95,3.93,7.47 +mobilenetv4_hybrid_large_075,256,9021.75,28.366,256,22.75,2.06,11.64 +convnextv2_pico,288,8940.75,28.624,256,9.07,2.27,10.08 +mobilenetv4_conv_large,256,8935.42,28.64,256,32.59,2.86,12.14 +efficientnet_lite2,260,8893.65,28.775,256,6.09,0.89,12.9 +efficientvit_b1,288,8878.52,28.825,256,9.1,0.87,11.96 +dpn68,224,8840.27,28.949,256,12.61,2.35,10.47 +regnety_016,224,8805.36,29.063,256,11.2,1.63,8.04 +tf_efficientnet_lite2,260,8796.92,29.092,256,6.09,0.89,12.9 +hgnet_tiny,224,8776.27,29.161,256,14.74,4.54,6.36 +seresnext26ts,256,8763.95,29.202,256,10.39,2.43,10.52 +eca_resnext26ts,256,8762.16,29.207,256,10.3,2.43,10.52 +vit_relpos_small_patch16_rpn_224,224,8724.84,29.332,256,21.97,4.59,13.05 +fbnetv3_d,256,8723.73,29.335,256,10.31,0.68,11.1 +vit_small_r26_s32_224,224,8684.28,29.469,256,36.43,3.56,9.85 +efficientnet_b0,256,8654.63,29.57,256,5.29,0.52,8.81 +mobilenet_edgetpu_v2_m,256,8614.85,29.707,256,8.46,2.42,10.65 +botnet26t_256,256,8607.7,29.728,256,12.49,3.32,11.98 +halonet26t,256,8583.89,29.814,256,12.48,3.19,11.69 +efficientnet_blur_b0,224,8559.05,29.9,256,5.29,0.43,8.72 +resnest14d,224,8557.7,29.905,256,10.61,2.76,7.33 +edgenext_small_rw,256,8555.55,29.913,256,7.83,1.58,9.51 +flexivit_small,240,8505.35,30.09,256,22.06,5.35,14.18 +ecaresnext50t_32x4d,224,8481.39,30.175,256,15.41,2.7,10.09 +ecaresnext26t_32x4d,224,8479.72,30.18,256,15.41,2.7,10.09 +seresnext26t_32x4d,224,8460.91,30.245,256,16.81,2.7,10.09 +dpn68b,224,8448.18,30.292,256,12.61,2.35,10.47 +seresnet34,288,8419.97,30.395,256,21.96,6.07,6.18 +efficientnet_b0_g16_evos,224,8418.52,30.399,256,8.11,1.01,7.42 +ecaresnet101d_pruned,224,8411.86,30.422,256,24.88,3.48,7.69 +resnet101,176,8404.43,30.451,256,44.55,4.92,10.08 +seresnext26d_32x4d,224,8398.85,30.471,256,16.81,2.73,10.19 +resnet34d,288,8385.33,30.521,256,21.82,6.47,7.51 +rexnet_150,224,8361.13,30.608,256,9.73,0.9,11.21 +efficientnetv2_rw_t,224,8343.87,30.671,256,13.65,1.93,9.94 +repvit_m3,224,8338.0,30.693,256,10.68,1.89,13.94 +pvt_v2_b1,224,8309.51,30.799,256,14.01,2.12,15.39 +convit_tiny,224,8301.19,30.83,256,5.71,1.26,7.94 +ecaresnetlight,224,8286.53,30.885,256,30.16,4.11,8.42 +eca_nfnet_l0,224,8268.29,30.953,256,24.14,4.35,10.47 +xcit_nano_12_p16_384,384,8253.59,31.007,256,3.05,1.64,12.15 +resnet50,224,8237.46,31.069,256,25.56,4.11,11.11 +nfnet_l0,224,8226.21,31.11,256,35.07,4.36,10.47 +cs3darknet_focus_l,256,8211.8,31.166,256,21.15,4.66,8.03 +tresnet_m,224,8210.79,31.169,256,31.39,5.75,7.31 +mobileone_s2,224,8190.43,31.246,256,7.88,1.34,11.55 +coatnext_nano_rw_224,224,8142.29,31.431,256,14.7,2.47,12.8 +regnetz_005,288,8113.29,31.543,256,7.12,0.86,9.68 +efficientnet_b1,224,8096.89,31.607,256,7.79,0.59,9.36 +eca_botnext26ts_256,256,8088.97,31.637,256,10.59,2.46,11.6 +mobileone_s0,224,8070.39,31.71,256,5.29,1.09,15.48 +dla60,224,8036.02,31.847,256,22.04,4.26,10.16 +ghostnetv2_160,224,8032.91,31.859,256,12.39,0.42,7.23 +resnet26,288,8028.16,31.879,256,16.0,3.9,12.15 +hgnetv2_b3,288,7993.28,32.017,256,16.29,2.94,8.38 +resnet32ts,256,7987.07,32.043,256,17.96,4.63,11.58 +eca_halonext26ts,256,7959.44,32.153,256,10.76,2.44,11.46 +cs3darknet_l,256,7898.11,32.403,256,21.16,4.86,8.55 +resnet33ts,256,7888.41,32.444,256,19.68,4.76,11.66 +resnet50c,224,7858.28,32.567,256,25.58,4.35,11.92 +fastvit_t8,256,7845.11,32.622,256,4.03,0.7,8.63 +mobilenetv3_large_150d,256,7830.3,32.683,256,14.62,1.03,12.35 +efficientnet_b0_g8_gn,224,7820.23,32.725,256,6.56,0.66,6.75 +vit_small_resnet26d_224,224,7754.96,33.002,256,63.61,5.07,11.12 +lambda_resnet26t,256,7725.02,33.129,256,10.96,3.02,11.87 +coat_lite_tiny,224,7696.35,33.252,256,5.72,1.6,11.65 +resnet50t,224,7687.2,33.293,256,25.57,4.32,11.82 +levit_conv_512,224,7679.59,33.326,256,95.17,5.64,10.22 +mobilevit_xs,256,7674.3,33.348,256,2.32,1.05,16.33 +resnext26ts,288,7671.02,33.363,256,10.3,3.07,13.31 +tf_efficientnetv2_b2,260,7642.74,33.487,256,10.1,1.72,9.84 +resnet50d,224,7639.23,33.502,256,25.58,4.35,11.92 +mobilenetv4_hybrid_medium,320,7554.53,33.877,256,11.07,2.05,14.36 +ecaresnet26t,256,7544.42,33.923,256,16.01,3.35,10.53 +efficientnet_cc_b0_8e,224,7516.53,34.049,256,24.01,0.42,9.42 +nf_regnet_b3,288,7509.89,34.079,256,18.59,1.67,11.84 +efficientnet_cc_b0_4e,224,7508.02,34.087,256,13.31,0.41,9.42 +gmlp_s16_224,224,7502.53,34.112,256,19.42,4.42,15.1 +resnetv2_50,224,7495.78,34.144,256,25.55,4.11,11.11 +vit_tiny_patch16_384,384,7452.45,34.342,256,5.79,4.7,25.39 +vovnet39a,224,7432.2,34.436,256,22.6,7.09,6.73 +gcresnet33ts,256,7420.0,34.491,256,19.88,4.76,11.68 +tf_efficientnetv2_b3,240,7371.52,34.718,256,14.36,1.93,9.95 +wide_resnet50_2,176,7362.71,34.761,256,68.88,7.29,8.97 +resnet152,160,7349.12,34.825,256,60.19,5.9,11.51 +resnetaa34d,288,7340.2,34.867,256,21.82,7.33,8.38 +selecsls84,224,7334.1,34.895,256,50.95,5.9,7.57 +resnetaa50,224,7291.3,35.101,256,25.56,5.15,11.64 +efficientnet_em,240,7258.93,35.257,256,6.9,3.04,14.34 +levit_conv_512d,224,7214.89,35.472,256,92.5,5.85,11.3 +tf_efficientnet_em,240,7200.67,35.543,256,6.9,3.04,14.34 +gcresnext26ts,288,7186.05,35.614,256,10.48,3.07,13.33 +vit_base_patch32_plus_256,256,7183.84,35.627,256,119.48,7.79,7.76 +res2net50_48w_2s,224,7183.73,35.627,256,25.29,4.18,11.72 +coat_lite_mini,224,7158.54,35.75,256,11.01,2.0,12.25 +resnet26d,288,7151.59,35.787,256,16.01,4.29,13.48 +efficientvit_b2,224,7108.81,36.002,256,24.33,1.6,14.62 +repvit_m1_5,224,7103.0,36.031,256,14.64,2.31,15.7 +ese_vovnet19b_dw,288,7097.11,36.062,256,6.54,2.22,13.63 +resnet50_gn,224,7077.2,36.163,256,25.56,4.14,11.11 +eca_resnet33ts,256,7061.62,36.243,256,19.68,4.76,11.66 +resnetv2_50t,224,7051.5,36.295,256,25.57,4.32,11.82 +seresnet33ts,256,7046.47,36.321,256,19.78,4.76,11.66 +eca_vovnet39b,224,7017.69,36.47,256,22.6,7.09,6.74 +resnetv2_50d,224,7006.58,36.528,256,25.57,4.35,11.92 +crossvit_small_240,240,6948.36,36.834,256,26.86,5.63,18.17 +inception_v3,299,6933.82,36.911,256,23.83,5.73,8.97 +levit_512,224,6931.73,36.922,256,95.17,5.64,10.22 +seresnext26ts,288,6920.44,36.982,256,10.39,3.07,13.32 +eca_resnext26ts,288,6919.11,36.989,256,10.3,3.07,13.32 +nf_ecaresnet50,224,6916.89,37.001,256,25.56,4.21,11.13 +resnetblur50,224,6906.06,37.06,256,25.56,5.16,12.02 +ese_vovnet39b,224,6897.07,37.107,256,24.57,7.09,6.74 +hgnetv2_b4,288,6892.16,37.134,256,19.8,4.54,11.08 +nf_seresnet50,224,6872.82,37.239,256,28.09,4.21,11.13 +vgg11_bn,224,6872.74,37.239,256,132.87,7.62,7.44 +vgg11,224,6868.96,37.26,256,132.86,7.61,7.44 +resnext50_32x4d,224,6858.21,37.319,256,25.03,4.26,14.4 +resnetaa50d,224,6834.01,37.45,256,25.58,5.39,12.44 +sam2_hiera_tiny,224,6819.52,37.53,256,26.85,4.91,17.12 +legacy_seresnet50,224,6808.13,37.592,256,28.09,3.88,10.6 +resnet50_clip_gap,224,6805.83,37.606,256,23.53,5.39,12.44 +convnext_nano,288,6796.73,37.655,256,15.59,4.06,13.84 +dla60x,224,6789.78,37.695,256,17.35,3.54,13.8 +efficientnet_b1,240,6787.57,37.706,256,7.79,0.71,10.88 +mobileone_s3,224,6778.3,37.757,256,10.17,1.94,13.85 +edgenext_small,320,6777.59,37.762,256,5.59,1.97,14.16 +mobilevitv2_100,256,6775.09,37.776,256,4.9,1.84,16.08 +convnext_tiny_hnf,224,6762.18,37.848,256,28.59,4.47,13.44 +xcit_small_12_p16_224,224,6719.89,38.086,256,26.25,4.82,12.58 +inception_next_tiny,224,6716.71,38.104,256,28.06,4.19,11.98 +convnext_tiny,224,6713.74,38.121,256,28.59,4.47,13.44 +twins_svt_small,224,6712.42,38.129,256,24.06,2.94,13.75 +resnet50s,224,6700.52,38.196,256,25.68,5.47,13.52 +vit_little_patch16_reg1_gap_256,256,6683.54,38.293,256,22.52,6.27,18.06 +vit_relpos_base_patch32_plus_rpn_256,256,6635.1,38.573,256,119.42,7.68,8.01 +ese_vovnet39b_evos,224,6627.24,38.618,256,24.58,7.07,6.74 +vit_little_patch16_reg4_gap_256,256,6624.29,38.636,256,22.52,6.35,18.33 +skresnet50,224,6610.42,38.716,256,25.8,4.11,12.5 +rexnetr_200,224,6609.18,38.725,256,16.52,1.59,15.11 +cs3sedarknet_l,256,6604.77,38.75,256,21.91,4.86,8.56 +regnetz_b16_evos,224,6598.52,38.787,256,9.74,1.43,9.95 +efficientnet_b2_pruned,260,6597.68,38.791,256,8.31,0.73,9.13 +levit_512d,224,6590.97,38.832,256,92.5,5.85,11.3 +crossvit_15_240,240,6583.92,38.873,256,27.53,5.81,19.77 +densenet121,224,6580.95,38.89,256,7.98,2.87,6.9 +tf_efficientnet_cc_b0_4e,224,6574.97,38.926,256,13.31,0.41,9.42 +tf_efficientnet_cc_b0_8e,224,6564.08,38.99,256,24.01,0.42,9.42 +seresnet50,224,6510.44,39.312,256,28.09,4.11,11.13 +regnetz_b16,224,6497.98,39.386,256,9.72,1.45,9.95 +resnetblur50d,224,6469.2,39.563,256,25.58,5.4,12.82 +regnetx_032,224,6433.96,39.779,256,15.3,3.2,11.37 +resnext50d_32x4d,224,6433.36,39.782,256,25.05,4.5,15.2 +cspresnet50,256,6423.77,39.843,256,21.62,4.54,11.5 +haloregnetz_b,224,6420.84,39.86,256,11.68,1.97,11.94 +convformer_s18,224,6401.93,39.977,256,26.77,3.96,15.82 +gmixer_24_224,224,6397.38,40.007,256,24.72,5.28,14.45 +resnest26d,224,6382.9,40.098,256,17.07,3.64,9.97 +caformer_s18,224,6357.61,40.255,256,26.34,4.13,19.39 +resnet50_clip,224,6340.6,40.365,256,38.32,6.14,12.98 +hgnetv2_b5,224,6336.07,40.393,256,39.57,6.56,11.19 +tf_mixnet_m,224,6323.56,40.473,256,5.01,0.36,8.19 +cs3darknet_focus_l,288,6307.83,40.575,256,21.15,5.9,10.16 +repvgg_b1g4,224,6303.26,40.604,256,39.97,8.15,10.64 +vit_medium_patch16_clip_224,224,6298.39,40.635,256,38.59,8.0,15.93 +resnet32ts,288,6297.13,40.644,256,17.96,5.86,14.65 +deit3_medium_patch16_224,224,6292.44,40.675,256,38.85,8.0,15.93 +mixnet_m,224,6274.65,40.789,256,5.01,0.36,8.19 +convnextv2_tiny,224,6265.42,40.849,256,28.64,4.47,13.44 +convnextv2_nano,288,6252.04,40.936,256,15.62,4.06,13.84 +efficientformer_l3,224,6248.87,40.958,256,31.41,3.93,12.01 +coatnet_pico_rw_224,224,6223.54,41.125,256,10.85,2.05,14.62 +resnet33ts,288,6212.45,41.198,256,19.68,6.02,14.75 +vit_base_resnet26d_224,224,6210.89,41.208,256,101.4,6.97,13.16 +skresnet50d,224,6206.47,41.238,256,25.82,4.36,13.31 +tiny_vit_21m_224,224,6202.51,41.264,256,33.22,4.29,20.08 +ecaresnet50t,224,6182.25,41.4,256,25.57,4.32,11.83 +rexnet_200,224,6181.08,41.407,256,16.37,1.56,14.91 +vit_relpos_medium_patch16_224,224,6168.22,41.493,256,38.75,7.97,17.02 +poolformerv2_s24,224,6162.46,41.532,256,21.34,3.42,10.68 +seresnet50t,224,6161.79,41.537,256,28.1,4.32,11.83 +sehalonet33ts,256,6154.72,41.584,256,13.69,3.55,14.7 +ecaresnet50d,224,6146.92,41.638,256,25.58,4.35,11.93 +vit_srelpos_medium_patch16_224,224,6143.0,41.665,256,38.74,7.96,16.21 +cs3darknet_l,288,6142.02,41.67,256,21.16,6.16,10.83 +crossvit_15_dagger_240,240,6130.99,41.745,256,28.21,6.13,20.43 +vovnet57a,224,6130.59,41.749,256,36.64,8.95,7.52 +cspresnet50d,256,6120.99,41.814,256,21.64,4.86,12.55 +convnext_nano_ols,288,6113.65,41.864,256,15.65,4.38,15.5 +cspresnet50w,256,6107.36,41.907,256,28.12,5.04,12.19 +vit_relpos_medium_patch16_cls_224,224,6098.64,41.967,256,38.76,8.03,18.24 +resnetrs50,224,6086.47,42.05,256,35.69,4.48,12.14 +xcit_nano_12_p8_224,224,6075.31,42.129,256,3.05,2.16,15.71 +gcresnext50ts,256,6060.87,42.228,256,15.67,3.75,15.46 +fbnetv3_g,240,6057.83,42.25,256,16.62,1.28,14.87 +densenetblur121d,224,6056.82,42.256,256,8.0,3.11,7.9 +gcvit_xxtiny,224,6049.45,42.308,256,12.0,2.14,15.36 +vit_base_r26_s32_224,224,6038.4,42.386,256,101.38,6.81,12.36 +nf_regnet_b3,320,6032.74,42.424,256,18.59,2.05,14.61 +efficientnet_b1,256,6002.08,42.642,256,7.79,0.77,12.22 +mobilevit_s,256,5976.11,42.827,256,5.58,2.03,19.94 +resnet152,176,5947.93,43.031,256,60.19,7.22,13.99 +tf_efficientnet_b1,240,5946.3,43.042,256,7.79,0.71,10.88 +res2next50,224,5944.69,43.054,256,24.67,4.2,13.71 +res2net50_26w_4s,224,5895.46,43.412,256,25.7,4.28,12.61 +resnet26t,320,5888.07,43.468,256,16.01,5.24,16.44 +res2net50_14w_8s,224,5885.45,43.487,256,25.06,4.21,13.28 +dla60_res2next,224,5850.87,43.744,256,17.03,3.49,13.17 +resmlp_24_224,224,5846.3,43.778,256,30.02,5.96,10.91 +twins_pcpvt_small,224,5845.62,43.784,256,24.11,3.83,18.08 +dla60_res2net,224,5837.47,43.845,256,20.85,4.15,12.34 +edgenext_base,256,5822.01,43.961,256,18.51,3.85,15.58 +gcresnet33ts,288,5797.71,44.145,256,19.88,6.02,14.78 +regnety_040,224,5793.29,44.179,256,20.65,4.0,12.29 +eva02_tiny_patch14_336,336,5792.23,44.186,256,5.76,4.68,27.16 +resnetv2_50x1_bit,224,5786.08,44.234,256,25.55,4.23,11.11 +regnetv_040,224,5783.78,44.252,256,20.64,4.0,12.29 +ecaresnet50d_pruned,288,5762.03,44.418,256,19.94,4.19,10.61 +efficientvit_l1,224,5756.46,44.461,256,52.65,5.27,15.85 +visformer_small,224,5750.7,44.507,256,40.22,4.88,11.43 +eva02_small_patch14_224,224,5748.38,44.524,256,21.62,6.14,18.28 +ese_vovnet57b,224,5743.03,44.566,256,38.61,8.95,7.52 +hgnet_small,224,5739.36,44.595,256,24.36,8.53,8.79 +gcresnet50t,256,5724.09,44.713,256,25.9,5.42,14.67 +nf_resnet50,256,5711.71,44.81,256,25.56,5.46,14.52 +resnet51q,256,5706.87,44.849,256,35.7,6.38,16.55 +hiera_tiny_224,224,5685.03,45.021,256,27.91,4.91,17.13 +efficientnet_b2,256,5683.07,45.037,256,9.11,0.89,12.81 +seresnext50_32x4d,224,5625.84,45.495,256,27.56,4.26,14.42 +legacy_seresnext50_32x4d,224,5623.19,45.516,256,27.56,4.26,14.42 +sebotnet33ts_256,256,5614.57,45.585,256,13.7,3.89,17.46 +seresnetaa50d,224,5601.18,45.695,256,28.11,5.4,12.46 +res2net50d,224,5592.35,45.768,256,25.72,4.52,13.41 +regnety_032,224,5575.12,45.908,256,19.44,3.2,11.26 +eca_resnet33ts,288,5547.26,46.139,256,19.68,6.02,14.76 +seresnet33ts,288,5546.78,46.143,256,19.78,6.02,14.76 +mobilenetv4_conv_large,320,5543.63,46.169,256,32.59,4.47,18.97 +focalnet_tiny_srf,224,5540.33,46.197,256,28.43,4.42,16.32 +resnetv2_50d_frn,224,5526.17,46.316,256,25.59,4.33,11.92 +coatnet_0_rw_224,224,5518.99,46.375,256,27.44,4.43,18.73 +davit_tiny,224,5515.29,46.407,256,28.36,4.54,18.89 +fastvit_t12,256,5513.86,46.418,256,7.55,1.42,12.42 +vit_relpos_medium_patch16_rpn_224,224,5513.6,46.421,256,38.73,7.97,17.02 +resnetrs101,192,5499.51,46.54,256,63.62,6.04,12.7 +vit_medium_patch16_gap_240,240,5483.44,46.677,256,44.4,9.22,18.81 +efficientformerv2_l,224,5466.54,46.819,256,26.32,2.59,18.54 +regnetx_040,224,5462.75,46.854,256,22.12,3.99,12.2 +resnetv2_50d_gn,224,5452.78,46.939,256,25.57,4.38,11.92 +coatnet_nano_rw_224,224,5433.12,47.109,256,15.14,2.41,15.41 +edgenext_small_rw,320,5425.79,47.173,256,7.83,2.46,14.85 +resnetv2_50d_evos,224,5402.37,47.378,256,25.59,4.33,11.92 +efficientvit_b2,256,5396.4,47.427,256,24.33,2.09,19.03 +dla102,224,5369.84,47.664,256,33.27,7.19,14.18 +cspresnext50,256,5358.14,47.768,256,20.57,4.05,15.86 +hrnet_w18_ssld,224,5312.43,48.178,256,21.3,4.32,16.31 +mobilevitv2_125,256,5304.51,48.251,256,7.48,2.86,20.1 +gc_efficientnetv2_rw_t,288,5303.02,48.263,256,13.68,3.2,16.45 +resnest50d_1s4x24d,224,5302.55,48.268,256,25.68,4.43,13.57 +coatnet_nano_cc_224,224,5300.81,48.285,256,13.76,2.24,15.02 +densenet169,224,5293.85,48.347,256,14.15,3.4,7.3 +resnet61q,256,5274.43,48.526,256,36.85,7.8,17.01 +darknet53,256,5272.3,48.545,256,41.61,9.31,12.39 +hgnet_tiny,288,5268.94,48.578,256,14.74,7.51,10.51 +lambda_resnet26rpt_256,256,5256.58,48.689,256,10.99,3.16,11.87 +resnet50_mlp,256,5238.43,48.86,256,26.65,7.05,16.25 +rdnet_tiny,224,5230.54,48.933,256,23.86,5.06,15.98 +nextvit_small,224,5230.17,48.937,256,31.76,5.81,18.44 +nfnet_f0,192,5229.68,48.94,256,71.49,7.21,10.16 +efficientnet_b3_pruned,300,5219.72,49.033,256,9.86,1.04,11.86 +cs3darknet_focus_x,256,5215.35,49.076,256,35.02,8.03,10.69 +resnet101,224,5204.99,49.174,256,44.55,7.83,16.23 +poolformer_s24,224,5178.8,49.423,256,21.39,3.41,10.68 +dm_nfnet_f0,192,5176.64,49.442,256,71.49,7.21,10.16 +fastvit_s12,256,5169.44,49.511,256,9.47,1.82,13.67 +xcit_tiny_12_p16_384,384,5122.18,49.969,256,6.72,3.64,18.26 +efficientnet_lite3,300,5121.62,49.975,256,8.2,1.65,21.85 +fastvit_sa12,256,5118.51,50.004,256,11.58,1.96,14.03 +focalnet_tiny_lrf,224,5112.56,50.063,256,28.65,4.49,17.76 +seresnext26t_32x4d,288,5093.87,50.247,256,16.81,4.46,16.68 +darknetaa53,256,5089.88,50.286,256,36.02,7.97,12.39 +cs3sedarknet_l,288,5080.41,50.38,256,21.91,6.16,10.83 +tf_efficientnet_lite3,300,5063.4,50.55,256,8.2,1.65,21.85 +hrnet_w18,224,5061.86,50.564,256,21.3,4.32,16.31 +seresnext26d_32x4d,288,5057.03,50.614,256,16.81,4.51,16.85 +cs3darknet_x,256,5045.79,50.726,256,35.05,8.38,11.35 +resnet101c,224,5044.49,50.739,256,44.57,8.08,17.04 +swin_tiny_patch4_window7_224,224,5039.45,50.789,256,28.29,4.51,17.06 +maxvit_pico_rw_256,256,4998.32,51.207,256,7.46,1.83,22.3 +ecaresnetlight,288,4963.55,51.567,256,30.16,6.79,13.91 +maxvit_rmlp_pico_rw_256,256,4963.22,51.568,256,7.52,1.85,24.86 +eca_nfnet_l0,288,4962.52,51.577,256,24.14,7.12,17.29 +resnet50,288,4962.25,51.58,256,25.56,6.8,18.37 +resnet101d,224,4941.95,51.792,256,44.57,8.08,17.04 +mobilenetv4_hybrid_medium,384,4939.03,51.822,256,11.07,3.01,21.18 +mobileone_s4,224,4931.74,51.899,256,14.95,3.04,17.74 +nfnet_l0,288,4931.09,51.906,256,35.07,7.13,17.29 +skresnext50_32x4d,224,4919.18,52.031,256,27.48,4.5,17.18 +vit_medium_patch16_gap_256,256,4903.6,52.197,256,38.86,10.59,22.15 +coatnet_bn_0_rw_224,224,4887.13,52.371,256,27.44,4.67,22.04 +dpn68b,288,4849.7,52.777,256,12.61,3.89,17.3 +mobilenetv3_large_150d,320,4830.54,52.987,256,14.62,1.61,19.29 +vit_base_resnet50d_224,224,4826.53,53.03,256,110.97,8.73,16.92 +ecaresnet26t,320,4809.11,53.223,256,16.01,5.24,16.44 +vgg13,224,4804.86,53.27,256,133.05,11.31,12.25 +vgg13_bn,224,4801.12,53.312,256,133.05,11.33,12.25 +repvgg_b1,224,4794.12,53.389,256,57.42,13.16,10.64 +halonet50ts,256,4780.36,53.542,256,22.73,5.3,19.2 +coatnet_rmlp_nano_rw_224,224,4765.2,53.712,256,15.15,2.62,20.34 +gcresnext50ts,288,4747.63,53.91,256,15.67,4.75,19.57 +lambda_resnet50ts,256,4746.66,53.923,256,21.54,5.07,17.48 +swinv2_cr_tiny_224,224,4745.98,53.93,256,28.33,4.66,28.45 +efficientnet_cc_b1_8e,240,4739.38,54.005,256,39.72,0.75,15.44 +ecaresnet50t,256,4722.81,54.195,256,25.57,5.64,15.45 +ecaresnet101d_pruned,288,4706.4,54.383,256,24.88,5.75,12.71 +pvt_v2_b2,224,4697.71,54.485,256,25.36,4.05,27.53 +efficientnetv2_rw_t,288,4690.17,54.572,256,13.65,3.19,16.42 +efficientnet_b1,288,4690.13,54.573,256,7.79,0.97,15.46 +swinv2_cr_tiny_ns_224,224,4689.94,54.575,256,28.33,4.66,28.45 +vit_medium_patch16_reg1_gap_256,256,4686.95,54.609,256,38.88,10.63,22.26 +gcvit_xtiny,224,4677.16,54.722,256,19.98,2.93,20.26 +nf_resnet101,224,4669.02,54.819,256,44.55,8.01,16.23 +regnety_040_sgn,224,4665.34,54.863,256,20.65,4.03,12.29 +lamhalobotnet50ts_256,256,4659.1,54.936,256,22.57,5.02,18.44 +vit_medium_patch16_reg4_gap_256,256,4651.05,55.032,256,38.88,10.76,22.6 +resnet50t,288,4649.07,55.055,256,25.57,7.14,19.53 +dla102x,224,4635.33,55.219,256,26.31,5.89,19.42 +hiera_small_224,224,4619.55,55.408,256,35.01,6.42,20.75 +resnet50d,288,4619.02,55.413,256,25.58,7.19,19.7 +wide_resnet50_2,224,4612.63,55.49,256,68.88,11.43,14.4 +resnetv2_101,224,4600.01,55.642,256,44.54,7.83,16.23 +resnet101_clip_gap,224,4595.55,55.696,256,42.52,9.11,17.56 +tf_efficientnet_b2,260,4594.48,55.709,256,9.11,1.02,13.83 +resnetaa101d,224,4591.39,55.747,256,44.57,9.12,17.56 +tf_mixnet_l,224,4570.85,55.997,256,7.33,0.58,10.84 +efficientvit_l2,224,4564.31,56.076,256,63.71,6.97,19.58 +tf_efficientnetv2_b3,300,4554.51,56.198,256,14.36,3.04,15.74 +resnetv2_50,288,4548.51,56.273,256,25.55,6.79,18.37 +mixnet_l,224,4545.22,56.312,256,7.33,0.58,10.84 +resnet101s,224,4541.57,56.358,256,44.67,9.19,18.64 +gcresnet50t,288,4540.87,56.364,256,25.9,6.86,18.57 +mvitv2_tiny,224,4518.92,56.64,256,24.17,4.7,21.16 +cait_xxs24_224,224,4502.7,56.839,256,11.96,2.53,20.29 +resnet51q,288,4496.23,56.927,256,35.7,8.07,20.94 +nf_resnet50,288,4484.43,57.077,256,25.56,6.88,18.37 +resnest50d,224,4484.41,57.077,256,27.48,5.4,14.36 +nf_regnet_b4,320,4455.95,57.441,256,30.21,3.29,19.88 +crossvit_18_240,240,4445.99,57.569,256,43.27,9.05,26.26 +resnetblur101d,224,4442.61,57.614,256,44.57,9.12,17.94 +efficientnet_b2,288,4434.83,57.715,256,9.11,1.12,16.2 +resnext101_32x4d,224,4430.11,57.776,256,44.18,8.01,21.23 +halo2botnet50ts_256,256,4424.96,57.843,256,22.64,5.02,21.78 +botnet50ts_256,256,4416.09,57.959,256,22.74,5.54,22.23 +vitamin_small_224,224,4405.81,58.094,256,22.03,5.92,26.38 +resnetv2_101d,224,4395.97,58.225,256,44.56,8.07,17.04 +resnetaa50,288,4392.86,58.267,256,25.56,8.52,19.24 +nf_ecaresnet101,224,4388.13,58.329,256,44.55,8.01,16.27 +resnet101_clip,224,4373.65,58.522,256,56.26,9.81,18.08 +nf_seresnet101,224,4356.81,58.747,256,49.33,8.02,16.27 +hieradet_small,256,4349.43,58.848,256,34.72,8.51,27.76 +mobilevitv2_150,256,4334.6,59.05,256,10.59,4.09,24.11 +rexnetr_300,224,4324.73,59.184,256,34.81,3.39,22.16 +tf_efficientnet_cc_b1_8e,240,4322.38,59.217,256,39.72,0.75,15.44 +tresnet_v2_l,224,4301.4,59.505,256,46.17,8.85,16.34 +swin_s3_tiny_224,224,4290.26,59.66,256,28.33,4.64,19.13 +resnext101_32x8d,176,4284.27,59.743,256,88.79,10.33,19.37 +res2net50_26w_6s,224,4277.02,59.844,256,37.05,6.33,15.28 +ese_vovnet39b,288,4248.82,60.241,256,24.57,11.71,11.13 +legacy_seresnet101,224,4227.93,60.54,256,49.33,7.61,15.74 +resnet50_gn,288,4222.36,60.62,256,25.56,6.85,18.37 +cs3sedarknet_x,256,4215.77,60.715,256,35.4,8.38,11.35 +wide_resnet101_2,176,4209.79,60.801,256,126.89,14.31,13.18 +fbnetv3_g,288,4208.81,60.814,256,16.62,1.77,21.09 +crossvit_18_dagger_240,240,4205.84,60.858,256,44.27,9.5,27.03 +maxxvit_rmlp_nano_rw_256,256,4202.69,60.903,256,16.78,4.37,26.05 +vit_base_patch32_384,384,4178.56,61.256,256,88.3,13.06,16.5 +vit_base_patch32_clip_384,384,4178.4,61.258,256,88.3,13.06,16.5 +twins_pcpvt_base,224,4172.72,61.341,256,43.83,6.68,25.25 +resnetblur50,288,4164.2,61.467,256,25.56,8.52,19.87 +efficientvit_b2,288,4145.81,61.737,256,24.33,2.64,24.03 +resnet61q,288,4130.49,61.968,256,36.85,9.87,21.52 +darknet53,288,4124.09,62.065,256,41.61,11.78,15.68 +resnext50_32x4d,288,4120.75,62.115,256,25.03,7.04,23.81 +coatnet_rmlp_0_rw_224,224,4120.51,62.117,256,27.45,4.72,24.89 +poolformerv2_s36,224,4120.18,62.123,256,30.79,5.01,15.82 +resnetaa50d,288,4119.71,62.13,256,25.58,8.92,20.57 +seresnet101,224,4119.53,62.132,256,49.33,7.84,16.27 +cs3edgenet_x,256,4107.9,62.309,256,47.82,11.53,12.92 +cspdarknet53,256,4106.02,62.337,256,27.64,6.57,16.81 +volo_d1_224,224,4101.98,62.399,256,26.63,6.94,24.43 +convnext_tiny_hnf,288,4094.49,62.513,256,28.59,7.39,22.21 +convnext_tiny,288,4073.96,62.828,256,28.59,7.39,22.21 +hrnet_w32,224,4056.25,63.101,256,41.23,8.97,22.02 +regnetx_080,224,4050.3,63.196,256,39.57,8.02,14.06 +nextvit_base,224,4038.08,63.385,256,44.82,8.29,23.71 +pit_b_distilled_224,224,4034.42,63.444,256,74.79,12.5,33.07 +pit_b_224,224,4022.18,63.637,256,73.76,12.42,32.94 +fastvit_mci0,256,4004.83,63.911,256,11.41,2.42,18.29 +convnext_small,224,3998.3,64.017,256,50.22,8.71,21.56 +darknetaa53,288,3994.45,64.079,256,36.02,10.08,15.68 +inception_next_small,224,3986.65,64.204,256,49.37,8.36,19.27 +ecaresnet101d,224,3983.05,64.261,256,44.57,8.08,17.07 +coat_lite_small,224,3969.68,64.477,256,19.84,3.96,22.09 +regnetz_c16,256,3947.47,64.841,256,13.46,2.51,16.57 +regnetx_064,224,3944.55,64.89,256,26.21,6.49,16.37 +cs3sedarknet_xdw,256,3941.85,64.932,256,21.6,5.97,17.18 +cs3darknet_x,288,3934.32,65.058,256,35.05,10.6,14.36 +regnetz_b16,288,3931.2,65.11,256,9.72,2.39,16.43 +rexnetr_200,288,3927.78,65.166,256,16.52,2.62,24.96 +resnetblur50d,288,3921.99,65.263,256,25.58,8.92,21.19 +resmlp_36_224,224,3921.81,65.266,256,44.69,8.91,16.33 +pvt_v2_b2_li,224,3910.42,65.455,256,22.55,3.91,27.6 +maxxvitv2_nano_rw_256,256,3888.05,65.832,256,23.7,6.26,23.05 +resnext50d_32x4d,288,3885.38,65.879,256,25.05,7.44,25.13 +vit_large_patch32_224,224,3882.7,65.923,256,305.51,15.39,13.3 +seresnet50,288,3880.91,65.955,256,28.09,6.8,18.39 +res2net101_26w_4s,224,3875.61,66.043,256,45.21,8.1,18.45 +regnetz_c16_evos,256,3866.46,66.2,256,13.49,2.48,16.57 +repvit_m2_3,224,3852.0,66.448,256,23.69,4.57,26.21 +xcit_tiny_12_p8_224,224,3851.28,66.461,256,6.71,4.81,23.6 +densenet121,288,3850.05,66.481,256,7.98,4.74,11.41 +vgg16_bn,224,3843.29,66.6,256,138.37,15.5,13.56 +resnet101d,256,3841.99,66.622,256,44.57,10.55,22.25 +mixer_b16_224,224,3841.01,66.64,256,59.88,12.62,14.53 +regnetz_b16_evos,288,3840.11,66.654,256,9.74,2.36,16.43 +vgg16,224,3839.88,66.659,256,138.36,15.47,13.56 +vit_medium_patch16_rope_reg1_gap_256,256,3839.28,66.669,256,38.74,10.63,22.26 +mobilenetv4_conv_large,384,3820.17,67.003,256,32.59,6.43,27.31 +convnextv2_tiny,288,3792.59,67.49,256,28.64,7.39,22.21 +rexnet_300,224,3771.96,67.859,256,34.71,3.44,22.4 +convnextv2_small,224,3766.94,67.949,256,50.32,8.71,21.56 +densenet201,224,3748.64,68.28,256,20.01,4.34,7.85 +res2net101d,224,3734.18,68.545,256,45.23,8.35,19.25 +hgnetv2_b5,288,3726.11,68.693,256,39.57,10.84,18.5 +nest_tiny,224,3705.24,69.082,256,17.06,5.83,25.48 +efficientnetv2_s,288,3702.94,69.124,256,21.46,4.75,20.13 +ecaresnet50t,288,3690.88,69.35,256,25.57,7.14,19.55 +seresnet50t,288,3690.42,69.359,256,28.1,7.14,19.55 +edgenext_base,320,3685.34,69.455,256,18.51,6.01,24.32 +coatnet_0_224,224,3684.19,69.475,256,25.04,4.58,24.01 +convit_small,224,3679.62,69.563,256,27.78,5.76,17.87 +ecaresnet50d,288,3676.55,69.621,256,25.58,7.19,19.72 +swinv2_tiny_window8_256,256,3670.64,69.733,256,28.35,5.96,24.57 +nest_tiny_jx,224,3669.11,69.761,256,17.06,5.83,25.48 +eca_nfnet_l1,256,3668.26,69.777,256,41.41,9.62,22.04 +efficientvit_b3,224,3667.52,69.791,256,48.65,3.99,26.9 +mobilevitv2_175,256,3660.41,69.927,256,14.25,5.54,28.13 +resnet152,224,3646.55,70.193,256,60.19,11.56,22.56 +seresnext101_32x4d,224,3626.8,70.575,256,48.96,8.02,21.26 +legacy_seresnext101_32x4d,224,3621.17,70.685,256,48.96,8.02,21.26 +inception_v4,299,3620.42,70.7,256,42.68,12.28,15.09 +xcit_small_24_p16_224,224,3616.94,70.768,256,47.67,9.1,23.64 +tresnet_l,224,3593.17,71.235,256,55.99,10.9,11.9 +resnet152c,224,3589.3,71.313,256,60.21,11.8,23.36 +dla169,224,3570.86,71.681,256,53.39,11.6,20.2 +densenetblur121d,288,3562.85,71.841,256,8.0,5.14,13.06 +tnt_s_patch16_224,224,3559.68,71.906,256,23.76,5.24,24.37 +resnetv2_101x1_bit,224,3551.18,72.078,256,44.54,8.04,16.23 +convnextv2_nano,384,3546.15,72.18,256,15.62,7.22,24.61 +rdnet_small,224,3539.46,72.314,256,50.44,8.74,22.55 +resnet152d,224,3528.53,72.542,256,60.21,11.8,23.36 +regnetv_040,288,3523.74,72.639,256,20.64,6.6,20.3 +efficientvit_l2,256,3518.25,72.753,256,63.71,9.09,25.49 +efficientnetv2_rw_s,288,3505.36,73.02,256,23.94,4.91,21.41 +vit_small_resnet50d_s16_224,224,3504.26,73.043,256,57.53,13.48,24.82 +maxvit_nano_rw_256,256,3501.58,73.099,256,15.45,4.46,30.28 +vit_small_patch16_18x2_224,224,3500.73,73.117,256,64.67,13.71,35.69 +maxvit_rmlp_nano_rw_256,256,3494.64,73.244,256,15.5,4.47,31.92 +res2net50_26w_8s,224,3491.2,73.317,256,48.4,8.37,17.95 +hgnet_small,288,3466.9,73.83,256,24.36,14.09,14.53 +coatnet_rmlp_1_rw_224,224,3457.73,74.026,256,41.69,7.85,35.47 +mobilenetv4_hybrid_medium,448,3448.71,74.22,256,11.07,4.2,29.64 +resnest50d_4s2x40d,224,3439.65,74.416,256,30.42,4.4,17.94 +focalnet_small_srf,224,3422.65,74.785,256,49.89,8.62,26.26 +regnety_040,288,3396.57,75.36,256,20.65,6.61,20.3 +poolformer_s36,224,3394.32,75.41,256,30.86,5.0,15.82 +mvitv2_small,224,3388.47,75.541,256,34.87,7.0,28.08 +davit_small,224,3382.96,75.663,256,49.75,8.8,30.49 +ese_vovnet99b,224,3373.76,75.869,256,63.2,16.51,11.27 +mixer_l32_224,224,3364.3,76.082,256,206.94,11.27,19.86 +vit_base_patch16_siglip_gap_224,224,3352.55,76.35,256,85.8,17.49,23.75 +vit_base_patch16_xp_224,224,3348.63,76.439,256,86.51,17.56,23.9 +vit_base_patch16_224_miil,224,3348.42,76.443,256,94.4,17.59,23.91 +seresnext50_32x4d,288,3348.33,76.445,256,27.56,7.04,23.82 +seresnetaa50d,288,3347.65,76.461,256,28.11,8.92,20.59 +vit_betwixt_patch16_reg1_gap_256,256,3346.86,76.479,256,60.4,16.32,27.83 +vit_base_patch16_224,224,3345.67,76.508,256,86.57,17.58,23.9 +convformer_s36,224,3344.88,76.523,256,40.01,7.67,30.5 +deit_base_patch16_224,224,3344.81,76.527,256,86.57,17.58,23.9 +vit_base_patch16_clip_quickgelu_224,224,3343.2,76.564,256,86.19,17.58,23.9 +deit3_base_patch16_224,224,3342.68,76.576,256,86.59,17.58,23.9 +vit_base_patch16_clip_224,224,3342.03,76.591,256,86.57,17.58,23.9 +pvt_v2_b3,224,3337.39,76.696,256,45.24,6.92,37.7 +hiera_small_abswin_256,256,3333.92,76.777,256,34.36,8.29,26.38 +caformer_s36,224,3329.65,76.872,256,39.3,8.0,37.53 +efficientnet_b3,288,3326.65,76.944,256,12.23,1.63,21.49 +vit_base_patch16_siglip_224,224,3320.61,77.085,256,92.88,17.73,24.06 +vit_betwixt_patch16_reg4_gap_256,256,3319.25,77.116,256,60.4,16.52,28.24 +resnet152s,224,3317.78,77.15,256,60.32,12.92,24.96 +cs3se_edgenet_x,256,3309.24,77.349,256,50.72,11.53,12.94 +vit_base_patch16_gap_224,224,3303.26,77.489,256,86.57,17.49,25.59 +vit_small_patch16_36x1_224,224,3302.19,77.513,256,64.67,13.71,35.69 +deit_base_distilled_patch16_224,224,3300.25,77.56,256,87.34,17.68,24.05 +cs3sedarknet_x,288,3293.75,77.714,256,35.4,10.6,14.37 +vit_relpos_base_patch16_224,224,3288.47,77.838,256,86.43,17.51,24.97 +nextvit_large,224,3287.18,77.867,256,57.87,10.78,28.99 +vit_base_mci_224,224,3282.25,77.986,256,86.35,17.73,24.65 +regnetv_064,224,3266.81,78.353,256,30.58,6.39,16.41 +resnetv2_50d_gn,288,3266.39,78.364,256,25.57,7.24,19.7 +vit_relpos_base_patch16_clsgap_224,224,3264.17,78.417,256,86.43,17.6,25.12 +repvgg_b2,224,3260.0,78.517,256,89.02,20.45,12.9 +beit_base_patch16_224,224,3259.83,78.519,256,86.53,17.58,23.9 +vit_relpos_base_patch16_cls_224,224,3259.47,78.529,256,86.43,17.6,25.12 +repvgg_b2g4,224,3254.26,78.656,256,61.76,12.63,12.9 +resnetv2_50d_evos,288,3251.16,78.731,256,25.59,7.15,19.7 +regnety_080,224,3246.07,78.854,256,39.18,8.0,17.97 +beitv2_base_patch16_224,224,3245.71,78.862,256,86.53,17.58,23.9 +regnety_064,224,3237.99,79.05,256,30.58,6.39,16.41 +sequencer2d_s,224,3229.54,79.258,256,27.65,4.96,11.31 +cs3edgenet_x,288,3219.84,79.497,256,47.82,14.59,16.36 +maxvit_tiny_rw_224,224,3218.36,79.532,256,29.06,5.11,33.11 +coatnet_1_rw_224,224,3216.02,79.59,256,41.72,8.04,34.6 +efficientnet_el_pruned,300,3211.46,79.704,256,10.59,8.0,30.7 +efficientnet_el,300,3210.88,79.719,256,10.59,8.0,30.7 +mixnet_xl,224,3208.22,79.783,256,11.9,0.93,14.57 +vgg19,224,3200.7,79.971,256,143.67,19.63,14.86 +vgg19_bn,224,3198.64,80.025,256,143.68,19.66,14.86 +fastvit_sa24,256,3196.7,80.071,256,21.55,3.8,24.32 +legacy_xception,299,3195.72,80.097,256,22.86,8.4,35.83 +tf_efficientnet_el,300,3194.92,80.117,256,10.59,8.0,30.7 +regnety_032,288,3185.8,80.346,256,19.44,5.29,18.61 +vit_small_patch16_384,384,3183.86,80.395,256,22.2,15.52,50.78 +resnetv2_152,224,3179.36,80.509,256,60.19,11.55,22.56 +deit3_small_patch16_384,384,3174.6,80.63,256,22.21,15.52,50.78 +hrnet_w30,224,3172.72,80.677,256,37.71,8.15,21.21 +focalnet_small_lrf,224,3161.39,80.966,256,50.34,8.74,28.61 +swin_small_patch4_window7_224,224,3153.83,81.161,256,49.61,8.77,27.47 +tf_efficientnetv2_s,300,3151.03,81.233,256,21.46,5.35,22.73 +mobilevitv2_200,256,3150.51,81.247,256,18.45,7.22,32.15 +resnet101,288,3147.94,81.312,256,44.55,12.95,26.83 +vit_base_patch32_clip_448,448,3138.59,81.555,256,88.34,17.93,23.9 +dpn92,224,3109.73,82.312,256,37.67,6.54,18.21 +hiera_base_224,224,3102.53,82.503,256,51.52,9.4,30.42 +nfnet_f0,256,3101.19,82.539,256,71.49,12.62,18.05 +mobilenetv4_conv_aa_large,384,3093.53,82.743,256,32.59,7.07,32.29 +dm_nfnet_f0,256,3083.56,83.011,256,71.49,12.62,18.05 +resnetv2_152d,224,3076.59,83.198,256,60.2,11.8,23.36 +hrnet_w18_ssld,288,3064.15,83.535,256,21.3,7.14,26.96 +dla102x2,224,3061.68,83.603,256,41.28,9.34,29.91 +nf_regnet_b4,384,3049.71,83.932,256,30.21,4.7,28.61 +gcvit_tiny,224,3042.25,84.138,256,28.22,4.79,29.82 +cait_xxs36_224,224,3031.38,84.438,256,17.3,3.77,30.34 +xception41p,299,3019.53,84.771,256,26.91,9.25,39.86 +densenet161,224,3006.8,85.129,256,28.68,7.79,11.06 +ecaresnet50t,320,2995.44,85.452,256,25.57,8.82,24.13 +regnety_080_tv,224,2993.18,85.516,256,39.38,8.51,19.73 +vit_base_patch16_rpn_224,224,2978.25,85.944,256,86.54,17.49,23.75 +twins_pcpvt_large,224,2977.85,85.958,256,60.99,9.84,35.82 +regnetz_040,256,2959.14,86.499,256,27.12,4.06,24.19 +vit_small_r26_s32_384,384,2948.63,86.81,256,36.47,10.43,29.85 +gmlp_b16_224,224,2948.58,86.812,256,73.08,15.78,30.21 +legacy_seresnet152,224,2940.44,87.052,256,66.82,11.33,22.08 +regnetz_040_h,256,2937.29,87.143,256,28.94,4.12,24.29 +twins_svt_base,224,2935.09,87.21,256,56.07,8.59,26.33 +regnetz_d8,256,2918.46,87.707,256,23.37,3.97,23.74 +flexivit_base,240,2911.53,87.916,256,86.59,20.29,28.36 +vit_relpos_base_patch16_rpn_224,224,2909.18,87.987,256,86.41,17.51,24.97 +efficientformer_l7,224,2896.41,88.374,256,82.23,10.17,24.45 +regnetz_d8_evos,256,2895.78,88.394,256,23.46,4.5,24.92 +seresnet152,224,2888.03,88.63,256,66.82,11.57,22.61 +mvitv2_small_cls,224,2869.23,89.212,256,34.87,7.04,28.17 +swinv2_cr_small_224,224,2869.18,89.213,256,49.7,9.07,50.27 +dpn98,224,2866.56,89.294,256,61.57,11.73,25.2 +swinv2_cr_small_ns_224,224,2851.92,89.753,256,49.7,9.08,50.27 +inception_resnet_v2,299,2841.25,90.09,256,55.84,13.18,25.06 +hrnet_w40,224,2841.0,90.097,256,57.56,12.75,25.29 +vit_mediumd_patch16_reg4_gap_256,256,2831.24,90.41,256,64.11,17.87,37.57 +maxxvit_rmlp_tiny_rw_256,256,2818.8,90.808,256,29.64,6.66,39.76 +regnety_040_sgn,288,2814.17,90.956,256,20.65,6.67,20.3 +eva02_base_patch16_clip_224,224,2810.17,91.088,256,86.26,17.62,26.32 +wide_resnet50_2,288,2801.8,91.36,256,68.88,18.89,23.81 +efficientvit_b3,256,2799.72,91.426,256,48.65,5.2,35.01 +poolformerv2_m36,224,2797.01,91.515,256,56.08,8.81,22.02 +resnetv2_101,288,2796.77,91.523,256,44.54,12.94,26.83 +vit_betwixt_patch16_rope_reg4_gap_256,256,2794.77,91.589,256,60.23,16.52,28.24 +resnetaa101d,288,2788.15,91.807,256,44.57,15.07,29.03 +hgnetv2_b6,224,2778.72,92.115,256,75.26,16.88,21.23 +xcit_tiny_24_p16_384,384,2775.49,92.225,256,12.12,6.87,34.29 +mobilenetv4_hybrid_large,384,2771.92,92.344,256,37.76,7.77,34.52 +efficientvit_l2,288,2770.71,92.385,256,63.71,11.51,32.19 +levit_conv_384_s8,224,2749.96,93.083,256,39.12,9.98,35.86 +resnet152d,256,2742.49,93.336,256,60.21,15.41,30.51 +mobilenetv4_conv_large,448,2739.84,93.426,256,32.59,8.75,37.17 +coatnet_rmlp_1_rw2_224,224,2704.02,94.662,256,41.72,8.11,40.13 +resnetblur101d,288,2697.04,94.909,256,44.57,15.07,29.65 +repvgg_b3g4,224,2696.88,94.914,256,83.83,17.89,15.1 +efficientnet_b3,320,2696.04,94.944,256,12.23,2.01,26.52 +convnext_base,224,2678.52,95.564,256,88.59,15.38,28.75 +regnetx_120,224,2677.22,95.611,256,46.11,12.13,21.37 +resnext101_64x4d,224,2667.58,95.957,256,83.46,15.52,31.21 +levit_384_s8,224,2646.86,96.708,256,39.12,9.98,35.86 +resnext101_32x8d,224,2646.71,96.714,256,88.79,16.48,31.21 +wide_resnet101_2,224,2643.91,96.816,256,126.89,22.8,21.23 +inception_next_base,224,2636.33,97.093,256,86.67,14.85,25.69 +tf_efficientnet_b3,300,2632.25,97.244,256,12.23,1.87,23.83 +resnet200,224,2631.56,97.27,256,64.67,15.07,32.19 +resnext101_32x4d,288,2614.05,97.922,256,44.18,13.24,35.09 +rexnetr_300,288,2612.58,97.977,256,34.81,5.59,36.61 +vit_base_patch16_siglip_gap_256,256,2590.2,98.824,256,85.84,23.13,33.23 +vit_large_r50_s32_224,224,2566.89,99.721,256,328.99,19.58,24.41 +vit_base_patch16_siglip_256,256,2564.67,99.808,256,92.93,23.44,33.63 +maxvit_tiny_tf_224,224,2550.75,100.351,256,30.92,5.6,35.78 +efficientnet_b3_gn,288,2546.16,100.533,256,11.73,1.74,23.35 +regnetz_d32,256,2543.95,100.62,256,27.58,5.98,23.74 +samvit_base_patch16_224,224,2528.02,101.254,256,86.46,17.54,24.54 +eva02_small_patch14_336,336,2526.33,101.323,256,22.13,15.48,54.33 +crossvit_base_240,240,2521.38,101.521,256,105.03,21.22,36.33 +convnextv2_base,224,2520.76,101.546,256,88.72,15.38,28.75 +sequencer2d_m,224,2518.88,101.621,256,38.31,6.55,14.26 +regnety_120,224,2511.21,101.932,256,51.82,12.14,21.38 +regnetz_c16,320,2510.69,101.954,256,13.46,3.92,25.88 +coat_tiny,224,2491.49,102.738,256,5.5,4.35,27.2 +vit_base_patch16_reg4_gap_256,256,2482.98,103.091,256,86.62,23.5,33.89 +seresnet101,288,2460.54,104.032,256,49.33,12.95,26.87 +repvgg_b3,224,2459.38,104.081,256,123.09,29.16,15.1 +swinv2_tiny_window16_256,256,2451.11,104.433,256,28.35,6.68,39.02 +resnet101d,320,2444.46,104.716,256,44.57,16.48,34.77 +regnetz_c16_evos,320,2443.36,104.763,256,13.49,3.86,25.88 +xception41,299,2423.55,105.62,256,26.97,9.28,39.86 +tresnet_xl,224,2413.65,106.052,256,78.44,15.2,15.34 +efficientnet_lite4,380,2412.2,106.117,256,13.01,4.04,45.66 +convnext_small,288,2411.61,106.142,256,50.22,14.39,35.65 +coatnet_1_224,224,2409.54,106.233,256,42.23,8.7,39.0 +hrnet_w48_ssld,224,2404.14,106.471,256,77.47,17.34,28.56 +hrnet_w48,224,2403.21,106.513,256,77.47,17.34,28.56 +tf_efficientnet_lite4,380,2401.87,106.574,256,13.01,4.04,45.66 +caformer_m36,224,2378.95,107.598,256,56.2,13.29,50.48 +ecaresnet101d,288,2377.02,107.688,256,44.57,13.35,28.19 +hiera_base_plus_224,224,2375.75,107.745,256,69.9,12.67,37.98 +fastvit_mci1,256,2370.49,107.983,256,21.54,4.72,32.84 +rdnet_base,224,2367.82,108.105,256,87.45,15.4,31.14 +maxvit_tiny_rw_256,256,2365.28,108.22,256,29.07,6.74,44.35 +resnetrs101,288,2361.51,108.395,256,63.62,13.56,28.53 +convformer_m36,224,2358.97,108.511,256,57.05,12.89,42.05 +pvt_v2_b5,224,2356.7,108.615,256,81.96,11.76,50.92 +pvt_v2_b4,224,2355.66,108.663,256,62.56,10.14,53.74 +maxvit_rmlp_tiny_rw_256,256,2354.21,108.731,256,29.15,6.77,46.92 +seresnext101_64x4d,224,2353.54,108.76,256,88.23,15.53,31.25 +xcit_medium_24_p16_224,224,2347.78,109.028,256,84.4,16.13,31.71 +seresnext101_32x8d,224,2341.18,109.335,256,93.57,16.48,31.25 +vit_mediumd_patch16_rope_reg1_gap_256,256,2336.5,109.556,256,63.95,17.65,37.02 +regnetx_160,224,2330.93,109.816,256,54.28,15.99,25.52 +fastvit_sa36,256,2327.07,109.998,256,31.53,5.64,34.61 +volo_d2_224,224,2323.74,110.155,256,58.68,14.34,41.34 +nest_small,224,2322.48,110.217,256,38.35,10.35,40.04 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+vit_large_patch14_dinov2,518,133.12,1923.026,256,304.37,507.15,1058.82 +vit_large_patch14_reg4_dinov2,518,132.04,1938.813,256,304.37,508.9,1064.02 +tf_efficientnet_b8,672,130.88,977.99,128,87.41,63.48,442.89 +swinv2_cr_huge_384,384,130.75,489.486,64,657.94,352.04,583.18 +swinv2_cr_giant_224,224,125.01,1023.872,128,2598.76,483.85,309.15 +maxvit_xlarge_tf_384,384,123.97,516.244,64,475.32,292.78,668.76 +vit_huge_patch16_gap_448,448,123.46,2073.454,256,631.67,544.7,636.83 +cait_m36_384,384,120.83,2118.7,256,271.22,173.11,734.81 +volo_d5_512,512,113.48,2255.978,256,296.09,425.09,1105.37 +xcit_large_24_p8_384,384,112.54,2274.795,256,188.93,415.0,531.82 +eva_giant_patch14_336,336,111.59,2294.19,256,1013.01,620.64,550.67 +nfnet_f6,576,107.92,2372.106,256,438.36,378.69,452.2 +dm_nfnet_f6,576,107.35,2384.806,256,438.36,378.69,452.2 +maxvit_large_tf_512,512,101.74,629.046,64,212.33,244.75,942.15 +convnextv2_huge,512,100.55,1273.014,128,660.29,600.81,413.07 +tf_efficientnet_l2,475,90.19,1064.381,96,480.31,172.11,609.89 +nfnet_f7,608,84.41,3032.802,256,499.5,480.39,570.85 +regnety_2560,384,76.59,1671.223,128,1282.6,747.83,296.49 +davit_huge_fl,768,69.12,925.974,64,360.64,744.84,1060.3 +resnetv2_152x4_bit,480,66.53,1442.988,96,936.53,844.84,414.26 +eva02_enormous_patch14_clip_224,224,63.84,4009.924,256,4350.56,1132.46,497.58 +samvit_base_patch16,1024,61.83,258.784,16,89.67,486.43,1343.27 +maxvit_xlarge_tf_512,512,61.08,785.831,48,475.77,534.14,1413.22 +sam2_hiera_large,1024,53.33,900.092,48,212.15,907.48,2190.34 +vit_giant_patch14_dinov2,518,40.02,4797.862,192,1136.48,1784.2,2757.89 +vit_giant_patch14_reg4_dinov2,518,39.61,4847.503,192,1136.48,1790.08,2771.21 +swinv2_cr_giant_384,384,36.91,866.946,32,2598.76,1450.71,1394.86 +eva_giant_patch14_560,560,35.98,7115.889,256,1014.45,1906.76,2577.17 +efficientnet_l2,800,32.95,1942.195,64,480.31,479.12,1707.39 +tf_efficientnet_l2,800,31.83,1005.239,32,480.31,479.12,1707.39 +samvit_large_patch16,1024,26.29,304.233,8,308.28,1493.86,2553.78 +vit_so400m_patch14_siglip_gap_896,896,23.37,5478.218,128,416.87,2731.49,8492.88 +samvit_huge_patch16,1024,15.57,770.869,12,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx4090.csv b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx4090.csv new file mode 100644 index 0000000000000000000000000000000000000000..1ea87d62f23d8c241b6a0f2abf7954703c316d5a --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nchw-pt240-cu124-rtx4090.csv @@ -0,0 +1,1445 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_vit,160,188343.61,5.426,1024,0.37,0.04,0.48 +test_byobnet,160,114439.82,8.933,1024,0.46,0.03,0.43 +test_efficientnet,160,101055.26,10.121,1024,0.36,0.06,0.55 +tinynet_e,106,76644.06,13.346,1024,2.04,0.03,0.69 +mobilenetv3_small_050,224,70186.63,14.579,1024,1.59,0.03,0.92 +lcnet_035,224,64212.36,15.936,1024,1.64,0.03,1.04 +efficientvit_m0,224,54771.53,18.686,1024,2.35,0.08,0.91 +lcnet_050,224,53644.11,19.078,1024,1.88,0.05,1.26 +mobilenetv3_small_075,224,49475.98,20.686,1024,2.04,0.05,1.3 +mobilenetv3_small_100,224,43953.49,23.286,1024,2.54,0.06,1.42 +tinynet_d,152,40047.08,25.554,1024,2.34,0.05,1.42 +efficientvit_m1,224,38637.78,26.491,1024,2.98,0.17,1.33 +tf_mobilenetv3_small_075,224,37336.46,27.408,1024,2.04,0.05,1.3 +tf_mobilenetv3_small_minimal_100,224,37294.33,27.442,1024,2.04,0.06,1.41 +efficientvit_m2,224,34551.89,29.625,1024,4.19,0.2,1.47 +tf_mobilenetv3_small_100,224,34059.68,30.049,1024,2.54,0.06,1.42 +lcnet_075,224,33742.36,30.337,1024,2.36,0.1,1.99 +mobilenetv4_conv_small,224,32546.3,31.45,1024,3.77,0.19,1.97 +efficientvit_m3,224,29796.09,34.355,1024,6.9,0.27,1.62 +mnasnet_small,224,29488.73,34.713,1024,2.03,0.07,2.16 +levit_128s,224,28915.97,35.397,1024,7.78,0.31,1.88 +ghostnet_050,224,27810.93,36.808,1024,2.59,0.05,1.77 +efficientvit_m4,224,27584.68,37.111,1024,8.8,0.3,1.7 +vit_small_patch32_224,224,27011.35,37.895,1024,22.88,1.15,2.5 +lcnet_100,224,26901.09,38.053,1024,2.95,0.16,2.52 +regnetx_002,224,26857.85,38.105,1024,2.68,0.2,2.16 +resnet18,160,26266.23,38.966,1024,11.69,0.93,1.27 +resnet10t,176,25959.28,39.426,1024,5.44,0.7,1.51 +mobilenetv4_conv_small,256,25751.02,39.753,1024,3.77,0.25,2.57 +repghostnet_050,224,25423.49,40.26,1024,2.31,0.05,2.02 +levit_conv_128s,224,25136.44,40.725,1024,7.78,0.31,1.88 +regnety_002,224,25031.98,40.889,1024,3.16,0.2,2.17 +mobilenetv2_035,224,25024.38,40.909,1024,1.68,0.07,2.86 +vit_tiny_r_s16_p8_224,224,24696.86,41.446,1024,6.34,0.44,2.06 +efficientvit_b0,224,22706.84,45.084,1024,3.41,0.1,2.87 +mnasnet_050,224,22309.73,45.888,1024,2.22,0.11,3.07 +pit_ti_224,224,21640.68,47.298,1024,4.85,0.7,6.19 +pit_ti_distilled_224,224,21496.66,47.616,1024,5.1,0.71,6.23 +tinynet_c,184,20204.93,50.66,1024,2.46,0.11,2.87 +repghostnet_058,224,19802.21,51.689,1024,2.55,0.07,2.59 +mixer_s32_224,224,19527.38,52.427,1024,19.1,1.0,2.28 +mobilenetv2_050,224,19456.73,52.616,1024,1.97,0.1,3.64 +semnasnet_050,224,19111.19,53.559,1024,2.08,0.11,3.44 +levit_128,224,19082.4,53.651,1024,9.21,0.41,2.71 +efficientvit_m5,224,18614.46,54.998,1024,12.47,0.53,2.41 +vit_medium_patch32_clip_224,224,18383.79,55.687,1024,39.69,2.0,3.34 +regnetx_004,224,18131.35,56.448,1024,5.16,0.4,3.14 +gernet_s,224,17973.35,56.961,1024,8.17,0.75,2.65 +lcnet_150,224,17737.51,57.717,1024,4.5,0.34,3.79 +deit_tiny_patch16_224,224,17673.84,57.926,1024,5.72,1.26,5.97 +vit_tiny_patch16_224,224,17666.09,57.952,1024,5.72,1.26,5.97 +cs3darknet_focus_s,256,17602.37,58.161,1024,3.27,0.69,2.7 +deit_tiny_distilled_patch16_224,224,17560.74,58.299,1024,5.91,1.27,6.01 +levit_conv_128,224,17447.46,58.676,1024,9.21,0.41,2.71 +regnetx_004_tv,224,17439.64,58.689,1024,5.5,0.42,3.17 +levit_192,224,16980.83,60.29,1024,10.95,0.66,3.2 +cs3darknet_s,256,16965.42,60.345,1024,3.28,0.72,2.97 +resnet10t,224,16340.38,62.643,1024,5.44,1.1,2.43 +resnet34,160,16119.94,63.502,1024,21.8,1.87,1.91 +repghostnet_080,224,15726.41,65.091,1024,3.28,0.1,3.22 +levit_conv_192,224,15726.05,65.102,1024,10.95,0.66,3.2 +mobilenetv3_large_075,224,15518.95,65.962,1024,3.99,0.16,4.0 +vit_xsmall_patch16_clip_224,224,15266.09,67.063,1024,8.28,1.79,6.65 +hardcorenas_a,224,14844.77,68.969,1024,5.26,0.23,4.38 +mnasnet_075,224,14443.19,70.885,1024,3.17,0.23,4.77 +ese_vovnet19b_slim_dw,224,14337.06,71.41,1024,1.9,0.4,5.28 +nf_regnet_b0,192,14314.09,71.525,1024,8.76,0.37,3.15 +mobilenetv3_rw,224,14298.34,71.594,1024,5.48,0.23,4.41 +resnet14t,176,14213.29,72.022,1024,10.08,1.07,3.61 +mobilenetv3_large_100,224,14191.17,72.134,1024,5.48,0.23,4.41 +pit_xs_224,224,14164.01,72.272,1024,10.62,1.4,7.71 +pit_xs_distilled_224,224,14041.03,72.903,1024,11.0,1.41,7.76 +ghostnet_100,224,13948.64,73.4,1024,5.18,0.15,3.55 +regnetx_006,224,13813.98,74.091,1024,6.2,0.61,3.98 +mobilenetv1_100,224,13760.09,74.406,1024,4.23,0.58,5.04 +tf_mobilenetv3_large_075,224,13614.3,75.186,1024,3.99,0.16,4.0 +hardcorenas_b,224,13584.5,75.367,1024,5.18,0.26,5.09 +resnet18,224,13537.15,75.62,1024,11.69,1.82,2.48 +hardcorenas_c,224,13512.54,75.768,1024,5.52,0.28,5.01 +mobilenetv1_100h,224,13446.31,76.143,1024,5.28,0.63,5.09 +regnety_004,224,13238.44,77.296,1024,4.34,0.41,3.89 +tf_efficientnetv2_b0,192,13104.66,78.117,1024,7.14,0.54,3.51 +tf_mobilenetv3_large_minimal_100,224,13074.99,78.292,1024,3.92,0.22,4.4 +tinynet_b,188,13009.4,78.686,1024,3.73,0.21,4.44 +mobilenet_edgetpu_v2_xs,224,12965.31,78.967,1024,4.46,0.7,4.8 +vit_betwixt_patch32_clip_224,224,12906.63,79.313,1024,61.41,3.09,4.17 +convnext_atto,224,12819.99,79.862,1024,3.7,0.55,3.81 +repghostnet_100,224,12769.67,80.164,1024,4.07,0.15,3.98 +mnasnet_100,224,12674.12,80.779,1024,4.38,0.33,5.46 +seresnet18,224,12647.44,80.94,1024,11.78,1.82,2.49 +hardcorenas_d,224,12555.09,81.548,1024,7.5,0.3,4.93 +levit_256,224,12506.52,81.863,1024,18.89,1.13,4.23 +legacy_seresnet18,224,12443.24,82.276,1024,11.78,1.82,2.49 +tf_mobilenetv3_large_100,224,12230.56,83.698,1024,5.48,0.23,4.41 +convnext_atto_ols,224,12208.81,83.859,1024,3.7,0.58,4.11 +mobilenetv2_075,224,12206.99,83.872,1024,2.64,0.22,5.86 +edgenext_xx_small,256,12172.17,84.111,1024,1.33,0.26,3.33 +semnasnet_075,224,12007.14,85.256,1024,2.91,0.23,5.54 +regnety_006,224,11941.12,85.707,1024,6.06,0.61,4.33 +levit_conv_256,224,11783.71,86.886,1024,18.89,1.13,4.23 +repghostnet_111,224,11492.86,89.073,1024,4.54,0.18,4.38 +spnasnet_100,224,11256.71,90.94,1024,4.42,0.35,6.03 +levit_256d,224,11118.19,92.088,1024,26.21,1.4,4.93 +hardcorenas_f,224,11102.22,92.22,1024,8.2,0.35,5.57 +convnext_femto,224,11040.93,92.732,1024,5.22,0.79,4.57 +resnet18d,224,11008.69,92.989,1024,11.71,2.06,3.29 +repvgg_a0,224,10985.0,93.181,1024,9.11,1.52,3.59 +dla46_c,224,10958.84,93.426,1024,1.3,0.58,4.5 +mobilenetv1_100,256,10920.28,93.756,1024,4.23,0.76,6.59 +hardcorenas_e,224,10912.94,93.82,1024,8.07,0.35,5.65 +mobilenetv4_conv_medium,224,10887.04,94.044,1024,9.72,0.84,5.8 +ghostnet_130,224,10826.86,94.566,1024,7.36,0.24,4.6 +ese_vovnet19b_slim,224,10822.95,94.602,1024,3.17,1.69,3.52 +mobilenetv2_100,224,10810.08,94.71,1024,3.5,0.31,6.68 +mobilenetv1_100h,256,10741.59,95.317,1024,5.28,0.82,6.65 +semnasnet_100,224,10684.42,95.813,1024,3.89,0.32,6.23 +regnetx_008,224,10649.58,96.116,1024,7.26,0.81,5.15 +convnext_femto_ols,224,10568.01,96.88,1024,5.23,0.82,4.87 +crossvit_tiny_240,240,10467.46,97.811,1024,7.01,1.57,9.08 +mobilenet_edgetpu_100,224,10392.92,98.516,1024,4.09,1.0,5.75 +efficientnet_lite0,224,10376.87,98.665,1024,4.65,0.4,6.74 +mobilenetv1_125,224,10367.58,98.748,1024,6.27,0.89,6.3 +mobilevit_xxs,256,10336.7,99.048,1024,1.27,0.42,8.34 +fbnetc_100,224,10321.13,99.198,1024,5.57,0.4,6.51 +tinynet_a,192,10287.99,99.507,1024,6.19,0.35,5.41 +tf_efficientnetv2_b0,224,10217.13,100.198,1024,7.14,0.73,4.77 +mobilenetv4_hybrid_medium_075,224,10204.4,100.333,1024,7.31,0.66,5.65 +hgnetv2_b0,224,10169.22,100.683,1024,6.0,0.33,2.12 +vit_base_patch32_clip_224,224,10127.52,101.084,1024,88.22,4.41,5.01 +vit_base_patch32_224,224,10112.91,101.231,1024,88.22,4.41,5.01 +tf_efficientnetv2_b1,192,10100.97,101.35,1024,8.14,0.76,4.59 +regnety_008,224,9957.68,102.806,1024,6.26,0.81,5.25 +levit_conv_256d,224,9877.75,103.652,1024,26.21,1.4,4.93 +crossvit_9_240,240,9867.74,103.759,1024,8.55,1.85,9.52 +repghostnet_130,224,9782.41,104.652,1024,5.48,0.25,5.24 +regnety_008_tv,224,9578.99,106.856,1024,6.43,0.84,5.42 +edgenext_xx_small,288,9574.3,106.938,1024,1.33,0.33,4.21 +resnetblur18,224,9357.08,109.406,1024,11.69,2.34,3.39 +vit_small_patch32_384,384,9286.73,110.252,1024,22.92,3.45,8.25 +xcit_nano_12_p16_224,224,9284.43,110.262,1024,3.05,0.56,4.17 +mobilenet_edgetpu_v2_s,224,9204.18,111.24,1024,5.99,1.21,6.6 +visformer_tiny,224,9194.72,111.341,1024,10.32,1.27,5.72 +dla46x_c,224,9155.27,111.834,1024,1.07,0.54,5.66 +crossvit_9_dagger_240,240,9081.92,112.735,1024,8.78,1.99,9.97 +mobilenetv4_conv_medium,256,9068.95,112.899,1024,9.72,1.1,7.58 +pvt_v2_b0,224,8956.42,114.305,1024,3.67,0.57,7.99 +resnet14t,224,8930.87,114.632,1024,10.08,1.69,5.8 +efficientnet_b0,224,8926.12,114.703,1024,5.29,0.4,6.75 +mnasnet_140,224,8908.91,114.927,1024,7.12,0.6,7.71 +fbnetv3_b,224,8904.43,114.985,1024,8.6,0.42,6.97 +pit_s_224,224,8898.37,115.049,1024,23.46,2.88,11.56 +pit_s_distilled_224,224,8880.77,115.275,1024,24.04,2.9,11.64 +mobilevitv2_050,256,8864.77,115.498,1024,1.37,0.48,8.04 +cs3darknet_focus_m,256,8810.63,116.207,1024,9.3,1.98,4.89 +tf_efficientnet_lite0,224,8709.74,117.542,1024,4.65,0.4,6.74 +dla60x_c,224,8644.34,118.444,1024,1.32,0.59,6.01 +efficientnet_b1_pruned,240,8640.04,118.503,1024,6.33,0.4,6.21 +efficientvit_b1,224,8637.15,118.543,1024,9.1,0.53,7.25 +convnext_pico,224,8576.11,119.388,1024,9.05,1.37,6.1 +regnetz_005,224,8537.29,119.917,1024,7.12,0.52,5.86 +rexnet_100,224,8512.86,120.248,1024,4.8,0.41,7.44 +repghostnet_150,224,8511.4,120.282,1024,6.58,0.32,6.0 +mobilenetv1_125,256,8425.89,121.511,1024,6.27,1.16,8.23 +repvit_m1,224,8403.37,121.801,1024,5.49,0.83,7.45 +vit_base_patch32_clip_quickgelu_224,224,8389.12,122.035,1024,87.85,4.41,5.01 +ese_vovnet19b_dw,224,8324.1,123.002,1024,6.54,1.34,8.25 +resnet18,288,8261.03,123.927,1024,11.69,3.01,4.11 +repvgg_a1,224,8255.57,124.011,1024,14.09,2.64,4.74 +rexnetr_100,224,8248.96,124.109,1024,4.88,0.43,7.72 +convnext_pico_ols,224,8234.27,124.344,1024,9.06,1.43,6.5 +cs3darknet_m,256,8195.28,124.934,1024,9.31,2.08,5.28 +resnet34,224,8192.41,124.968,1024,21.8,3.67,3.74 +resnet50,160,8139.65,125.774,1024,25.56,2.1,5.67 +mobilenetv4_hybrid_medium,224,8135.37,125.856,1024,11.07,0.98,6.84 +vit_tiny_r_s16_p8_384,384,8129.06,125.953,1024,6.36,1.34,6.49 +selecsls42,224,8099.05,126.406,1024,30.35,2.94,4.62 +mobilenetv2_110d,224,8095.22,126.469,1024,4.52,0.45,8.71 +nf_regnet_b0,256,8083.32,126.666,1024,8.76,0.64,5.58 +selecsls42b,224,8049.57,127.183,1024,32.46,2.98,4.62 +repvit_m0_9,224,7983.61,128.234,1024,5.49,0.83,7.45 +tf_efficientnetv2_b2,208,7955.98,128.679,1024,10.1,1.06,6.0 +vit_base_patch32_clip_256,256,7846.08,130.484,1024,87.86,5.76,6.65 +hrnet_w18_small,224,7772.99,131.724,1024,13.19,1.61,5.72 +convnext_atto,288,7751.5,132.09,1024,3.7,0.91,6.3 +efficientnet_b0_gn,224,7748.62,132.137,1024,5.29,0.42,6.75 +levit_384,224,7713.47,132.738,1024,39.13,2.36,6.26 +seresnet18,288,7713.21,132.732,1024,11.78,3.01,4.11 +gernet_m,224,7709.22,132.814,1024,21.14,3.02,5.24 +vit_small_patch16_224,224,7693.38,133.087,1024,22.05,4.61,11.95 +deit_small_patch16_224,224,7687.89,133.182,1024,22.05,4.61,11.95 +resnet50d,160,7681.19,133.288,1024,25.58,2.22,6.08 +tf_efficientnet_b0,224,7640.8,133.985,1024,5.29,0.4,6.75 +deit_small_distilled_patch16_224,224,7620.92,134.352,1024,22.44,4.63,12.02 +edgenext_x_small,256,7580.19,135.075,1024,2.34,0.54,5.93 +seresnet34,224,7569.0,135.26,1024,21.96,3.67,3.74 +ghostnetv2_100,224,7562.78,135.385,1024,6.16,0.18,4.55 +skresnet18,224,7543.01,135.725,1024,11.96,1.82,3.24 +mobilenetv2_140,224,7486.19,136.767,1024,6.11,0.6,9.57 +semnasnet_140,224,7485.31,136.75,1024,6.11,0.6,8.87 +legacy_seresnet34,224,7446.07,137.508,1024,21.96,3.67,3.74 +fbnetv3_d,224,7426.0,137.878,1024,10.31,0.52,8.5 +hgnetv2_b1,224,7416.62,138.054,1024,6.34,0.49,2.73 +convnext_atto_ols,288,7379.61,138.745,1024,3.7,0.96,6.8 +mixer_b32_224,224,7373.37,138.863,1024,60.29,3.24,6.29 +vit_pwee_patch16_reg1_gap_256,256,7329.65,139.691,1024,15.25,4.37,15.87 +resnet34d,224,7202.58,142.142,1024,21.82,3.91,4.54 +levit_conv_384,224,7171.07,142.782,1024,39.13,2.36,6.26 +efficientnet_lite1,240,7085.22,144.508,1024,5.42,0.62,10.14 +efficientnet_b0,256,7082.56,144.564,1024,5.29,0.52,8.81 +dla34,224,7078.3,144.651,1024,15.74,3.07,5.02 +mobilenet_edgetpu_v2_m,224,7064.66,144.928,1024,8.46,1.85,8.15 +mixnet_s,224,7052.75,145.177,1024,4.13,0.25,6.25 +fbnetv3_b,256,7022.87,145.793,1024,8.6,0.55,9.1 +seresnet50,160,6986.99,146.503,1024,28.09,2.1,5.69 +cs3darknet_focus_m,288,6969.39,146.91,1024,9.3,2.51,6.19 +eva02_tiny_patch14_224,224,6896.46,148.467,1024,5.5,1.7,9.14 +ecaresnet50t,160,6885.36,148.705,1024,25.57,2.21,6.04 +tf_efficientnetv2_b1,240,6876.19,148.891,1024,8.14,1.21,7.34 +efficientvit_b1,256,6866.64,149.111,1024,9.1,0.69,9.46 +selecsls60b,224,6862.29,149.193,1024,32.77,3.63,5.52 +selecsls60,224,6832.72,149.84,1024,30.67,3.59,5.52 +vit_wee_patch16_reg1_gap_256,256,6818.08,150.174,1024,13.42,3.83,13.9 +mobilenetv4_conv_blur_medium,224,6816.69,150.205,1024,9.72,1.22,8.58 +deit3_small_patch16_224,224,6812.85,150.286,1024,22.06,4.61,11.95 +efficientnet_es,224,6810.09,150.348,1024,5.44,1.81,8.73 +mixer_s16_224,224,6808.46,150.387,1024,18.53,3.79,5.97 +efficientnet_blur_b0,224,6806.84,150.42,1024,5.29,0.43,8.72 +tiny_vit_5m_224,224,6795.79,150.652,1024,12.08,1.28,11.25 +repvit_m1_0,224,6784.56,150.903,1024,7.3,1.13,8.69 +regnetx_016,224,6766.08,151.313,1024,9.19,1.62,7.93 +resnet50,176,6703.95,152.718,1024,25.56,2.62,6.92 +convnext_femto,288,6687.36,153.109,1024,5.22,1.3,7.56 +flexivit_small,240,6660.66,153.724,1024,22.06,5.35,14.18 +efficientnet_b0_g16_evos,224,6654.19,153.871,1024,8.11,1.01,7.42 +resmlp_12_224,224,6650.72,153.939,1024,15.35,3.01,5.5 +resnet26,224,6639.65,154.197,1024,16.0,2.36,7.35 +resnet18d,288,6633.67,154.335,1024,11.71,3.41,5.43 +repvit_m2,224,6610.13,154.886,1024,8.8,1.36,9.43 +mobilenetv4_hybrid_medium,256,6587.07,155.438,1024,11.07,1.29,9.01 +convnextv2_atto,224,6572.88,155.773,1024,3.71,0.55,3.81 +resnetrs50,160,6568.89,155.826,1024,35.69,2.29,6.2 +resnext50_32x4d,160,6540.25,156.542,1024,25.03,2.17,7.35 +cs3darknet_m,288,6499.55,157.533,1024,9.31,2.63,6.69 +rexnetr_130,224,6489.24,157.769,1024,7.61,0.68,9.81 +rexnet_130,224,6476.24,158.089,1024,7.56,0.68,9.71 +resnetaa34d,224,6464.72,158.371,1024,21.82,4.43,5.07 +repghostnet_200,224,6438.16,159.023,1024,9.8,0.54,7.96 +convnext_femto_ols,288,6405.94,159.835,1024,5.23,1.35,8.06 +xcit_tiny_12_p16_224,224,6316.09,162.101,1024,6.72,1.24,6.29 +gmixer_12_224,224,6305.79,162.375,1024,12.7,2.67,7.26 +tf_mixnet_s,224,6303.08,162.43,1024,4.13,0.25,6.25 +mobilenetv4_conv_aa_medium,256,6295.03,162.653,1024,9.72,1.58,10.3 +repvit_m1_1,224,6283.35,162.917,1024,8.8,1.36,9.43 +tf_efficientnet_es,224,6275.13,163.154,1024,5.44,1.81,8.73 +efficientnet_b1,224,6258.96,163.587,1024,7.79,0.59,9.36 +efficientnet_es_pruned,224,6256.91,163.63,1024,5.44,1.81,8.73 +efficientnet_b0_g8_gn,224,6255.71,163.672,1024,6.56,0.66,6.75 +convnext_nano,224,6249.99,163.825,1024,15.59,2.46,8.37 +repvgg_b0,224,6210.44,164.855,1024,15.82,3.41,6.15 +hgnetv2_b0,288,6150.46,166.477,1024,6.0,0.54,3.51 +ecaresnet50d_pruned,224,6136.73,166.846,1024,19.94,2.53,6.43 +hgnetv2_b4,224,6125.62,167.15,1024,19.8,2.75,6.7 +tf_efficientnet_lite1,240,6121.06,167.26,1024,5.42,0.62,10.14 +resnet26d,224,6072.87,168.59,1024,16.01,2.6,8.15 +efficientnet_cc_b0_4e,224,6016.7,170.181,1024,13.31,0.41,9.42 +efficientnet_cc_b0_8e,224,6014.04,170.256,1024,24.01,0.42,9.42 +nf_regnet_b1,256,6005.41,170.494,1024,10.22,0.82,7.27 +mobilenetv4_conv_medium,320,5999.7,170.66,1024,9.72,1.71,11.84 +mobilenet_edgetpu_v2_l,224,5960.09,171.794,1024,10.92,2.55,9.05 +vit_relpos_small_patch16_224,224,5956.13,171.907,1024,21.98,4.59,13.05 +edgenext_x_small,288,5955.77,171.92,1024,2.34,0.68,7.5 +regnety_016,224,5939.38,172.381,1024,11.2,1.63,8.04 +darknet17,256,5937.82,172.434,1024,14.3,3.26,7.18 +fbnetv3_d,256,5909.96,173.251,1024,10.31,0.68,11.1 +vit_srelpos_small_patch16_224,224,5903.24,173.446,1024,21.97,4.59,12.16 +nf_resnet26,224,5897.16,173.627,1024,16.0,2.41,7.35 +mobilevitv2_075,256,5875.41,174.271,1024,2.87,1.05,12.06 +nf_regnet_b2,240,5860.87,174.704,1024,14.31,0.97,7.23 +ghostnetv2_130,224,5854.47,174.894,1024,8.96,0.28,5.9 +efficientnet_b2_pruned,260,5817.56,176.002,1024,8.31,0.73,9.13 +vit_base_patch32_plus_256,256,5803.45,176.415,1024,119.48,7.79,7.76 +tiny_vit_11m_224,224,5800.65,176.504,1024,20.35,2.04,13.49 +vit_tiny_patch16_384,384,5746.17,178.189,1024,5.79,4.7,25.39 +gmlp_ti16_224,224,5736.13,178.502,1024,5.87,1.34,7.55 +mobilenet_edgetpu_v2_m,256,5727.79,178.761,1024,8.46,2.42,10.65 +mobilenetv2_120d,224,5720.65,178.972,1024,5.83,0.69,11.97 +vit_relpos_small_patch16_rpn_224,224,5707.01,179.411,1024,21.97,4.59,13.05 +resnetblur18,288,5705.18,179.458,1024,11.69,3.87,5.6 +rexnetr_150,224,5686.03,180.063,1024,9.78,0.89,11.13 +convnext_nano_ols,224,5675.19,180.417,1024,15.65,2.65,9.38 +poolformer_s12,224,5647.45,181.292,1024,11.92,1.82,5.53 +efficientformer_l1,224,5641.9,181.48,1024,12.29,1.3,5.53 +convnextv2_femto,224,5629.97,181.868,1024,5.23,0.79,4.57 +rexnet_150,224,5597.8,182.901,1024,9.73,0.9,11.21 +efficientnet_lite2,260,5526.09,185.282,1024,6.09,0.89,12.9 +efficientnet_b1,240,5515.48,185.64,1024,7.79,0.71,10.88 +darknet21,256,5507.84,185.899,1024,20.86,3.93,7.47 +edgenext_small,256,5504.6,186.01,1024,5.59,1.26,9.07 +mobilenetv4_conv_blur_medium,256,5452.15,140.844,768,9.72,1.59,11.2 +resnext50_32x4d,176,5448.72,187.904,1024,25.03,2.71,8.97 +tf_efficientnet_cc_b0_8e,224,5378.31,190.379,1024,24.01,0.42,9.42 +tf_efficientnet_cc_b0_4e,224,5350.74,191.362,1024,13.31,0.41,9.42 +resnet101,160,5347.38,191.468,1024,44.55,4.0,8.28 +gernet_l,256,5345.88,191.534,1024,31.08,4.57,8.0 +efficientvit_b1,288,5336.48,191.869,1024,9.1,0.87,11.96 +regnetz_005,288,5324.94,192.275,1024,7.12,0.86,9.68 +hgnet_tiny,224,5289.94,193.552,1024,14.74,4.54,6.36 +repvgg_a2,224,5234.66,195.589,1024,28.21,5.7,6.26 +mobilenetv4_conv_large,256,5210.97,196.493,1024,32.59,2.86,12.14 +convnext_pico,288,5203.54,196.772,1024,9.05,2.27,10.08 +cs3darknet_focus_l,256,5201.05,196.865,1024,21.15,4.66,8.03 +vit_relpos_base_patch32_plus_rpn_256,256,5200.74,196.879,1024,119.42,7.68,8.01 +mobilenetv3_large_150d,256,5199.32,196.924,1024,14.62,1.03,12.35 +resnest14d,224,5198.83,196.94,1024,10.61,2.76,7.33 +vit_medium_patch16_clip_224,224,5170.05,198.043,1024,38.59,8.0,15.93 +sedarknet21,256,5136.28,199.326,1024,20.95,3.93,7.47 +tf_efficientnetv2_b2,260,5112.53,200.262,1024,10.1,1.72,9.84 +regnetz_b16,224,5111.47,200.306,1024,9.72,1.45,9.95 +efficientnetv2_rw_t,224,5105.62,200.547,1024,13.65,1.93,9.94 +hgnetv2_b2,224,5071.32,201.903,1024,11.22,1.15,4.12 +edgenext_small_rw,256,5063.53,202.213,1024,7.83,1.58,9.51 +legacy_seresnext26_32x4d,224,5035.51,203.34,1024,16.79,2.49,9.39 +mobilenetv4_hybrid_large_075,256,5015.06,204.168,1024,22.75,2.06,11.64 +ecaresnet101d_pruned,224,5009.62,204.385,1024,24.88,3.48,7.69 +tf_efficientnetv2_b3,240,5006.45,204.508,1024,14.36,1.93,9.95 +crossvit_small_240,240,5004.67,204.591,1024,26.86,5.63,18.17 +convnext_pico_ols,288,4990.08,205.192,1024,9.06,2.37,10.74 +resnext26ts,256,4989.4,205.207,1024,10.3,2.43,10.52 +resnet34,288,4989.09,205.219,1024,21.8,6.07,6.18 +efficientnet_b1,256,4979.81,205.61,1024,7.79,0.77,12.22 +pvt_v2_b1,224,4972.11,205.919,1024,14.01,2.12,15.39 +mixnet_m,224,4961.12,206.387,1024,5.01,0.36,8.19 +dpn48b,224,4946.37,207.002,1024,9.13,1.69,8.92 +sam2_hiera_tiny,224,4939.34,207.285,1024,26.85,4.91,17.12 +nf_ecaresnet26,224,4936.32,207.425,1024,16.0,2.41,7.36 +eca_resnext26ts,256,4936.16,207.428,1024,10.3,2.43,10.52 +mobilevit_xs,256,4915.37,156.229,768,2.32,1.05,16.33 +cs3darknet_l,256,4898.39,209.032,1024,21.16,4.86,8.55 +tf_efficientnet_b1,240,4891.58,209.309,1024,7.79,0.71,10.88 +nf_seresnet26,224,4877.41,209.923,1024,17.4,2.41,7.36 +gcresnext26ts,256,4873.11,210.116,1024,10.48,2.43,10.53 +ecaresnetlight,224,4830.89,211.948,1024,30.16,4.11,8.42 +seresnext26ts,256,4825.46,212.166,1024,10.39,2.43,10.52 +tf_efficientnet_lite2,260,4812.46,212.754,1024,6.09,0.89,12.9 +resnet26t,256,4797.54,213.414,1024,16.01,3.35,10.52 +convnext_tiny,224,4796.99,213.45,1024,28.59,4.47,13.44 +coatnext_nano_rw_224,224,4780.38,214.189,1024,14.7,2.47,12.8 +ecaresnext50t_32x4d,224,4760.92,215.063,1024,15.41,2.7,10.09 +ecaresnext26t_32x4d,224,4760.44,215.086,1024,15.41,2.7,10.09 +ghostnetv2_160,224,4741.68,215.94,1024,12.39,0.42,7.23 +mobileone_s1,224,4716.61,217.083,1024,4.83,0.86,9.67 +efficientnet_b2,256,4713.06,217.249,1024,9.11,0.89,12.81 +ese_vovnet19b_dw,288,4711.0,217.348,1024,6.54,2.22,13.63 +vit_little_patch16_reg1_gap_256,256,4683.45,218.614,1024,22.52,6.27,18.06 +gc_efficientnetv2_rw_t,224,4667.78,219.357,1024,13.68,1.94,9.97 +nf_regnet_b1,288,4666.05,219.44,1024,10.22,1.02,9.2 +vit_small_resnet26d_224,224,4663.1,219.577,1024,63.61,5.07,11.12 +vit_little_patch16_reg4_gap_256,256,4660.7,219.682,1024,22.52,6.35,18.33 +efficientnet_b3_pruned,300,4660.19,219.714,1024,9.86,1.04,11.86 +seresnext26t_32x4d,224,4649.25,220.222,1024,16.81,2.7,10.09 +tf_mixnet_m,224,4635.47,220.876,1024,5.01,0.36,8.19 +crossvit_15_240,240,4634.12,220.945,1024,27.53,5.81,19.77 +vit_small_r26_s32_224,224,4628.86,221.202,1024,36.43,3.56,9.85 +seresnet34,288,4622.08,221.495,1024,21.96,6.07,6.18 +vit_relpos_medium_patch16_cls_224,224,4616.98,221.77,1024,38.76,8.03,18.24 +tresnet_m,224,4615.66,221.824,1024,31.39,5.75,7.31 +deit3_medium_patch16_224,224,4613.13,221.958,1024,38.85,8.0,15.93 +seresnext26d_32x4d,224,4594.08,222.859,1024,16.81,2.73,10.19 +hgnetv2_b1,288,4576.22,223.749,1024,6.34,0.82,4.51 +cs3sedarknet_l,256,4561.19,224.486,1024,21.91,4.86,8.56 +levit_512,224,4507.01,227.185,1024,95.17,5.64,10.22 +nf_regnet_b2,272,4499.96,227.541,1024,14.31,1.22,9.27 +repvit_m3,224,4488.27,228.109,1024,10.68,1.89,13.94 +selecsls84,224,4485.13,228.28,1024,50.95,5.9,7.57 +coatnet_pico_rw_224,224,4472.02,228.953,1024,10.85,2.05,14.62 +resnetv2_50,224,4451.45,230.006,1024,25.55,4.11,11.11 +mobilevitv2_100,256,4424.26,173.574,768,4.9,1.84,16.08 +wide_resnet50_2,176,4418.37,231.739,1024,68.88,7.29,8.97 +hiera_tiny_224,224,4417.32,231.797,1024,27.91,4.91,17.13 +coat_lite_tiny,224,4413.07,232.018,1024,5.72,1.6,11.65 +resnet101,176,4401.33,232.627,1024,44.55,4.92,10.08 +crossvit_15_dagger_240,240,4395.44,232.947,1024,28.21,6.13,20.43 +vgg11,224,4384.0,233.545,1024,132.86,7.61,7.44 +resnet34d,288,4379.98,233.76,1024,21.82,6.47,7.51 +eca_botnext26ts_256,256,4372.52,234.17,1024,10.59,2.46,11.6 +ecaresnet26t,256,4365.68,234.537,1024,16.01,3.35,10.53 +convit_tiny,224,4364.79,234.585,1024,5.71,1.26,7.94 +skresnet34,224,4357.69,234.957,1024,22.28,3.67,5.13 +vovnet39a,224,4329.41,236.505,1024,22.6,7.09,6.73 +convnextv2_pico,224,4303.27,237.943,1024,9.07,1.37,6.1 +eca_halonext26ts,256,4303.05,237.954,1024,10.76,2.44,11.46 +cspresnet50,256,4286.58,238.859,1024,21.62,4.54,11.5 +fbnetv3_g,240,4271.76,239.698,1024,16.62,1.28,14.87 +fastvit_t8,256,4268.46,239.88,1024,4.03,0.7,8.63 +dla60,224,4267.0,239.958,1024,22.04,4.26,10.16 +resnetv2_50t,224,4244.14,241.24,1024,25.57,4.32,11.82 +regnetx_032,224,4235.6,241.712,1024,15.3,3.2,11.37 +hrnet_w18_small_v2,224,4233.72,241.85,1024,15.6,2.62,9.65 +levit_512d,224,4224.89,242.354,1024,92.5,5.85,11.3 +resnet32ts,256,4223.86,242.395,1024,17.96,4.63,11.58 +mobilenetv4_hybrid_medium,320,4223.22,242.451,1024,11.07,2.05,14.36 +resnet50,224,4217.6,242.763,1024,25.56,4.11,11.11 +levit_conv_512,224,4205.6,243.467,1024,95.17,5.64,10.22 +resnetv2_50d,224,4198.97,243.837,1024,25.57,4.35,11.92 +efficientvit_b2,224,4196.18,244.014,1024,24.33,1.6,14.62 +lambda_resnet26t,256,4192.89,244.204,1024,10.96,3.02,11.87 +coat_lite_mini,224,4171.78,245.439,1024,11.01,2.0,12.25 +resnet33ts,256,4170.49,245.507,1024,19.68,4.76,11.66 +regnety_032,224,4166.74,245.728,1024,19.44,3.2,11.26 +botnet26t_256,256,4164.92,245.843,1024,12.49,3.32,11.98 +halonet26t,256,4157.99,246.255,1024,12.48,3.19,11.69 +ese_vovnet39b,224,4150.95,246.672,1024,24.57,7.09,6.74 +eca_vovnet39b,224,4144.28,247.067,1024,22.6,7.09,6.74 +dpn68,224,4139.9,247.329,1024,12.61,2.35,10.47 +hgnetv2_b3,224,4136.91,247.511,1024,16.29,1.78,5.07 +coatnet_nano_cc_224,224,4135.45,247.589,1024,13.76,2.24,15.02 +rexnetr_200,224,4135.03,185.7,768,16.52,1.59,15.11 +rexnet_200,224,4128.12,186.013,768,16.37,1.56,14.91 +vit_relpos_medium_patch16_224,224,4120.0,248.525,1024,38.75,7.97,17.02 +eca_resnet33ts,256,4101.91,249.618,1024,19.68,4.76,11.66 +vit_srelpos_medium_patch16_224,224,4077.53,251.113,1024,38.74,7.96,16.21 +gcresnet33ts,256,4074.93,251.273,1024,19.88,4.76,11.68 +dpn68b,224,4071.04,251.513,1024,12.61,2.35,10.47 +cs3darknet_focus_l,288,4065.11,251.879,1024,21.15,5.9,10.16 +resnet26,288,4059.09,252.243,1024,16.0,3.9,12.15 +seresnet33ts,256,4037.86,253.571,1024,19.78,4.76,11.66 +resnet50t,224,4031.22,253.987,1024,25.57,4.32,11.82 +visformer_small,224,4021.8,254.581,1024,40.22,4.88,11.43 +resnet50d,224,3994.33,256.335,1024,25.58,4.35,11.92 +davit_tiny,224,3979.92,192.952,768,28.36,4.54,18.89 +cspresnet50w,256,3979.02,257.332,1024,28.12,5.04,12.19 +resnetaa34d,288,3962.03,258.426,1024,21.82,7.33,8.38 +resnet50c,224,3951.72,259.096,1024,25.58,4.35,11.92 +resnetv2_50x1_bit,224,3946.63,259.432,1024,25.55,4.23,11.11 +cspresnet50d,256,3928.57,260.626,1024,21.64,4.86,12.55 +efficientnet_b1,288,3916.7,261.423,1024,7.79,0.97,15.46 +resnext26ts,288,3914.08,261.582,1024,10.3,3.07,13.31 +convnext_tiny_hnf,224,3907.17,262.058,1024,28.59,4.47,13.44 +vit_base_resnet26d_224,224,3907.09,262.061,1024,101.4,6.97,13.16 +bat_resnext26ts,256,3900.03,262.512,1024,10.73,2.53,12.51 +resnetaa50,224,3892.01,263.074,1024,25.56,5.15,11.64 +coatnet_nano_rw_224,224,3890.95,263.144,1024,15.14,2.41,15.41 +regnetv_040,224,3888.77,263.292,1024,20.64,4.0,12.29 +vit_relpos_medium_patch16_rpn_224,224,3883.35,263.671,1024,38.73,7.97,17.02 +twins_svt_small,224,3875.45,264.197,1024,24.06,2.94,13.75 +eca_resnext26ts,288,3875.29,264.216,1024,10.3,3.07,13.32 +mobileone_s2,224,3859.75,265.286,1024,7.88,1.34,11.55 +hgnetv2_b4,288,3849.96,265.959,1024,19.8,4.54,11.08 +haloregnetz_b,224,3847.23,266.147,1024,11.68,1.97,11.94 +legacy_seresnet50,224,3845.77,266.241,1024,28.09,3.88,10.6 +cs3darknet_l,288,3845.41,266.274,1024,21.16,6.16,10.83 +efficientnet_cc_b1_8e,240,3844.11,266.369,1024,39.72,0.75,15.44 +tf_efficientnet_em,240,3839.63,266.664,1024,6.9,3.04,14.34 +mobilevit_s,256,3835.87,200.199,768,5.58,2.03,19.94 +levit_conv_512d,224,3830.33,267.323,1024,92.5,5.85,11.3 +vgg11_bn,224,3829.44,267.372,1024,132.87,7.62,7.44 +tf_efficientnet_b2,260,3827.63,267.5,1024,9.11,1.02,13.83 +gcresnext26ts,288,3824.01,267.764,1024,10.48,3.07,13.33 +regnety_040,224,3819.46,268.036,1024,20.65,4.0,12.29 +resnet152,160,3816.3,268.292,1024,60.19,5.9,11.51 +resnetv2_50d_gn,224,3798.52,269.547,1024,25.57,4.38,11.92 +convnext_nano,288,3784.13,270.586,1024,15.59,4.06,13.84 +mixnet_l,224,3783.23,270.648,1024,7.33,0.58,10.84 +ecaresnet50d_pruned,288,3782.37,270.708,1024,19.94,4.19,10.61 +seresnext26ts,288,3781.76,270.745,1024,10.39,3.07,13.32 +resnet50_gn,224,3769.97,271.59,1024,25.56,4.14,11.11 +res2net50_48w_2s,224,3744.75,273.407,1024,25.29,4.18,11.72 +repvit_m1_5,224,3729.62,274.53,1024,14.64,2.31,15.7 +tiny_vit_21m_224,224,3725.54,274.83,1024,33.22,4.29,20.08 +resnest26d,224,3723.36,274.993,1024,17.07,3.64,9.97 +resnet26d,288,3717.52,275.422,1024,16.01,4.29,13.48 +efficientnet_b2,288,3712.61,275.797,1024,9.11,1.12,16.2 +efficientnet_em,240,3709.85,275.987,1024,6.9,3.04,14.34 +vovnet57a,224,3706.22,276.265,1024,36.64,8.95,7.52 +resnetaa50d,224,3694.51,277.138,1024,25.58,5.39,12.44 +resnet50_clip_gap,224,3684.19,277.913,1024,23.53,5.39,12.44 +regnetx_040,224,3677.93,278.37,1024,22.12,3.99,12.2 +convnextv2_atto,288,3666.13,279.278,1024,3.71,0.91,6.3 +inception_v3,299,3663.18,279.511,1024,23.83,5.73,8.97 +hiera_small_224,224,3657.67,279.941,1024,35.01,6.42,20.75 +gcvit_xxtiny,224,3656.12,280.058,1024,12.0,2.14,15.36 +twins_pcpvt_small,224,3652.03,280.363,1024,24.11,3.83,18.08 +seresnet50,224,3649.19,280.58,1024,28.09,4.11,11.13 +resnetblur50,224,3629.01,282.14,1024,25.56,5.16,12.02 +densenet121,224,3614.99,283.247,1024,7.98,2.87,6.9 +vit_medium_patch16_gap_240,240,3607.76,283.806,1024,44.4,9.22,18.81 +ecaresnet50t,224,3581.41,285.899,1024,25.57,4.32,11.83 +cs3sedarknet_l,288,3578.3,286.15,1024,21.91,6.16,10.83 +vit_base_r26_s32_224,224,3567.73,286.987,1024,101.38,6.81,12.36 +mobilenetv4_conv_large,320,3558.21,287.765,1024,32.59,4.47,18.97 +inception_next_tiny,224,3555.09,288.019,1024,28.06,4.19,11.98 +ecaresnet50d,224,3553.36,288.157,1024,25.58,4.35,11.93 +mobileone_s0,224,3552.46,288.227,1024,5.29,1.09,15.48 +coatnet_rmlp_nano_rw_224,224,3537.96,289.405,1024,15.15,2.62,20.34 +tf_mixnet_l,224,3536.96,289.484,1024,7.33,0.58,10.84 +dla60x,224,3535.6,289.605,1024,17.35,3.54,13.8 +ese_vovnet57b,224,3532.21,289.885,1024,38.61,8.95,7.52 +edgenext_small,320,3521.78,290.744,1024,5.59,1.97,14.16 +mobilevitv2_125,256,3514.09,218.532,768,7.48,2.86,20.1 +resnet50_clip,224,3513.39,291.426,1024,38.32,6.14,12.98 +vit_base_patch32_384,384,3507.62,291.907,1024,88.3,13.06,16.5 +nf_regnet_b3,288,3505.93,292.061,1024,18.59,1.67,11.84 +vit_base_patch32_clip_384,384,3505.16,292.11,1024,88.3,13.06,16.5 +hgnetv2_b5,224,3503.99,292.219,1024,39.57,6.56,11.19 +tf_efficientnet_cc_b1_8e,240,3502.1,292.382,1024,39.72,0.75,15.44 +seresnet50t,224,3492.13,293.202,1024,28.1,4.32,11.83 +vit_large_patch32_224,224,3482.36,294.033,1024,305.51,15.39,13.3 +vit_medium_patch16_reg1_gap_256,256,3459.44,295.982,1024,38.88,10.63,22.26 +vit_medium_patch16_reg4_gap_256,256,3450.76,296.726,1024,38.88,10.76,22.6 +resnetblur50d,224,3438.8,297.748,1024,25.58,5.4,12.82 +cs3darknet_focus_x,256,3437.61,297.861,1024,35.02,8.03,10.69 +eca_nfnet_l0,224,3436.69,297.944,1024,24.14,4.35,10.47 +convnext_nano_ols,288,3435.64,298.035,1024,15.65,4.38,15.5 +resnetrs50,224,3434.24,298.144,1024,35.69,4.48,12.14 +resnext50_32x4d,224,3427.06,298.772,1024,25.03,4.26,14.4 +coatnet_0_rw_224,224,3425.88,298.879,1024,27.44,4.43,18.73 +hgnet_small,224,3416.04,299.744,1024,24.36,8.53,8.79 +cspresnext50,256,3409.98,300.268,1024,20.57,4.05,15.86 +dla60_res2net,224,3403.55,300.841,1024,20.85,4.15,12.34 +res2net50_26w_4s,224,3401.73,300.973,1024,25.7,4.28,12.61 +maxvit_pico_rw_256,256,3401.1,225.792,768,7.46,1.83,22.3 +resmlp_24_224,224,3399.81,301.162,1024,30.02,5.96,10.91 +res2net50_14w_8s,224,3398.1,301.29,1024,25.06,4.21,13.28 +maxvit_rmlp_pico_rw_256,256,3396.4,226.105,768,7.52,1.85,24.86 +resnet50s,224,3396.16,301.49,1024,25.68,5.47,13.52 +convnextv2_femto,288,3395.92,301.516,1024,5.23,1.3,7.56 +regnety_040_sgn,224,3390.72,301.971,1024,20.65,4.03,12.29 +xcit_tiny_24_p16_224,224,3375.55,303.331,1024,12.12,2.34,11.82 +efficientvit_b2,256,3369.65,303.868,1024,24.33,2.09,19.03 +nfnet_f0,192,3349.0,305.741,1024,71.49,7.21,10.16 +nfnet_l0,224,3341.81,306.39,1024,35.07,4.36,10.47 +hieradet_small,256,3335.5,230.232,768,34.72,8.51,27.76 +edgenext_base,256,3315.68,308.815,1024,18.51,3.85,15.58 +resnest50d_1s4x24d,224,3309.67,309.367,1024,25.68,4.43,13.57 +efficientnet_lite3,300,3296.2,155.31,512,8.2,1.65,21.85 +dla60_res2next,224,3295.03,310.75,1024,17.03,3.49,13.17 +mobilenetv3_large_150d,320,3287.23,233.606,768,14.62,1.61,19.29 +cs3darknet_x,256,3280.58,312.118,1024,35.05,8.38,11.35 +crossvit_18_240,240,3276.68,312.485,1024,43.27,9.05,26.26 +lambda_resnet26rpt_256,256,3275.72,234.435,768,10.99,3.16,11.87 +resnet32ts,288,3273.65,312.761,1024,17.96,5.86,14.65 +resnext50d_32x4d,224,3267.01,313.407,1024,25.05,4.5,15.2 +densenetblur121d,224,3260.14,314.077,1024,8.0,3.11,7.9 +res2net50d,224,3253.03,314.728,1024,25.72,4.52,13.41 +darknetaa53,256,3249.03,315.15,1024,36.02,7.97,12.39 +edgenext_small_rw,320,3242.29,315.807,1024,7.83,2.46,14.85 +resnet33ts,288,3239.71,316.048,1024,19.68,6.02,14.75 +seresnetaa50d,224,3235.08,316.472,1024,28.11,5.4,12.46 +tf_efficientnetv2_b3,300,3232.69,316.734,1024,14.36,3.04,15.74 +focalnet_tiny_srf,224,3226.46,317.357,1024,28.43,4.42,16.32 +efficientnetv2_rw_t,288,3225.91,317.41,1024,13.65,3.19,16.42 +eva02_small_patch14_224,224,3225.8,317.422,1024,21.62,6.14,18.28 +gcresnext50ts,256,3223.82,317.616,1024,15.67,3.75,15.46 +res2next50,224,3218.65,318.1,1024,24.67,4.2,13.71 +gcresnet50t,256,3218.31,318.16,1024,25.9,5.42,14.67 +gmixer_24_224,224,3213.22,318.663,1024,24.72,5.28,14.45 +eca_resnet33ts,288,3197.58,320.22,1024,19.68,6.02,14.76 +efficientvit_l1,224,3180.29,321.964,1024,52.65,5.27,15.85 +mobileone_s3,224,3179.46,322.047,1024,10.17,1.94,13.85 +repvgg_b1g4,224,3167.88,323.216,1024,39.97,8.15,10.64 +gcresnet33ts,288,3167.19,323.296,1024,19.88,6.02,14.78 +crossvit_18_dagger_240,240,3155.34,324.503,1024,44.27,9.5,27.03 +vit_medium_patch16_gap_256,256,3146.86,325.379,1024,38.86,10.59,22.15 +resnet152,176,3145.18,325.546,1024,60.19,7.22,13.99 +hgnetv2_b2,288,3131.53,326.979,1024,11.22,1.89,6.8 +seresnet33ts,288,3123.88,327.768,1024,19.78,6.02,14.76 +regnetz_b16,288,3121.28,328.043,1024,9.72,2.39,16.43 +legacy_seresnext50_32x4d,224,3106.31,329.614,1024,27.56,4.26,14.42 +nextvit_small,224,3105.38,329.731,1024,31.76,5.81,18.44 +convnextv2_nano,224,3104.37,329.834,1024,15.62,2.46,8.37 +xcit_nano_12_p16_384,384,3100.3,330.26,1024,3.05,1.64,12.15 +regnetz_c16,256,3087.39,331.642,1024,13.46,2.51,16.57 +resnet26t,320,3086.83,331.703,1024,16.01,5.24,16.44 +pit_b_distilled_224,224,3072.82,333.214,1024,74.79,12.5,33.07 +poolformerv2_s12,224,3071.46,333.361,1024,11.89,1.83,5.53 +cs3sedarknet_x,256,3067.22,333.833,1024,35.4,8.38,11.35 +ecaresnet101d_pruned,288,3064.98,334.074,1024,24.88,5.75,12.71 +pit_b_224,224,3054.02,335.268,1024,73.76,12.42,32.94 +eva02_tiny_patch14_336,336,3050.79,335.63,1024,5.76,4.68,27.16 +gc_efficientnetv2_rw_t,288,3042.11,336.591,1024,13.68,3.2,16.45 +resnetrs101,192,3039.94,336.818,1024,63.62,6.04,12.7 +fbnetv3_g,288,3035.76,337.294,1024,16.62,1.77,21.09 +resnet51q,256,3034.29,337.446,1024,35.7,6.38,16.55 +seresnext50_32x4d,224,3033.27,337.56,1024,27.56,4.26,14.42 +rdnet_tiny,224,3025.51,338.425,1024,23.86,5.06,15.98 +darknet53,256,2995.64,341.801,1024,41.61,9.31,12.39 +ecaresnetlight,288,2980.87,343.501,1024,30.16,6.79,13.91 +xcit_small_12_p16_224,224,2979.57,343.65,1024,26.25,4.82,12.58 +nf_ecaresnet50,224,2969.89,344.775,1024,25.56,4.21,11.13 +convnext_small,224,2968.52,344.929,1024,50.22,8.71,21.56 +coatnet_bn_0_rw_224,224,2961.32,345.762,1024,27.44,4.67,22.04 +densenet169,224,2960.88,345.825,1024,14.15,3.4,7.3 +focalnet_tiny_lrf,224,2958.28,346.126,1024,28.65,4.49,17.76 +pvt_v2_b2,224,2957.1,346.245,1024,25.36,4.05,27.53 +nf_seresnet50,224,2955.47,346.454,1024,28.09,4.21,11.13 +fastvit_t12,256,2935.13,348.86,1024,7.55,1.42,12.42 +hgnet_tiny,288,2932.95,349.114,1024,14.74,7.51,10.51 +regnetx_080,224,2928.56,349.629,1024,39.57,8.02,14.06 +vit_base_resnet50d_224,224,2926.61,349.865,1024,110.97,8.73,16.92 +convnext_tiny,288,2917.07,351.019,1024,28.59,7.39,22.21 +mobilevitv2_150,256,2915.63,175.59,512,10.59,4.09,24.11 +coatnet_rmlp_0_rw_224,224,2907.73,352.136,1024,27.45,4.72,24.89 +skresnet50,224,2900.52,353.008,1024,25.8,4.11,12.5 +poolformer_s24,224,2899.97,353.076,1024,21.39,3.41,10.68 +mobilenetv4_hybrid_medium,384,2891.5,354.122,1024,11.07,3.01,21.18 +nf_regnet_b3,320,2880.71,355.451,1024,18.59,2.05,14.61 +tf_efficientnet_lite3,300,2879.81,177.761,512,8.2,1.65,21.85 +resnet50_mlp,256,2876.92,355.906,1024,26.65,7.05,16.25 +sehalonet33ts,256,2863.49,357.576,1024,13.69,3.55,14.7 +gcvit_xtiny,224,2857.49,358.335,1024,19.98,2.93,20.26 +cs3sedarknet_xdw,256,2854.33,358.703,1024,21.6,5.97,17.18 +seresnext26t_32x4d,288,2852.69,358.931,1024,16.81,4.46,16.68 +gmlp_s16_224,224,2850.78,359.18,1024,19.42,4.42,15.1 +deit_base_patch16_224,224,2847.84,359.551,1024,86.57,17.58,23.9 +resnetv2_101,224,2835.29,361.131,1024,44.54,7.83,16.23 +deit_base_distilled_patch16_224,224,2834.0,361.303,1024,87.34,17.68,24.05 +seresnext26d_32x4d,288,2821.4,362.912,1024,16.81,4.51,16.85 +nf_resnet50,256,2816.37,363.566,1024,25.56,5.46,14.52 +regnetx_064,224,2813.74,363.901,1024,26.21,6.49,16.37 +ecaresnet26t,320,2807.22,364.749,1024,16.01,5.24,16.44 +dla102,224,2806.5,364.845,1024,33.27,7.19,14.18 +nest_tiny,224,2805.62,364.963,1024,17.06,5.83,25.48 +ecaresnet50t,256,2802.02,365.426,1024,25.57,5.64,15.45 +efficientnet_b3,288,2792.77,183.313,512,12.23,1.63,21.49 +wide_resnet50_2,224,2792.33,366.693,1024,68.88,11.43,14.4 +vit_base_patch16_224_miil,224,2791.53,366.793,1024,94.4,17.59,23.91 +skresnet50d,224,2791.4,366.81,1024,25.82,4.36,13.31 +vit_base_patch16_224,224,2787.15,367.372,1024,86.57,17.58,23.9 +vit_base_patch16_clip_224,224,2783.55,367.849,1024,86.57,17.58,23.9 +fastvit_sa12,256,2781.99,368.06,1024,11.58,1.96,14.03 +vgg13,224,2780.3,368.277,1024,133.05,11.31,12.25 +resnet61q,256,2779.68,368.358,1024,36.85,7.8,17.01 +fastvit_s12,256,2776.35,368.806,1024,9.47,1.82,13.67 +maxxvit_rmlp_nano_rw_256,256,2775.33,276.707,768,16.78,4.37,26.05 +cs3edgenet_x,256,2763.68,370.501,1024,47.82,11.53,12.92 +lambda_resnet50ts,256,2759.83,371.018,1024,21.54,5.07,17.48 +dm_nfnet_f0,192,2757.16,371.375,1024,71.49,7.21,10.16 +nest_tiny_jx,224,2755.91,371.543,1024,17.06,5.83,25.48 +rexnetr_300,224,2754.02,278.836,768,34.81,3.39,22.16 +cspdarknet53,256,2752.11,372.057,1024,27.64,6.57,16.81 +mixnet_xl,224,2741.75,373.463,1024,11.9,0.93,14.57 +resnet101,224,2736.75,374.138,1024,44.55,7.83,16.23 +resnetv2_50,288,2733.41,374.59,1024,25.55,6.79,18.37 +repvgg_b1,224,2730.49,374.994,1024,57.42,13.16,10.64 +coatnet_0_224,224,2722.37,188.051,512,25.04,4.58,24.01 +resnetv2_101d,224,2718.59,376.633,1024,44.56,8.07,17.04 +wide_resnet101_2,176,2695.47,379.875,1024,126.89,14.31,13.18 +vit_base_mci_224,224,2690.01,380.639,1024,86.35,17.73,24.65 +beitv2_base_patch16_224,224,2684.73,381.393,1024,86.53,17.58,23.9 +res2net50_26w_6s,224,2681.29,381.848,1024,37.05,6.33,15.28 +rexnet_300,224,2677.08,286.849,768,34.71,3.44,22.4 +swin_tiny_patch4_window7_224,224,2659.27,385.037,1024,28.29,4.51,17.06 +maxxvitv2_nano_rw_256,256,2649.05,289.896,768,23.7,6.26,23.05 +beit_base_patch16_224,224,2644.21,387.233,1024,86.53,17.58,23.9 +resnet101d,224,2638.05,388.135,1024,44.57,8.08,17.04 +vit_base_patch32_clip_448,448,2613.19,391.83,1024,88.34,17.93,23.9 +resnet101c,224,2612.47,391.937,1024,44.57,8.08,17.04 +mixer_b16_224,224,2607.54,392.687,1024,59.88,12.62,14.53 +vit_relpos_base_patch16_clsgap_224,224,2605.09,393.051,1024,86.43,17.6,25.12 +efficientnetv2_s,288,2604.83,393.096,1024,21.46,4.75,20.13 +twins_pcpvt_base,224,2604.17,393.184,1024,43.83,6.68,25.25 +efficientvit_b2,288,2600.38,393.768,1024,24.33,2.64,24.03 +convnextv2_pico,288,2600.23,393.79,1024,9.07,2.27,10.08 +vit_relpos_base_patch16_cls_224,224,2599.77,393.861,1024,86.43,17.6,25.12 +efficientformer_l3,224,2594.92,394.593,1024,31.41,3.93,12.01 +resnet50,288,2592.09,395.018,1024,25.56,6.8,18.37 +maxvit_nano_rw_256,256,2589.91,296.516,768,15.45,4.46,30.28 +maxvit_rmlp_nano_rw_256,256,2586.52,296.905,768,15.5,4.47,31.92 +deit3_base_patch16_224,224,2583.38,396.36,1024,86.59,17.58,23.9 +pvt_v2_b2_li,224,2575.01,397.638,1024,22.55,3.91,27.6 +regnety_032,288,2560.26,399.931,1024,19.44,5.29,18.61 +efficientvit_l2,224,2558.08,400.28,1024,63.71,6.97,19.58 +rexnetr_200,288,2556.5,200.242,512,16.52,2.62,24.96 +cs3darknet_x,288,2555.27,400.72,1024,35.05,10.6,14.36 +darknetaa53,288,2551.71,401.279,1024,36.02,10.08,15.68 +tresnet_v2_l,224,2548.5,401.775,1024,46.17,8.85,16.34 +hgnetv2_b3,288,2548.29,401.817,1024,16.29,2.94,8.38 +cs3se_edgenet_x,256,2541.11,402.95,1024,50.72,11.53,12.94 +resnest50d,224,2525.71,405.398,1024,27.48,5.4,14.36 +gcresnext50ts,288,2518.28,406.608,1024,15.67,4.75,19.57 +gcresnet50t,288,2515.86,406.998,1024,25.9,6.86,18.57 +xcit_nano_12_p8_224,224,2506.42,408.525,1024,3.05,2.16,15.71 +vit_small_patch16_384,384,2502.77,409.123,1024,22.2,15.52,50.78 +resnet101_clip_gap,224,2494.59,410.455,1024,42.52,9.11,17.56 +hiera_base_224,224,2493.69,410.614,1024,51.52,9.4,30.42 +resnetaa101d,224,2493.54,410.631,1024,44.57,9.12,17.56 +resnetv2_101x1_bit,224,2493.42,410.646,1024,44.54,8.04,16.23 +davit_small,224,2490.93,308.299,768,49.75,8.8,30.49 +dpn68b,288,2486.84,411.746,1024,12.61,3.89,17.3 +efficientnetv2_rw_s,288,2485.89,411.904,1024,23.94,4.91,21.41 +hrnet_w18_ssld,224,2477.35,413.328,1024,21.3,4.32,16.31 +mobilenetv4_conv_large,384,2476.57,413.456,1024,32.59,6.43,27.31 +cait_xxs24_224,224,2472.37,414.153,1024,11.96,2.53,20.29 +mobilevitv2_175,256,2468.71,207.376,512,14.25,5.54,28.13 +resnet50t,288,2467.58,414.952,1024,25.57,7.14,19.53 +vit_betwixt_patch16_reg1_gap_256,256,2466.98,415.052,1024,60.4,16.32,27.83 +lamhalobotnet50ts_256,256,2464.83,415.423,1024,22.57,5.02,18.44 +hrnet_w18,224,2463.39,415.645,1024,21.3,4.32,16.31 +vit_base_patch16_siglip_gap_224,224,2459.11,416.38,1024,85.8,17.49,23.75 +flexivit_base,240,2458.77,416.449,1024,86.59,20.29,28.36 +vit_betwixt_patch16_reg4_gap_256,256,2454.92,417.093,1024,60.4,16.52,28.24 +legacy_seresnet101,224,2446.63,418.514,1024,49.33,7.61,15.74 +resnet50d,288,2439.85,419.669,1024,25.58,7.19,19.7 +vit_base_patch16_siglip_224,224,2438.72,419.861,1024,92.88,17.73,24.06 +resnet101_clip,224,2414.07,424.148,1024,56.26,9.81,18.08 +tresnet_l,224,2412.56,424.417,1024,55.99,10.9,11.9 +ese_vovnet39b,288,2409.2,318.758,768,24.57,11.71,11.13 +darknet53,288,2407.72,425.266,1024,41.61,11.78,15.68 +cs3sedarknet_x,288,2391.52,428.157,1024,35.4,10.6,14.37 +mixer_l32_224,224,2388.54,428.693,1024,206.94,11.27,19.86 +resnetblur101d,224,2387.51,428.868,1024,44.57,9.12,17.94 +coat_lite_small,224,2386.34,429.081,1024,19.84,3.96,22.09 +regnetv_040,288,2386.15,429.115,1024,20.64,6.6,20.3 +swin_s3_tiny_224,224,2384.47,429.416,1024,28.33,4.64,19.13 +regnety_080,224,2379.83,430.251,1024,39.18,8.0,17.97 +resnetaa50,288,2379.68,430.28,1024,25.56,8.52,19.24 +nextvit_base,224,2376.93,430.787,1024,44.82,8.29,23.71 +vit_base_patch16_gap_224,224,2375.57,431.026,1024,86.57,17.49,25.59 +sebotnet33ts_256,256,2374.22,161.709,384,13.7,3.89,17.46 +resnet101s,224,2366.29,432.714,1024,44.67,9.19,18.64 +convnext_tiny_hnf,288,2365.38,432.876,1024,28.59,7.39,22.21 +mobileone_s4,224,2361.85,433.537,1024,14.95,3.04,17.74 +regnety_040,288,2353.01,435.158,1024,20.65,6.61,20.3 +vit_small_patch16_36x1_224,224,2352.87,435.189,1024,64.67,13.71,35.69 +regnetv_064,224,2352.23,435.302,1024,30.58,6.39,16.41 +vit_relpos_base_patch16_224,224,2344.88,436.673,1024,86.43,17.51,24.97 +seresnet101,224,2343.32,436.959,1024,49.33,7.84,16.27 +dla102x,224,2342.65,437.086,1024,26.31,5.89,19.42 +vit_small_resnet50d_s16_224,224,2336.25,438.285,1024,57.53,13.48,24.82 +ecaresnet101d,224,2325.28,440.352,1024,44.57,8.08,17.07 +regnety_064,224,2324.93,440.376,1024,30.58,6.39,16.41 +volo_d1_224,224,2323.65,440.668,1024,26.63,6.94,24.43 +resnetv2_50d_gn,288,2323.37,440.707,1024,25.57,7.24,19.7 +hiera_small_abswin_256,256,2323.14,440.761,1024,34.36,8.29,26.38 +densenet201,224,2317.12,441.908,1024,20.01,4.34,7.85 +resnet51q,288,2316.81,441.957,1024,35.7,8.07,20.94 +efficientnet_b3,320,2307.8,221.837,512,12.23,2.01,26.52 +resnet50_gn,288,2303.59,444.495,1024,25.56,6.85,18.37 +halonet50ts,256,2300.62,445.077,1024,22.73,5.3,19.2 +nf_resnet101,224,2293.33,446.489,1024,44.55,8.01,16.23 +resnext101_32x8d,176,2291.04,446.931,1024,88.79,10.33,19.37 +maxvit_tiny_rw_224,224,2283.98,336.235,768,29.06,5.11,33.11 +resmlp_36_224,224,2277.0,449.683,1024,44.69,8.91,16.33 +efficientnet_b3_gn,288,2272.8,225.253,512,11.73,1.74,23.35 +vit_base_patch16_clip_quickgelu_224,224,2269.68,451.136,1024,86.19,17.58,23.9 +legacy_xception,299,2267.92,338.617,768,22.86,8.4,35.83 +tf_efficientnet_b3,300,2265.14,226.006,512,12.23,1.87,23.83 +resnetaa50d,288,2259.31,453.205,1024,25.58,8.92,20.57 +vitamin_small_224,224,2252.3,454.619,1024,22.03,5.92,26.38 +vit_medium_patch16_rope_reg1_gap_256,256,2246.04,455.888,1024,38.74,10.63,22.26 +vgg13_bn,224,2243.6,456.38,1024,133.05,11.33,12.25 +sequencer2d_s,224,2241.43,456.822,1024,27.65,4.96,11.31 +seresnet50,288,2233.67,458.373,1024,28.09,6.8,18.39 +efficientvit_b3,224,2229.48,459.279,1024,48.65,3.99,26.9 +repvgg_b2g4,224,2226.86,459.812,1024,61.76,12.63,12.9 +vit_small_patch16_18x2_224,224,2225.09,460.18,1024,64.67,13.71,35.69 +tf_efficientnetv2_s,300,2217.63,461.725,1024,21.46,5.35,22.73 +deit3_small_patch16_384,384,2216.36,461.997,1024,22.21,15.52,50.78 +resnetblur50,288,2215.39,462.19,1024,25.56,8.52,19.87 +res2net101_26w_4s,224,2210.59,463.166,1024,45.21,8.1,18.45 +regnety_080_tv,224,2199.69,465.492,1024,39.38,8.51,19.73 +xcit_tiny_12_p16_384,384,2198.84,465.672,1024,6.72,3.64,18.26 +hgnetv2_b5,288,2192.99,466.916,1024,39.57,10.84,18.5 +resnext101_32x4d,224,2191.44,467.242,1024,44.18,8.01,21.23 +res2net50_26w_8s,224,2190.96,467.347,1024,48.4,8.37,17.95 +ecaresnet50t,288,2189.92,467.571,1024,25.57,7.14,19.55 +vit_relpos_base_patch16_rpn_224,224,2183.57,468.919,1024,86.41,17.51,24.97 +densenet121,288,2181.11,469.465,1024,7.98,4.74,11.41 +ecaresnet50d,288,2174.22,470.95,1024,25.58,7.19,19.72 +pvt_v2_b3,224,2173.14,471.171,1024,45.24,6.92,37.7 +vgg16,224,2165.35,472.876,1024,138.36,15.47,13.56 +vit_base_patch16_xp_224,224,2165.22,472.901,1024,86.51,17.56,23.9 +vit_base_patch16_rpn_224,224,2161.0,473.826,1024,86.54,17.49,23.75 +repvit_m2_3,224,2157.85,474.494,1024,23.69,4.57,26.21 +cs3edgenet_x,288,2155.75,474.984,1024,47.82,14.59,16.36 +mvitv2_tiny,224,2155.16,475.117,1024,24.17,4.7,21.16 +swinv2_cr_tiny_224,224,2153.94,475.376,1024,28.33,4.66,28.45 +nf_resnet50,288,2147.63,476.776,1024,25.56,6.88,18.37 +ese_vovnet99b,224,2146.96,476.932,1024,63.2,16.51,11.27 +mobilevitv2_200,256,2143.14,358.33,768,18.45,7.22,32.15 +res2net101d,224,2140.6,478.302,1024,45.23,8.35,19.25 +seresnet50t,288,2138.78,478.747,1024,28.1,7.14,19.55 +edgenext_base,320,2134.2,479.781,1024,18.51,6.01,24.32 +resnet61q,288,2130.61,480.584,1024,36.85,9.87,21.52 +swinv2_cr_tiny_ns_224,224,2127.09,481.38,1024,28.33,4.66,28.45 +inception_next_small,224,2115.6,484.002,1024,49.37,8.36,19.27 +vit_mediumd_patch16_reg4_gap_256,256,2113.09,484.575,1024,64.11,17.87,37.57 +ese_vovnet39b_evos,224,2111.78,484.876,1024,24.58,7.07,6.74 +rdnet_small,224,2110.32,485.203,1024,50.44,8.74,22.55 +resnext50_32x4d,288,2108.45,485.637,1024,25.03,7.04,23.81 +eca_nfnet_l0,288,2099.14,487.794,1024,24.14,7.12,17.29 +mobilenetv4_hybrid_medium,448,2097.44,366.139,768,11.07,4.2,29.64 +convnext_base,224,2095.37,488.672,1024,88.59,15.38,28.75 +resnetblur50d,288,2095.33,488.675,1024,25.58,8.92,21.19 +regnety_040_sgn,288,2093.1,489.199,1024,20.65,6.67,20.3 +resnet101d,256,2091.32,489.613,1024,44.57,10.55,22.25 +coatnet_rmlp_1_rw_224,224,2085.77,490.917,1024,41.69,7.85,35.47 +xception41p,299,2084.2,245.637,512,26.91,9.25,39.86 +regnetz_040,256,2068.8,494.89,1024,27.12,4.06,24.19 +nf_regnet_b4,320,2065.2,495.816,1024,30.21,3.29,19.88 +regnetz_040_h,256,2063.38,496.243,1024,28.94,4.12,24.29 +efficientvit_l2,256,2060.07,497.046,1024,63.71,9.09,25.49 +resnest50d_4s2x40d,224,2047.78,500.025,1024,30.42,4.4,17.94 +nfnet_l0,288,2047.5,500.093,1024,35.07,7.13,17.29 +vit_base_patch16_reg4_gap_256,256,2044.87,500.737,1024,86.62,23.5,33.89 +regnetz_d32,256,2033.99,503.413,1024,27.58,5.98,23.74 +dpn92,224,2027.88,504.934,1024,37.67,6.54,18.21 +regnetz_d8,256,2018.69,507.232,1024,23.37,3.97,23.74 +resnext50d_32x4d,288,2012.24,508.858,1024,25.05,7.44,25.13 +focalnet_small_srf,224,1995.65,513.095,1024,49.89,8.62,26.26 +hgnet_small,288,1995.54,384.841,768,24.36,14.09,14.53 +vgg19,224,1994.6,513.358,1024,143.67,19.63,14.86 +crossvit_base_240,240,1994.0,513.516,1024,105.03,21.22,36.33 +regnetz_c16,320,1992.12,513.999,1024,13.46,3.92,25.88 +resnetv2_152,224,1991.8,514.076,1024,60.19,11.55,22.56 +mobilenetv4_conv_aa_large,384,1988.94,514.825,1024,32.59,7.07,32.29 +hiera_base_plus_224,224,1983.12,516.333,1024,69.9,12.67,37.98 +regnetx_120,224,1982.95,516.372,1024,46.11,12.13,21.37 +convnextv2_tiny,224,1982.1,516.59,1024,28.64,4.47,13.44 +seresnetaa50d,288,1977.18,517.879,1024,28.11,8.92,20.59 +repvgg_b2,224,1974.36,518.618,1024,89.02,20.45,12.9 +legacy_seresnext101_32x4d,224,1971.37,519.379,1024,48.96,8.02,21.26 +densenetblur121d,288,1970.87,519.545,1024,8.0,5.14,13.06 +convit_small,224,1970.28,519.698,1024,27.78,5.76,17.87 +coatnet_1_rw_224,224,1960.67,522.246,1024,41.72,8.04,34.6 +nfnet_f0,256,1953.19,524.243,1024,71.49,12.62,18.05 +botnet50ts_256,256,1951.79,262.305,512,22.74,5.54,22.23 +poolformer_s36,224,1948.45,525.506,1024,30.86,5.0,15.82 +convmixer_1024_20_ks9_p14,224,1942.47,527.139,1024,24.38,5.55,5.51 +resnet152,224,1941.85,527.301,1024,60.19,11.56,22.56 +skresnext50_32x4d,224,1938.97,528.085,1024,27.48,4.5,17.18 +resnetv2_152d,224,1938.05,528.334,1024,60.2,11.8,23.36 +fastvit_mci0,256,1935.03,529.168,1024,11.41,2.42,18.29 +inception_v4,299,1934.57,529.276,1024,42.68,12.28,15.09 +seresnext101_32x4d,224,1925.49,531.724,1024,48.96,8.02,21.26 +nextvit_large,224,1924.7,532.009,1024,57.87,10.78,28.99 +halo2botnet50ts_256,256,1915.33,534.611,1024,22.64,5.02,21.78 +coatnet_rmlp_1_rw2_224,224,1914.3,534.892,1024,41.72,8.11,40.13 +vgg16_bn,224,1909.7,536.182,1024,138.37,15.5,13.56 +resnetv2_50d_frn,224,1907.53,536.788,1024,25.59,4.33,11.92 +twins_svt_base,224,1904.0,537.783,1024,56.07,8.59,26.33 +gcvit_tiny,224,1903.79,537.854,1024,28.22,4.79,29.82 +vit_base_patch16_siglip_gap_256,256,1894.78,540.402,1024,85.84,23.13,33.23 +dla169,224,1893.96,540.641,1024,53.39,11.6,20.2 +convnextv2_nano,288,1893.91,405.49,768,15.62,4.06,13.84 +resnet152d,224,1891.46,541.352,1024,60.21,11.8,23.36 +efficientnet_el,300,1884.76,543.277,1024,10.59,8.0,30.7 +resnet152c,224,1882.54,543.916,1024,60.21,11.8,23.36 +twins_pcpvt_large,224,1881.08,544.338,1024,60.99,9.84,35.82 +nf_ecaresnet101,224,1877.87,545.282,1024,44.55,8.01,16.27 +vit_base_patch16_siglip_256,256,1875.34,546.002,1024,92.93,23.44,33.63 +maxvit_tiny_tf_224,224,1873.59,409.888,768,30.92,5.6,35.78 +maxxvit_rmlp_tiny_rw_256,256,1870.47,410.571,768,29.64,6.66,39.76 +efficientnet_el_pruned,300,1867.55,548.285,1024,10.59,8.0,30.7 +efficientnet_b3_gn,320,1866.95,205.663,384,11.73,2.14,28.83 +seresnext50_32x4d,288,1856.61,551.512,1024,27.56,7.04,23.82 +regnetz_b16_evos,224,1854.56,552.123,1024,9.74,1.43,9.95 +nf_seresnet101,224,1848.05,554.073,1024,49.33,8.02,16.27 +repvgg_b3g4,224,1843.59,555.409,1024,83.83,17.89,15.1 +regnety_120,224,1840.57,556.321,1024,51.82,12.14,21.38 +mobilenetv4_conv_large,448,1830.25,419.592,768,32.59,8.75,37.17 +focalnet_small_lrf,224,1830.02,559.535,1024,50.34,8.74,28.61 +caformer_s18,224,1825.4,560.94,1024,26.34,4.13,19.39 +nest_small,224,1821.81,562.055,1024,38.35,10.35,40.04 +efficientnet_b3_g8_gn,288,1817.33,422.578,768,14.25,2.59,23.35 +densenet161,224,1806.94,566.679,1024,28.68,7.79,11.06 +tresnet_xl,224,1805.47,567.135,1024,78.44,15.2,15.34 +convnext_small,288,1801.22,568.48,1024,50.22,14.39,35.65 +nest_small_jx,224,1800.35,568.755,1024,38.35,10.35,40.04 +ecaresnet50t,320,1797.24,569.733,1024,25.57,8.82,24.13 +vit_small_patch8_224,224,1790.17,571.984,1024,21.67,22.44,80.84 +vit_large_r50_s32_224,224,1790.0,572.042,1024,328.99,19.58,24.41 +efficientvit_b3,256,1789.45,429.156,768,48.65,5.2,35.01 +davit_base,224,1784.6,430.324,768,87.95,15.51,40.66 +tf_efficientnet_el,300,1777.69,576.0,1024,10.59,8.0,30.7 +vit_base_patch16_plus_240,240,1770.19,578.441,1024,117.56,27.41,33.08 +maxvit_tiny_rw_256,256,1767.83,434.406,768,29.07,6.74,44.35 +maxvit_rmlp_tiny_rw_256,256,1764.91,435.129,768,29.15,6.77,46.92 +sequencer2d_m,224,1760.97,581.467,1024,38.31,6.55,14.26 +xception41,299,1760.86,290.748,512,26.97,9.28,39.86 +resnet152s,224,1745.67,586.565,1024,60.32,12.92,24.96 +efficientnet_b4,320,1740.06,294.222,512,19.34,3.13,34.76 +coatnet_1_224,224,1739.09,294.386,512,42.23,8.7,39.0 +resnetv2_101,288,1737.4,589.353,1024,44.54,12.94,26.83 +hrnet_w30,224,1723.75,593.993,1024,37.71,8.15,21.21 +mixnet_xxl,224,1723.5,445.584,768,23.96,2.04,23.43 +convformer_s18,224,1723.21,594.213,1024,26.77,3.96,15.82 +legacy_seresnet152,224,1717.65,596.143,1024,66.82,11.33,22.08 +regnetx_160,224,1711.44,598.295,1024,54.28,15.99,25.52 +resnetv2_50d_evos,224,1709.91,598.829,1024,25.59,4.33,11.92 +rexnetr_300,288,1708.11,299.718,512,34.81,5.59,36.61 +mobilenetv4_hybrid_large,384,1707.76,599.592,1024,37.76,7.77,34.52 +eva02_base_patch16_clip_224,224,1707.16,599.804,1024,86.26,17.62,26.32 +wide_resnet50_2,288,1707.11,599.82,1024,68.88,18.89,23.81 +mvitv2_small_cls,224,1705.56,600.369,1024,34.87,7.04,28.17 +hrnet_w32,224,1694.43,604.278,1024,41.23,8.97,22.02 +xcit_tiny_12_p8_224,224,1681.45,608.969,1024,6.71,4.81,23.6 +resnet101,288,1679.51,609.673,1024,44.55,12.95,26.83 +tnt_s_patch16_224,224,1675.64,611.078,1024,23.76,5.24,24.37 +wide_resnet101_2,224,1674.87,611.366,1024,126.89,22.8,21.23 +vgg19_bn,224,1663.12,615.681,1024,143.68,19.66,14.86 +cait_xxs36_224,224,1660.72,616.58,1024,17.3,3.77,30.34 +swin_small_patch4_window7_224,224,1658.9,617.245,1024,49.61,8.77,27.47 +seresnet152,224,1654.98,618.709,1024,66.82,11.57,22.61 +vit_betwixt_patch16_rope_reg4_gap_256,256,1646.55,621.878,1024,60.23,16.52,28.24 +convnext_tiny,384,1644.05,311.408,512,28.59,13.14,39.48 +efficientformerv2_s0,224,1630.94,627.828,1024,3.6,0.41,5.3 +cs3se_edgenet_x,320,1622.94,630.928,1024,50.72,18.01,20.21 +mvitv2_small,224,1621.36,631.548,1024,34.87,7.0,28.08 +vit_relpos_base_patch16_plus_240,240,1621.32,631.559,1024,117.38,27.3,34.33 +efficientvit_l2,288,1608.39,636.638,1024,63.71,11.51,32.19 +convnext_base,256,1605.42,637.811,1024,88.59,20.09,37.55 +dm_nfnet_f0,256,1604.65,638.122,1024,71.49,12.62,18.05 +dla102x2,224,1600.16,639.91,1024,41.28,9.34,29.91 +maxvit_tiny_pm_256,256,1598.65,480.383,768,30.09,6.61,47.9 +efficientnet_lite4,380,1596.86,240.45,384,13.01,4.04,45.66 +xcit_small_24_p16_224,224,1593.77,642.475,1024,47.67,9.1,23.64 +samvit_base_patch16_224,224,1591.15,643.539,1024,86.46,17.54,24.54 +vit_small_r26_s32_384,384,1589.79,644.083,1024,36.47,10.43,29.85 +regnety_160,224,1584.47,646.244,1024,83.59,15.96,23.04 +hiera_base_abswin_256,256,1579.74,648.184,1024,51.27,12.46,40.7 +hgnetv2_b6,224,1577.08,649.276,1024,75.26,16.88,21.23 +vit_base_r50_s16_224,224,1574.79,650.212,1024,97.89,21.66,35.28 +poolformerv2_s24,224,1572.19,651.293,1024,21.34,3.42,10.68 +coat_tiny,224,1561.91,655.584,1024,5.5,4.35,27.2 +pvt_v2_b4,224,1560.91,655.996,1024,62.56,10.14,53.74 +pvt_v2_b5,224,1557.23,657.547,1024,81.96,11.76,50.92 +eca_nfnet_l1,256,1553.7,659.05,1024,41.41,9.62,22.04 +repvgg_b3,224,1551.91,659.803,1024,123.09,29.16,15.1 +xception65p,299,1551.33,329.999,512,39.82,13.91,52.48 +swinv2_tiny_window8_256,256,1547.17,661.823,1024,28.35,5.96,24.57 +resnetaa101d,288,1535.29,666.945,1024,44.57,15.07,29.03 +fastvit_sa24,256,1525.62,671.177,1024,21.55,3.8,24.32 +efficientnetv2_s,384,1507.35,679.311,1024,21.46,8.44,35.77 +efficientformerv2_s1,224,1505.34,680.214,1024,6.19,0.67,7.66 +resnet152d,256,1501.71,681.861,1024,60.21,15.41,30.51 +inception_next_base,224,1485.4,689.354,1024,86.67,14.85,25.69 +efficientnet_b3_g8_gn,320,1480.62,518.679,768,14.25,3.2,28.83 +regnety_080,288,1476.82,693.353,1024,39.18,13.22,29.69 +dpn98,224,1476.36,693.573,1024,61.57,11.73,25.2 +mobilenetv4_conv_aa_large,448,1470.08,522.395,768,32.59,9.63,43.94 +hrnet_w18_ssld,288,1462.78,700.012,1024,21.3,7.14,26.96 +resnetblur101d,288,1462.58,700.1,1024,44.57,15.07,29.65 +rdnet_base,224,1460.56,525.796,768,87.45,15.4,31.14 +hgnet_base,224,1454.32,528.06,768,71.58,25.14,15.47 +efficientnetv2_rw_s,384,1440.54,710.82,1024,23.94,8.72,38.03 +nf_regnet_b4,384,1439.96,711.11,1024,30.21,4.7,28.61 +seresnet101,288,1437.85,712.145,1024,49.33,12.95,26.87 +regnetv_064,288,1435.74,713.191,1024,30.58,10.55,27.11 +focalnet_base_srf,224,1425.69,718.219,1024,88.15,15.28,35.01 +eva02_small_patch14_336,336,1424.13,719.007,1024,22.13,15.48,54.33 +ecaresnet101d,288,1423.42,719.366,1024,44.57,13.35,28.19 +regnety_064,288,1420.12,720.99,1024,30.58,10.56,27.11 +tf_efficientnetv2_s,384,1419.64,721.279,1024,21.46,8.44,35.77 +tf_efficientnet_lite4,380,1419.26,270.533,384,13.01,4.04,45.66 +inception_resnet_v2,299,1412.75,724.752,1024,55.84,13.18,25.06 +resnext101_64x4d,224,1412.06,725.153,1024,83.46,15.52,31.21 +crossvit_15_dagger_408,408,1397.53,732.691,1024,28.5,21.45,95.05 +resnext101_32x8d,224,1396.99,732.978,1024,88.79,16.48,31.21 +resnet200,224,1396.79,733.081,1024,64.67,15.07,32.19 +efficientvit_b3,288,1387.6,553.447,768,48.65,6.58,44.2 +resnetrs101,288,1381.07,741.426,1024,63.62,13.56,28.53 +poolformer_m36,224,1377.13,743.544,1024,56.17,8.8,22.02 +maxvit_rmlp_small_rw_224,224,1365.13,562.563,768,64.9,10.75,49.3 +vit_mediumd_patch16_rope_reg1_gap_256,256,1360.17,752.819,1024,63.95,17.65,37.02 +resnext101_32x4d,288,1355.28,755.535,1024,44.18,13.24,35.09 +vit_so150m_patch16_reg4_gap_256,256,1352.83,756.904,1024,134.13,36.75,53.21 +vit_medium_patch16_gap_384,384,1340.12,764.084,1024,39.03,26.08,67.54 +vit_so150m_patch16_reg4_map_256,256,1340.1,764.094,1024,141.48,37.18,53.68 +swinv2_cr_small_224,224,1339.46,764.46,1024,49.7,9.07,50.27 +resnet101d,320,1328.63,770.69,1024,44.57,16.48,34.77 +regnetz_040,320,1328.31,385.424,512,27.12,6.35,37.78 +swinv2_cr_small_ns_224,224,1326.17,772.119,1024,49.7,9.08,50.27 +regnetz_040_h,320,1323.75,386.752,512,28.94,6.43,37.94 +focalnet_base_lrf,224,1318.02,776.898,1024,88.75,15.43,38.13 +eva02_base_patch14_224,224,1315.36,778.472,1024,85.76,23.22,36.55 +xception65,299,1311.64,390.314,512,39.92,13.96,52.48 +vit_base_patch16_rope_reg1_gap_256,256,1310.51,781.347,1024,86.43,23.22,33.39 +nest_base,224,1310.17,781.549,1024,67.72,17.96,53.39 +convnextv2_small,224,1310.01,781.645,1024,50.32,8.71,21.56 +nfnet_f1,224,1306.81,783.562,1024,132.63,17.87,22.94 +efficientnetv2_m,320,1304.81,784.765,1024,54.14,11.01,39.97 +volo_d2_224,224,1303.25,785.708,1024,58.68,14.34,41.34 +coatnet_2_rw_224,224,1301.67,393.319,512,73.87,15.09,49.22 +regnetz_d32,320,1300.21,787.532,1024,27.58,9.33,37.08 +seresnext101_64x4d,224,1298.53,788.558,1024,88.23,15.53,31.25 +nest_base_jx,224,1295.76,790.243,1024,67.72,17.96,53.39 +gmlp_b16_224,224,1289.85,793.862,1024,73.08,15.78,30.21 +mobilevitv2_150,384,1286.35,198.995,256,10.59,9.2,54.25 +hrnet_w40,224,1285.98,796.256,1024,57.56,12.75,25.29 +regnetz_d8,320,1285.82,796.348,1024,23.37,6.19,37.08 +seresnext101_32x8d,224,1284.38,797.243,1024,93.57,16.48,31.25 +seresnet152d,256,1276.56,802.126,1024,66.84,15.42,30.56 +resnetrs152,256,1273.91,803.74,1024,86.62,15.59,30.83 +mobilenetv4_conv_aa_large,480,1272.27,603.62,768,32.59,11.05,50.45 +convnextv2_tiny,288,1271.26,604.097,768,28.64,7.39,22.21 +cait_s24_224,224,1270.01,806.265,1024,46.92,9.35,40.58 +convnext_base,288,1266.87,808.261,1024,88.59,25.43,47.53 +seresnext101d_32x8d,224,1264.56,809.737,1024,93.59,16.72,32.05 +resnest101e,256,1259.59,812.93,1024,48.28,13.38,28.66 +efficientformer_l7,224,1257.98,813.975,1024,82.23,10.17,24.45 +twins_svt_large,224,1250.2,819.039,1024,99.27,15.15,35.1 +maxvit_small_tf_224,224,1245.42,411.087,512,68.93,11.66,53.17 +maxxvit_rmlp_small_rw_256,256,1239.49,619.588,768,66.01,14.67,58.38 +mobilenetv4_hybrid_large,448,1233.31,622.691,768,37.76,10.74,48.61 +resnet50x4_clip_gap,288,1228.45,833.533,1024,65.62,19.57,34.11 +coatnet_rmlp_2_rw_224,224,1227.94,416.935,512,73.88,15.18,54.78 +coat_mini,224,1224.8,836.033,1024,10.34,6.82,33.68 +coatnet_2_224,224,1217.65,420.455,512,74.68,16.5,52.67 +coat_lite_medium,224,1217.51,841.038,1024,44.57,9.81,40.06 +efficientnet_b4,384,1211.17,317.024,384,19.34,4.51,50.04 +swin_base_patch4_window7_224,224,1207.02,848.341,1024,87.77,15.47,36.63 +convnext_large,224,1206.16,848.95,1024,197.77,34.4,43.13 +tresnet_m,448,1204.65,850.01,1024,31.39,22.99,29.21 +mvitv2_base_cls,224,1201.41,852.308,1024,65.44,10.23,40.65 +vit_large_patch32_384,384,1196.88,855.533,1024,306.63,45.31,43.86 +resnet152,288,1192.43,858.72,1024,60.19,19.11,37.28 +seresnext101_32x4d,288,1192.03,859.008,1024,48.96,13.25,35.12 +tiny_vit_21m_384,384,1188.05,646.409,768,21.23,13.77,77.83 +seresnextaa101d_32x8d,224,1183.29,865.35,1024,93.59,17.25,34.16 +resnet50x4_clip,288,1179.84,867.878,1024,87.14,21.35,35.27 +xcit_tiny_24_p16_384,384,1171.17,874.311,1024,12.12,6.87,34.29 +levit_conv_384_s8,224,1166.16,439.026,512,39.12,9.98,35.86 +dm_nfnet_f1,224,1150.17,890.285,1024,132.63,17.87,22.94 +regnetz_e8,256,1147.85,892.072,1024,57.7,9.91,40.94 +swin_s3_small_224,224,1145.1,670.656,768,49.74,9.43,37.84 +mvitv2_base,224,1138.44,899.454,1024,51.47,10.16,40.5 +sequencer2d_l,224,1135.5,901.779,1024,54.3,9.74,22.12 +efficientnetv2_rw_m,320,1134.41,902.651,1024,53.24,12.72,47.14 +gcvit_small,224,1132.43,904.224,1024,51.09,8.57,41.61 +regnety_120,288,1127.97,680.842,768,51.82,20.06,35.34 +hrnet_w44,224,1125.91,909.412,1024,67.06,14.94,26.92 +levit_384_s8,224,1123.29,455.784,512,39.12,9.98,35.86 +regnetz_b16_evos,288,1122.27,684.3,768,9.74,2.36,16.43 +hrnet_w48_ssld,224,1116.96,916.749,1024,77.47,17.34,28.56 +hrnet_w48,224,1113.97,919.215,1024,77.47,17.34,28.56 +regnetz_c16_evos,256,1112.53,690.289,768,13.49,2.48,16.57 +tf_efficientnet_b4,380,1110.38,345.798,384,19.34,4.49,49.49 +xcit_medium_24_p16_224,224,1105.25,926.46,1024,84.4,16.13,31.71 +tnt_b_patch16_224,224,1094.56,935.51,1024,65.41,14.09,39.01 +mobilevitv2_175,384,1091.31,234.563,256,14.25,12.47,63.29 +dpn131,224,1083.25,945.269,1024,79.25,16.09,32.97 +nextvit_small,384,1081.98,946.382,1024,31.76,17.26,57.14 +resnet200d,256,1077.87,949.987,1024,64.69,20.0,43.09 +vit_betwixt_patch16_reg4_gap_384,384,1077.83,950.026,1024,60.6,39.71,85.28 +efficientvit_l3,224,1066.9,719.814,768,246.04,27.62,39.16 +convnextv2_nano,384,1066.76,359.947,384,15.62,7.22,24.61 +maxvit_rmlp_small_rw_256,256,1061.31,723.613,768,64.9,14.15,66.09 +poolformerv2_s36,224,1054.78,970.792,1024,30.79,5.01,15.82 +fastvit_sa36,256,1050.89,974.39,1024,31.53,5.64,34.61 +davit_large,224,1043.66,735.848,768,196.81,34.6,60.99 +convit_base,224,1041.2,983.452,1024,86.54,17.52,31.77 +legacy_senet154,224,1041.02,983.633,1024,115.09,20.77,38.69 +resnetv2_50d_evos,288,1038.94,985.591,1024,25.59,7.15,19.7 +poolformer_m48,224,1037.67,986.794,1024,73.47,11.59,29.17 +vitamin_base_224,224,1035.95,494.209,512,87.72,22.68,52.77 +crossvit_18_dagger_408,408,1032.79,991.461,1024,44.61,32.47,124.87 +fastvit_mci1,256,1032.41,991.824,1024,21.54,4.72,32.84 +maxxvitv2_rmlp_base_rw_224,224,1032.37,743.892,768,116.09,24.2,62.77 +swinv2_base_window12_192,192,1028.07,996.011,1024,109.28,11.9,39.72 +convnext_base,320,1024.52,749.592,768,88.59,31.39,58.68 +xcit_small_12_p16_384,384,1021.49,1002.43,1024,26.25,14.14,36.51 +resnetv2_50x1_bit,448,1019.03,502.406,512,25.55,16.62,44.46 +senet154,224,1016.88,1006.903,1024,115.09,20.77,38.69 +densenet264d,224,1016.07,1007.782,1024,72.74,13.57,14.0 +convnext_small,384,1015.93,755.929,768,50.22,25.58,63.37 +seresnet152,288,1013.71,1010.121,1024,66.82,19.11,37.34 +regnety_320,224,1007.78,1016.066,1024,145.05,32.34,30.26 +dpn107,224,1005.01,1018.86,1024,86.92,18.38,33.46 +xception71,299,1004.0,509.938,512,42.34,18.09,69.92 +hgnetv2_b6,288,981.26,782.643,768,75.26,27.9,35.09 +swinv2_cr_base_224,224,978.09,1046.904,1024,87.88,15.86,59.66 +eca_nfnet_l1,320,976.83,1048.265,1024,41.41,14.92,34.42 +regnety_160,288,976.51,524.284,512,83.59,26.37,38.07 +caformer_s36,224,971.41,1054.103,1024,39.3,8.0,37.53 +swinv2_cr_base_ns_224,224,970.9,1054.655,1024,87.88,15.86,59.66 +mobilenetv4_conv_aa_large,544,968.9,528.415,512,32.59,14.19,64.79 +swinv2_small_window8_256,256,964.83,1061.297,1024,49.73,11.58,40.14 +resnetv2_50x3_bit,224,964.09,796.571,768,217.32,37.06,33.34 +regnetx_320,224,962.49,1063.881,1024,107.81,31.81,36.3 +swinv2_cr_small_ns_256,256,961.42,1065.06,1024,49.7,12.07,76.21 +nf_regnet_b5,384,955.45,803.79,768,49.74,7.95,42.9 +swin_s3_base_224,224,954.89,1072.349,1024,71.13,13.69,48.26 +resnet152d,320,952.68,1074.832,1024,60.21,24.08,47.67 +swinv2_tiny_window16_256,256,949.54,404.378,384,28.35,6.68,39.02 +mobilevitv2_200,384,949.03,269.729,256,18.45,16.24,72.34 +efficientvit_l2,384,946.77,540.762,512,63.71,20.45,57.01 +coat_small,224,946.54,1081.808,1024,21.69,12.61,44.25 +convnextv2_base,224,943.15,814.261,768,88.72,15.38,28.75 +volo_d3_224,224,938.88,1090.634,1024,86.33,20.78,60.09 +ecaresnet200d,256,930.32,1100.668,1024,64.69,20.0,43.15 +vit_mediumd_patch16_reg4_gap_384,384,927.5,1104.011,1024,64.27,43.67,113.51 +deit_base_patch16_384,384,924.35,1107.782,1024,86.86,55.54,101.56 +deit_base_distilled_patch16_384,384,923.14,1109.233,1024,87.63,55.65,101.82 +convnext_large_mlp,256,921.21,833.66,768,200.13,44.94,56.33 +vit_base_patch16_384,384,915.77,1118.154,1024,86.86,55.54,101.56 +convformer_s36,224,915.39,1118.618,1024,40.01,7.67,30.5 +vit_base_patch16_clip_384,384,912.89,1121.683,1024,86.86,55.54,101.56 +vit_large_patch16_224,224,912.7,1121.913,1024,304.33,61.6,63.52 +eva_large_patch14_196,196,906.88,1129.113,1024,304.14,61.57,63.52 +seresnet200d,256,906.73,1129.3,1024,71.86,20.01,43.15 +resnetrs200,256,903.32,1133.498,1024,93.21,20.18,43.42 +hgnet_base,288,882.61,580.058,512,71.58,41.55,25.57 +xcit_tiny_24_p8_224,224,882.26,1160.631,1024,12.11,9.21,45.39 +rdnet_large,224,880.89,581.2,512,186.27,34.74,46.67 +resnext101_64x4d,288,874.91,1170.378,1024,83.46,25.66,51.59 +fastvit_ma36,256,873.15,1172.691,1024,44.07,7.88,41.09 +tf_efficientnetv2_m,384,871.16,1175.422,1024,54.14,15.85,57.52 +beit_large_patch16_224,224,866.7,1181.465,1024,304.43,61.6,63.52 +hrnet_w64,224,866.3,1181.977,1024,128.06,28.97,35.09 +efficientvit_l3,256,859.51,893.502,768,246.04,36.06,50.98 +mixer_l16_224,224,858.48,1192.773,1024,208.2,44.6,41.69 +vit_small_patch14_dinov2,518,855.54,1196.86,1024,22.06,46.76,198.79 +beitv2_large_patch16_224,224,855.13,1197.449,1024,304.43,61.6,63.52 +resnet200,288,849.17,1205.852,1024,64.67,24.91,53.21 +xcit_nano_12_p8_384,384,849.15,1205.887,1024,3.05,6.34,46.08 +nextvit_base,384,845.41,1211.22,1024,44.82,24.64,73.95 +deit3_base_patch16_384,384,844.28,1212.833,1024,86.88,55.54,101.56 +deit3_large_patch16_224,224,842.04,1216.066,1024,304.37,61.6,63.52 +gcvit_base,224,837.14,1223.178,1024,90.32,14.87,55.48 +vit_base_patch16_18x2_224,224,836.78,1223.713,1024,256.73,52.51,71.38 +beit_base_patch16_384,384,834.75,1226.684,1024,86.74,55.54,101.56 +hiera_large_224,224,833.73,1228.187,1024,213.74,40.34,83.37 +maxvit_rmlp_base_rw_224,224,831.5,923.612,768,116.14,23.15,92.64 +vit_small_patch14_reg4_dinov2,518,820.76,1247.592,1024,22.06,46.95,199.77 +seresnet152d,320,810.78,1262.948,1024,66.84,24.09,47.72 +resnetrs152,320,806.91,1269.013,1024,86.62,24.34,48.14 +vit_base_patch16_siglip_gap_384,384,803.81,1273.896,1024,86.09,55.43,101.3 +resnext101_32x16d,224,802.86,1275.414,1024,194.03,36.27,51.18 +volo_d1_384,384,801.94,1276.869,1024,26.78,22.75,108.55 +levit_conv_512_s8,224,796.51,321.385,256,74.05,21.82,52.28 +vit_base_patch16_siglip_384,384,796.23,1286.031,1024,93.18,56.12,102.2 +efficientformerv2_s2,224,793.27,1290.823,1024,12.71,1.27,11.77 +flexivit_large,240,792.48,1292.113,1024,304.36,70.99,75.39 +seresnext101_32x8d,288,790.85,1294.776,1024,93.57,27.24,51.63 +convnext_xlarge,224,789.11,973.216,768,350.2,60.98,57.5 +seresnext101d_32x8d,288,779.13,1314.209,1024,93.59,27.64,52.95 +fastvit_mci2,256,770.69,1328.66,1024,35.82,7.91,43.34 +xcit_small_12_p8_224,224,768.93,1331.684,1024,26.21,18.69,47.21 +efficientnetv2_m,416,757.66,1351.509,1024,54.14,18.6,67.5 +levit_512_s8,224,756.91,338.197,256,74.05,21.82,52.28 +nfnet_f2,256,754.41,1357.331,1024,193.78,33.76,41.85 +poolformerv2_m36,224,753.91,1358.223,1024,56.08,8.81,22.02 +coatnet_rmlp_3_rw_224,224,747.12,342.626,256,165.15,33.56,79.47 +swin_large_patch4_window7_224,224,734.47,1045.628,768,196.53,34.53,54.94 +coatnet_3_rw_224,224,734.18,348.657,256,181.81,33.44,73.83 +coatnet_3_224,224,734.12,348.691,256,166.97,36.56,79.01 +efficientnet_b5,416,732.18,349.614,256,30.39,8.27,80.68 +maxvit_base_tf_224,224,727.37,703.885,512,119.47,24.04,95.01 +seresnextaa101d_32x8d,288,726.54,1409.397,1024,93.59,28.51,56.44 +regnetz_e8,320,726.44,1057.182,768,57.7,15.46,63.94 +convnext_large,288,724.73,706.442,512,197.77,56.87,71.29 +convnextv2_tiny,384,722.62,531.372,384,28.64,13.14,39.48 +ecaresnet200d,288,722.05,1418.16,1024,64.69,25.31,54.59 +regnetz_d8_evos,256,720.34,1421.53,1024,23.46,4.5,24.92 +resnetv2_152x2_bit,224,716.98,1428.18,1024,236.34,46.95,45.11 +seresnet269d,256,714.47,1433.198,1024,113.67,26.59,53.6 +convnext_base,384,714.31,716.749,512,88.59,45.21,84.49 +regnetz_c16_evos,320,711.04,720.042,512,13.49,3.86,25.88 +seresnet200d,288,706.46,1449.448,1024,71.86,25.32,54.6 +caformer_m36,224,705.55,1451.316,1024,56.2,13.29,50.48 +swinv2_base_window8_256,256,704.56,1090.005,768,87.92,20.37,52.59 +davit_huge,224,697.25,734.284,512,348.92,61.23,81.32 +xcit_large_24_p16_224,224,695.71,1471.847,1024,189.1,35.86,47.27 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+swinv2_cr_giant_384,384,23.32,1372.464,32,2598.76,1450.71,1394.86 +vit_so400m_patch14_siglip_gap_896,896,19.9,4824.742,96,416.87,2731.49,8492.88 +samvit_large_patch16,1024,14.97,534.402,8,308.28,1493.86,2553.78 +samvit_huge_patch16,1024,9.99,600.87,6,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt113-cu117-rtx3090.csv b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt113-cu117-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..07b651d18bec42a74381bebb2bbd70b17559a3c7 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt113-cu117-rtx3090.csv @@ -0,0 +1,930 @@ +model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count +tinynet_e,72737.62,14.068,1024,106,0.03,0.69,2.04 +mobilenetv3_small_050,54822.3,18.668,1024,224,0.03,0.92,1.59 +lcnet_035,53629.35,19.084,1024,224,0.03,1.04,1.64 +lcnet_050,45492.41,22.499,1024,224,0.05,1.26,1.88 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+efficientnet_es_pruned,6115.4,167.434,1024,224,1.81,8.73,5.44 +efficientnet_es,6115.12,167.443,1024,224,1.81,8.73,5.44 +visformer_tiny,6103.09,167.772,1024,224,1.27,5.72,10.32 +seresnet34,6058.13,169.019,1024,224,3.67,3.74,21.96 +fbnetv3_b,6018.76,170.124,1024,256,0.55,9.1,8.6 +selecsls42,5953.76,171.98,1024,224,2.94,4.62,30.35 +selecsls42b,5921.2,172.924,1024,224,2.98,4.62,32.46 +resnet26,5895.21,173.69,1024,224,2.36,7.35,16.0 +edgenext_xx_small,5893.72,173.732,1024,288,0.33,4.21,1.33 +levit_384,5880.4,174.126,1024,224,2.36,6.26,39.13 +resnet34d,5865.98,174.555,1024,224,3.91,4.54,21.82 +legacy_seresnet34,5850.24,175.025,1024,224,3.67,3.74,21.96 +dla34,5827.3,175.712,1024,224,3.07,5.02,15.74 +tf_efficientnet_es,5781.29,177.112,1024,224,1.81,8.73,5.44 +cs3darknet_focus_m,5721.39,178.967,1024,288,2.51,6.19,9.3 +resnetblur18,5636.65,181.657,1024,224,2.34,3.39,11.69 +rexnetr_130,5590.0,183.173,1024,224,0.68,9.81,7.61 +mobilevit_xxs,5524.87,185.333,1024,256,0.42,8.34,1.27 +tf_efficientnet_lite1,5524.68,185.339,1024,240,0.62,10.14,5.42 +cs3darknet_m,5478.07,186.916,1024,288,2.63,6.69,9.31 +convnext_atto,5460.54,187.516,1024,288,0.91,6.3,3.7 +xcit_tiny_12_p16_224_dist,5457.72,187.611,1024,224,1.24,6.29,6.72 +xcit_tiny_12_p16_224,5456.63,187.649,1024,224,1.24,6.29,6.72 +skresnet18,5413.1,189.159,1024,224,1.82,3.24,11.96 +darknet17,5401.37,189.571,1024,256,3.26,7.18,14.3 +mixnet_s,5392.58,189.878,1024,224,0.25,6.25,4.13 +resmlp_12_224,5366.15,190.814,1024,224,3.01,5.5,15.35 +resmlp_12_distilled_224,5364.91,190.857,1024,224,3.01,5.5,15.35 +convnext_atto_ols,5288.94,193.6,1024,288,0.96,6.8,3.7 +vit_base_patch32_clip_224,5280.68,193.903,1024,224,4.41,5.01,88.22 +vit_base_patch32_224,5280.52,193.908,1024,224,4.41,5.01,88.22 +pit_xs_distilled_224,5272.13,194.218,1024,224,1.41,7.76,11.0 +pit_xs_224,5271.0,194.259,1024,224,1.4,7.71,10.62 +repvgg_b0,5252.66,194.939,1024,224,3.41,6.15,15.82 +mixer_b32_224,5221.71,196.094,1024,224,3.24,6.29,60.29 +pvt_v2_b0,5210.31,196.521,1024,224,0.57,7.99,3.67 +resnetaa34d,5171.78,197.986,1024,224,4.43,5.07,21.82 +selecsls60,5160.83,198.407,1024,224,3.59,5.52,30.67 +selecsls60b,5119.51,200.008,1024,224,3.63,5.52,32.77 +mobilenetv2_120d,5111.95,200.304,1024,224,0.69,11.97,5.83 +resnet26d,5108.26,200.449,1024,224,2.6,8.15,16.01 +gmixer_12_224,5064.97,202.162,1024,224,2.67,7.26,12.7 +gmlp_ti16_224,5007.93,204.464,1024,224,1.34,7.55,5.87 +mixer_s16_224,4998.69,204.842,1024,224,3.79,5.97,18.53 +tf_mixnet_s,4989.18,205.231,1024,224,0.25,6.25,4.13 +efficientnet_b0_g16_evos,4930.67,207.667,1024,224,1.01,7.42,8.11 +rexnetr_150,4900.22,208.959,1024,224,0.89,11.13,9.78 +fbnetv3_d,4881.14,209.776,1024,256,0.68,11.1,10.31 +darknet21,4850.41,211.105,1024,256,3.93,7.47,20.86 +nf_resnet26,4816.48,212.591,1024,224,2.41,7.35,16.0 +efficientnet_lite2,4781.65,214.14,1024,260,0.89,12.9,6.09 +convnext_femto,4749.12,215.607,1024,288,1.3,7.56,5.22 +tf_efficientnetv2_b2,4718.26,217.018,1024,260,1.72,9.84,10.1 +sedarknet21,4656.51,219.895,1024,256,3.93,7.47,20.95 +dla46x_c,4636.77,220.831,1024,224,0.54,5.66,1.07 +convnext_femto_ols,4618.33,221.714,1024,288,1.35,8.06,5.23 +resnext26ts,4603.25,222.441,1024,256,2.43,10.52,10.3 +efficientformer_l1,4566.14,224.248,1024,224,1.3,5.53,12.29 +dpn48b,4506.78,227.201,1024,224,1.69,8.92,9.13 +crossvit_tiny_240,4481.69,228.473,1024,240,1.57,9.08,7.01 +dla60x_c,4459.27,229.622,1024,224,0.59,6.01,1.32 +eca_resnext26ts,4456.63,229.759,1024,256,2.43,10.52,10.3 +seresnext26ts,4453.99,229.896,1024,256,2.43,10.52,10.39 +legacy_seresnext26_32x4d,4441.15,230.558,1024,224,2.49,9.39,16.79 +gernet_l,4396.56,232.898,1024,256,4.57,8.0,31.08 +mobilevitv2_075,4393.87,233.041,1024,256,1.05,12.06,2.87 +gcresnext26ts,4384.92,233.516,1024,256,2.43,10.53,10.48 +tf_efficientnet_b1,4370.6,234.282,1024,240,0.71,10.88,7.79 +tf_efficientnet_lite2,4293.9,238.467,1024,260,0.89,12.9,6.09 +rexnet_130,4262.16,240.243,1024,224,0.68,9.71,7.56 +efficientnet_b1,4239.44,241.53,1024,256,0.77,12.22,7.79 +vit_small_patch32_384,4239.1,241.55,1024,384,3.45,8.25,22.92 +crossvit_9_240,4212.37,243.082,1024,240,1.85,9.52,8.55 +crossvit_9_dagger_240,4095.03,250.049,1024,240,1.99,9.97,8.78 +nf_ecaresnet26,4091.86,250.24,1024,224,2.41,7.36,16.0 +nf_seresnet26,4088.47,250.449,1024,224,2.41,7.36,17.4 +efficientnet_cc_b0_8e,4076.51,251.183,1024,224,0.42,9.42,24.01 +efficientnet_cc_b0_4e,4073.3,251.382,1024,224,0.41,9.42,13.31 +ecaresnet50d_pruned,4055.39,252.492,1024,224,2.53,6.43,19.94 +efficientnet_b2_pruned,4030.92,254.025,1024,260,0.73,9.13,8.31 +ecaresnext50t_32x4d,4018.73,254.796,1024,224,2.7,10.09,15.41 +ecaresnext26t_32x4d,4017.09,254.9,1024,224,2.7,10.09,15.41 +seresnext26t_32x4d,4014.43,255.069,1024,224,2.7,10.09,16.81 +seresnext26tn_32x4d,4014.36,255.074,1024,224,2.7,10.09,16.81 +repvgg_a2,3987.84,256.77,1024,224,5.7,6.26,28.21 +poolformer_s12,3982.67,257.103,1024,224,1.82,5.53,11.92 +seresnext26d_32x4d,3979.57,257.303,1024,224,2.73,10.19,16.81 +vit_tiny_r_s16_p8_384,3963.05,258.374,1024,384,1.34,6.49,6.36 +resnet26t,3939.46,259.923,1024,256,3.35,10.52,16.01 +nf_regnet_b1,3911.64,261.772,1024,288,1.02,9.2,10.22 +rexnet_150,3881.93,263.775,1024,224,0.9,11.21,9.73 +nf_regnet_b2,3879.78,263.921,1024,272,1.22,9.27,14.31 +resnetv2_50,3865.49,264.896,1024,224,4.11,11.11,25.55 +regnetx_016,3852.41,265.794,1024,224,1.62,7.93,9.19 +tf_efficientnet_cc_b0_4e,3812.08,268.608,1024,224,0.41,9.42,13.31 +tf_efficientnet_cc_b0_8e,3803.67,269.202,1024,224,0.42,9.42,24.01 +convnext_pico,3747.49,273.239,1024,288,2.27,10.08,9.05 +ecaresnetlight,3744.45,273.459,1024,224,4.11,8.42,30.16 +dpn68,3724.59,274.917,1024,224,2.35,10.47,12.61 +edgenext_x_small,3714.71,275.646,1024,288,0.68,7.5,2.34 +gluon_resnet50_v1b,3672.76,278.798,1024,224,4.11,11.11,25.56 +ssl_resnet50,3671.85,278.866,1024,224,4.11,11.11,25.56 +efficientnet_em,3671.25,278.913,1024,240,3.04,14.34,6.9 +resnet50,3668.58,279.116,1024,224,4.11,11.11,25.56 +swsl_resnet50,3668.32,279.136,1024,224,4.11,11.11,25.56 +tv_resnet50,3667.14,279.225,1024,224,4.11,11.11,25.56 +dpn68b,3667.07,279.229,1024,224,2.35,10.47,12.61 +rexnetr_200,3659.45,279.811,1024,224,1.59,15.11,16.52 +convnext_pico_ols,3651.34,280.434,1024,288,2.37,10.74,9.06 +botnet26t_256,3594.28,284.883,1024,256,3.32,11.98,12.49 +bat_resnext26ts,3569.91,286.828,1024,256,2.53,12.51,10.73 +resnetv2_50t,3547.32,288.657,1024,224,4.32,11.82,25.57 +mixnet_m,3537.26,289.477,1024,224,0.36,8.19,5.01 +regnety_016,3531.88,289.919,1024,224,1.63,8.04,11.2 +tf_efficientnet_em,3529.62,290.106,1024,240,3.04,14.34,6.9 +resnetv2_50d,3525.02,290.482,1024,224,4.35,11.92,25.57 +halonet26t,3515.15,291.299,1024,256,3.19,11.69,12.48 +resnet32ts,3492.62,293.179,1024,256,4.63,11.58,17.96 +hrnet_w18_small_v2,3482.81,294.001,1024,224,2.62,9.65,15.6 +gluon_resnet50_v1c,3481.59,294.107,1024,224,4.35,11.92,25.58 +dla60,3466.91,295.351,1024,224,4.26,10.16,22.04 +resnet33ts,3460.78,295.875,1024,256,4.76,11.66,19.68 +tf_efficientnet_b2,3402.3,300.962,1024,260,1.02,13.83,9.11 +convit_tiny,3399.61,301.199,1024,224,1.26,7.94,5.71 +resnet50t,3373.72,303.51,1024,224,4.32,11.82,25.57 +tf_mixnet_m,3366.38,304.167,1024,224,0.36,8.19,5.01 +efficientnet_b3_pruned,3360.1,304.74,1024,300,1.04,11.86,9.86 +seresnet33ts,3354.27,305.27,1024,256,4.76,11.66,19.78 +resnet50d,3351.47,305.527,1024,224,4.35,11.92,25.58 +eca_resnet33ts,3350.95,305.574,1024,256,4.76,11.66,19.68 +vit_small_resnet26d_224,3346.77,305.954,1024,224,5.07,11.12,63.61 +cs3darknet_focus_l,3335.18,307.018,1024,288,5.9,10.16,21.15 +gluon_resnet50_v1d,3334.65,307.068,1024,224,4.35,11.92,25.58 +mobilevitv2_100,3324.63,307.994,1024,256,1.84,16.08,4.9 +vovnet39a,3320.12,308.408,1024,224,7.09,6.73,22.6 +legacy_seresnet50,3312.33,309.135,1024,224,3.88,10.6,28.09 +efficientnet_b0_gn,3307.86,309.554,1024,224,0.42,6.75,5.29 +gcresnet33ts,3307.01,309.633,1024,256,4.76,11.68,19.88 +pit_s_distilled_224,3301.25,310.173,1024,224,2.9,11.64,24.04 +pit_s_224,3299.97,310.295,1024,224,2.88,11.56,23.46 +mobilevit_xs,3252.28,314.844,1024,256,1.05,16.33,2.32 +deit_small_distilled_patch16_224,3233.6,316.663,1024,224,4.63,12.02,22.44 +efficientnet_b2a,3223.97,317.608,1024,288,1.12,16.2,9.11 +efficientnet_b2,3223.9,317.615,1024,288,1.12,16.2,9.11 +deit_small_patch16_224,3218.99,318.1,1024,224,4.61,11.95,22.05 +vit_small_patch16_224,3218.38,318.16,1024,224,4.61,11.95,22.05 +cs3darknet_l,3210.26,318.965,1024,288,6.16,10.83,21.16 +ese_vovnet39b,3206.21,319.369,1024,224,7.09,6.74,24.57 +eca_vovnet39b,3203.77,319.612,1024,224,7.09,6.74,22.6 +convnextv2_atto,3196.73,320.315,1024,288,0.91,6.3,3.71 +coatnet_pico_rw_224,3189.82,321.008,1024,224,2.05,14.62,10.85 +seresnet50,3181.57,321.841,1024,224,4.11,11.13,28.09 +pvt_v2_b1,3147.37,325.339,1024,224,2.12,15.39,14.01 +coat_lite_tiny,3146.41,325.439,1024,224,1.6,11.65,5.72 +res2net50_48w_2s,3127.52,327.404,1024,224,4.18,11.72,25.29 +eca_botnext26ts_256,3112.32,329.003,1024,256,2.46,11.6,10.59 +ecaresnet101d_pruned,3103.16,329.973,1024,224,3.48,7.69,24.88 +efficientnet_b0_g8_gn,3073.2,333.192,1024,224,0.66,6.75,6.56 +ssl_resnext50_32x4d,3071.68,333.356,1024,224,4.26,14.4,25.03 +dla60x,3071.64,333.359,1024,224,3.54,13.8,17.35 +swsl_resnext50_32x4d,3070.7,333.464,1024,224,4.26,14.4,25.03 +tv_resnext50_32x4d,3069.81,333.56,1024,224,4.26,14.4,25.03 +resnext50_32x4d,3069.72,333.57,1024,224,4.26,14.4,25.03 +gluon_resnext50_32x4d,3068.47,333.704,1024,224,4.26,14.4,25.03 +vit_small_r26_s32_224,3061.92,334.417,1024,224,3.56,9.85,36.43 +skresnet34,3055.95,335.073,1024,224,3.67,5.13,22.28 +deit3_small_patch16_224_in21ft1k,3048.82,335.855,1024,224,4.61,11.95,22.06 +deit3_small_patch16_224,3047.23,336.031,1024,224,4.61,11.95,22.06 +eca_halonext26ts,3035.71,337.305,1024,256,2.44,11.46,10.76 +haloregnetz_b,3032.47,337.665,1024,224,1.97,11.94,11.68 +vit_relpos_base_patch32_plus_rpn_256,3026.45,338.338,1024,256,7.68,8.01,119.42 +vit_relpos_small_patch16_rpn_224,3019.95,339.067,1024,224,4.59,13.05,21.97 +vit_relpos_small_patch16_224,3008.26,340.383,1024,224,4.59,13.05,21.98 +vit_srelpos_small_patch16_224,3000.96,341.213,1024,224,4.59,12.16,21.97 +xcit_nano_12_p16_384_dist,3000.48,341.266,1024,384,1.64,12.15,3.05 +cs3sedarknet_l,2995.41,341.845,1024,288,6.16,10.83,21.91 +resnetaa50d,2993.03,342.116,1024,224,5.39,12.44,25.58 +vgg11,2983.47,85.796,256,224,7.61,7.44,132.86 +selecsls84,2973.16,344.402,1024,224,5.9,7.57,50.95 +resnetrs50,2963.42,345.535,1024,224,4.48,12.14,35.69 +seresnet50t,2957.12,346.271,1024,224,4.32,11.83,28.1 +resnest14d,2954.69,346.556,1024,224,2.76,7.33,10.61 +gluon_resnet50_v1s,2953.65,346.677,1024,224,5.47,13.52,25.68 +coat_lite_mini,2952.61,346.799,1024,224,2.0,12.25,11.01 +ecaresnet50d,2945.96,347.583,1024,224,4.35,11.93,25.58 +densenet121,2933.45,349.064,1024,224,2.87,6.9,7.98 +tv_densenet121,2929.69,349.514,1024,224,2.87,6.9,7.98 +vit_base_patch32_plus_256,2929.65,349.519,1024,256,7.79,7.76,119.48 +rexnet_200,2927.94,349.723,1024,224,1.56,14.91,16.37 +xcit_tiny_24_p16_224_dist,2927.0,349.834,1024,224,2.34,11.82,12.12 +xcit_tiny_24_p16_224,2921.97,350.436,1024,224,2.34,11.82,12.12 +coatnet_nano_cc_224,2867.38,357.108,1024,224,2.24,15.02,13.76 +gcresnext50ts,2857.34,358.363,1024,256,3.75,15.46,15.67 +lambda_resnet26rpt_256,2853.55,358.839,1024,256,3.16,11.87,10.99 +resnext50d_32x4d,2845.08,359.908,1024,224,4.5,15.2,25.05 +mixnet_l,2828.6,361.996,1024,224,0.58,10.84,7.33 +densenet121d,2824.08,362.584,1024,224,3.11,7.7,8.0 +efficientnet_lite3,2821.84,362.87,1024,300,1.65,21.85,8.2 +cspresnet50,2793.65,366.534,1024,256,4.54,11.5,21.62 +coatnet_nano_rw_224,2781.93,368.077,1024,224,2.41,15.41,15.14 +vgg11_bn,2760.38,370.949,1024,224,7.62,7.44,132.87 +vovnet57a,2755.77,371.572,1024,224,8.95,7.52,36.64 +resmlp_24_224,2750.33,372.306,1024,224,5.96,10.91,30.02 +resmlp_24_distilled_224,2740.33,373.665,1024,224,5.96,10.91,30.02 +convnextv2_femto,2735.91,374.269,1024,288,1.3,7.56,5.23 +flexivit_small,2735.78,374.287,1024,240,5.35,14.18,22.06 +gcresnet50t,2732.04,374.8,1024,256,5.42,14.67,25.9 +legacy_seresnext50_32x4d,2722.84,376.065,1024,224,4.26,14.42,27.56 +seresnext50_32x4d,2721.47,376.256,1024,224,4.26,14.42,27.56 +gluon_seresnext50_32x4d,2720.58,376.379,1024,224,4.26,14.42,27.56 +visformer_small,2719.93,376.468,1024,224,4.88,11.43,40.22 +twins_svt_small,2713.39,377.374,1024,224,2.94,13.75,24.06 +resnetv2_50x1_bit_distilled,2708.81,378.014,1024,224,4.23,11.11,25.55 +res2net50_14w_8s,2692.9,380.248,1024,224,4.21,13.28,25.06 +resnetblur50,2685.97,381.228,1024,224,5.16,12.02,25.56 +vit_base_resnet26d_224,2684.6,381.421,1024,224,6.97,13.16,101.4 +tf_mixnet_l,2680.8,381.958,1024,224,0.58,10.84,7.33 +seresnetaa50d,2658.93,385.106,1024,224,5.4,12.46,28.11 +dla60_res2net,2656.16,385.506,1024,224,4.15,12.34,20.85 +cspresnet50d,2655.05,385.668,1024,256,4.86,12.55,21.64 +coatnext_nano_rw_224,2655.0,385.674,1024,224,2.47,12.8,14.7 +ese_vovnet57b,2654.33,385.773,1024,224,8.95,7.52,38.61 +tf_efficientnetv2_b3,2654.14,385.8,1024,300,3.04,15.74,14.36 +cspresnet50w,2641.68,387.621,1024,256,5.04,12.19,28.12 +res2net50_26w_4s,2629.64,389.395,1024,224,4.28,12.61,25.7 +regnetz_b16,2626.71,389.828,1024,288,2.39,16.43,9.72 +convnext_nano,2611.78,392.059,1024,288,4.06,13.84,15.59 +efficientnetv2_rw_t,2601.49,393.609,1024,288,3.19,16.42,13.65 +fbnetv3_g,2595.29,394.549,1024,288,1.77,21.09,16.62 +gmixer_24_224,2595.15,394.571,1024,224,5.28,14.45,24.72 +mobilevit_s,2586.09,395.952,1024,256,2.03,19.94,5.58 +coatnet_rmlp_nano_rw_224,2569.7,398.478,1024,224,2.62,20.34,15.15 +gcvit_xxtiny,2561.41,399.768,1024,224,2.14,15.36,12.0 +tf_efficientnet_lite3,2530.94,404.582,1024,300,1.65,21.85,8.2 +efficientnet_cc_b1_8e,2530.65,404.628,1024,240,0.75,15.44,39.72 +densenetblur121d,2522.66,405.908,1024,224,3.11,7.9,8.0 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+swinv2_base_window16_256,360.23,1065.959,384,256,22.02,84.71,87.92 +regnety_160,353.5,2172.566,768,288,26.37,38.07,83.59 +nasnetalarge,345.63,1111.004,384,331,23.89,90.56,88.75 +maxvit_tiny_tf_384,344.01,744.157,256,384,17.53,123.42,30.98 +xcit_small_24_p8_224,342.37,2990.915,1024,224,35.81,90.78,47.63 +xcit_small_24_p8_224_dist,342.26,2991.817,1024,224,35.81,90.78,47.63 +flexivit_large,335.35,3053.52,1024,240,70.99,75.39,304.36 +maxxvitv2_rmlp_large_rw_224,332.33,3081.271,1024,224,44.14,87.15,215.42 +vit_large_r50_s32_384,329.8,3104.921,1024,384,57.43,76.52,329.09 +pnasnet5large,328.89,1167.534,384,331,25.04,92.89,86.06 +tresnet_m_448,325.8,3143.01,1024,448,22.94,29.21,31.39 +volo_d1_384,323.04,1584.906,512,384,22.75,108.55,26.78 +volo_d4_224,318.96,3210.439,1024,224,44.34,80.22,192.96 +xcit_medium_24_p16_384_dist,312.74,3274.268,1024,384,47.39,91.64,84.4 +nfnet_f2,310.6,3296.869,1024,352,63.22,79.06,193.78 +vit_base_patch16_384,307.09,1250.42,384,384,55.54,101.56,86.86 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+tf_efficientnet_b6,274.07,700.545,192,528,19.4,167.39,43.04 +dm_nfnet_f2,264.79,2900.408,768,352,63.22,79.06,193.78 +beit_base_patch16_384,261.27,1469.733,384,384,55.54,101.56,86.74 +efficientnetv2_l,260.33,1966.694,512,480,56.4,157.99,118.52 +swinv2_cr_small_384,259.75,985.56,256,384,29.7,298.03,49.7 +tf_efficientnetv2_l,257.29,1989.923,512,480,56.4,157.99,118.52 +resnest200e,254.36,1006.453,256,320,35.69,82.78,70.2 +mvitv2_large,249.99,2048.061,512,224,43.87,112.02,217.99 +xcit_tiny_24_p8_384_dist,248.25,4124.916,1024,384,27.05,132.95,12.11 +convnext_xlarge,242.63,2110.182,512,288,100.8,95.05,350.2 +resmlp_big_24_224_in22ft1k,241.9,4233.056,1024,224,100.23,87.31,129.14 +resmlp_big_24_224,241.74,4235.988,1024,224,100.23,87.31,129.14 +resmlp_big_24_distilled_224,241.44,4241.249,1024,224,100.23,87.31,129.14 +convnextv2_large,239.52,1068.782,256,288,56.87,71.29,197.96 +coatnet_4_224,238.62,1072.827,256,224,62.48,129.26,275.43 +swin_base_patch4_window12_384,236.12,813.144,192,384,47.19,134.78,87.9 +xcit_medium_24_p8_224_dist,233.5,3289.007,768,224,63.53,121.23,84.32 +xcit_medium_24_p8_224,233.5,3289.104,768,224,63.53,121.23,84.32 +eca_nfnet_l3,229.87,2227.284,512,448,52.55,118.4,72.04 +vit_base_r50_s16_384,226.32,1696.687,384,384,67.43,135.03,98.95 +maxvit_small_tf_384,224.01,857.105,192,384,35.87,183.65,69.02 +xcit_small_12_p8_384_dist,221.54,1733.28,384,384,54.92,138.29,26.21 +swinv2_large_window12to16_192to256_22kft1k,220.1,1163.101,256,256,47.81,121.53,196.74 +volo_d5_224,210.88,4855.76,1024,224,72.4,118.11,295.46 +vit_base_patch8_224,199.67,1282.079,256,224,78.22,161.69,86.58 +cait_xs24_384,197.64,3885.811,768,384,19.28,183.98,26.67 +resnetrs350,196.19,5219.377,1024,384,77.59,154.74,163.96 +cait_xxs36_384,188.27,5439.03,1024,384,14.35,183.7,17.37 +swinv2_cr_base_384,185.68,1378.725,256,384,50.57,333.68,87.88 +coatnet_rmlp_2_rw_384,184.84,1038.746,192,384,47.69,209.43,73.88 +swinv2_cr_huge_224,184.09,2085.934,384,224,115.97,121.08,657.83 +convnext_xxlarge,183.68,2787.486,512,224,151.66,95.29,846.47 +volo_d2_384,180.56,2126.753,384,384,46.17,184.51,58.87 +xcit_large_24_p16_384_dist,176.39,5805.281,1024,384,105.35,137.17,189.1 +regnety_640,174.81,4393.396,768,224,64.16,42.5,281.38 +maxvit_xlarge_tf_224,171.63,1491.6,256,224,97.49,191.02,474.95 +nfnet_f3,170.11,4514.791,768,416,115.58,141.78,254.92 +densenet264d_iabn,167.13,6126.84,1024,224,13.47,14.0,72.74 +efficientnet_b7,166.38,1153.975,192,600,38.33,289.94,66.35 +maxvit_tiny_tf_512,163.72,781.809,128,512,33.49,257.59,31.05 +efficientnetv2_xl,162.7,3146.865,512,512,93.85,247.32,208.12 +tf_efficientnetv2_xl,161.32,3173.821,512,512,93.85,247.32,208.12 +tf_efficientnet_b7,160.43,1196.798,192,600,38.33,289.94,66.35 +resnetv2_152x2_bit_teacher_384,159.54,1604.579,256,384,136.16,132.56,236.34 +tresnet_l_448,154.66,6620.743,1024,448,43.5,47.56,55.99 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+resnest269e,119.17,2148.198,256,416,77.69,171.98,110.93 +resnetv2_152x2_bitm,117.29,2182.534,256,448,184.99,180.43,236.34 +xcit_small_24_p8_384_dist,116.59,3293.649,384,384,105.24,265.91,47.63 +tresnet_xl_448,115.63,8855.938,1024,448,60.65,61.31,78.44 +swinv2_cr_large_384,113.43,1128.479,128,384,108.95,404.96,196.68 +maxvit_small_tf_512,106.82,1198.298,128,512,67.26,383.77,69.13 +efficientnet_b8,106.21,1205.18,128,672,63.48,442.89,87.41 +tf_efficientnet_b8,102.86,1244.358,128,672,63.48,442.89,87.41 +eva_large_patch14_336,102.71,2492.371,256,336,191.1,270.24,304.53 +vit_large_patch14_clip_336,102.52,2496.99,256,336,191.11,270.24,304.53 +vit_large_patch16_384,102.5,2497.593,256,384,191.21,270.24,304.72 +cait_s36_384,101.88,5025.316,512,384,47.99,367.4,68.37 +eva_giant_patch14_224,101.84,10055.112,1024,224,267.18,192.64,1012.56 +vit_giant_patch14_224,100.71,7625.752,768,224,267.18,192.64,1012.61 +vit_giant_patch14_clip_224,100.43,7646.856,768,224,267.18,192.64,1012.65 +deit3_large_patch16_384_in21ft1k,99.81,2564.809,256,384,191.21,270.24,304.76 +deit3_large_patch16_384,99.8,2564.994,256,384,191.21,270.24,304.76 +swinv2_base_window12to24_192to384_22kft1k,96.12,665.832,64,384,55.25,280.36,87.92 +nfnet_f4,89.33,5731.574,512,512,216.26,262.26,316.07 +beit_large_patch16_384,88.56,2890.58,256,384,191.21,270.24,305.0 +maxvit_large_tf_384,86.44,1480.84,128,384,132.55,445.84,212.03 +regnety_1280,82.49,4654.845,384,224,127.66,71.58,644.81 +xcit_medium_24_p8_384_dist,79.96,3201.705,256,384,186.67,354.73,84.32 +resnetv2_101x3_bitm,79.41,2417.67,192,448,280.33,194.78,387.93 +volo_d3_448,77.64,2473.021,192,448,96.33,446.83,86.63 +dm_nfnet_f4,77.54,4952.036,384,512,216.26,262.26,316.07 +nfnet_f5,67.46,5691.915,384,544,290.97,349.71,377.21 +tf_efficientnet_l2,63.66,1507.989,96,475,172.11,609.89,480.31 +swinv2_large_window12to24_192to384_22kft1k,60.94,787.651,48,384,116.15,407.83,196.74 +vit_gigantic_patch14_224,60.18,8507.121,512,224,483.95,275.37,1844.44 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+maxvit_large_tf_512,41.04,1559.597,64,512,244.75,942.15,212.33 +eva_giant_patch14_336,39.89,6418.269,256,336,620.64,550.67,1013.01 +volo_d5_448,39.88,3209.812,128,448,315.06,737.92,295.91 +beit_large_patch16_512,35.33,2716.953,96,512,362.24,656.39,305.67 +cait_m36_384,32.89,7783.487,256,384,173.11,734.81,271.22 +resnetv2_152x4_bitm,30.46,3151.929,96,480,844.84,414.26,936.53 +volo_d5_512,27.89,4590.0,128,512,425.09,1105.37,296.09 +maxvit_xlarge_tf_512,24.38,1968.424,48,512,534.14,1413.22,475.77 +efficientnet_l2,23.13,1383.428,32,800,479.12,1707.39,480.31 +swinv2_cr_giant_384,15.06,2124.735,32,384,1450.71,1394.86,2598.76 +cait_m48_448,13.86,9235.876,128,448,329.41,1708.23,356.46 +eva_giant_patch14_560,10.52,3043.009,32,560,1906.76,2577.17,1014.45 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt210-cu121-rtx3090.csv b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt210-cu121-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..de1fe6a71f9888eace03f5c00ebde940a70433e0 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt210-cu121-rtx3090.csv @@ -0,0 +1,1205 @@ +model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count +tinynet_e,106,1024.0,75290.96,13.591,0.03,0.69,2.04 +mobilenetv3_small_050,224,1024.0,56785.93,18.023,0.03,0.92,1.59 +efficientvit_m0,224,1024.0,50656.23,20.205,0.08,0.91,2.35 +lcnet_035,224,1024.0,48853.22,20.951,0.03,1.04,1.64 +lcnet_050,224,1024.0,42147.98,24.285,0.05,1.26,1.88 +mobilenetv3_small_075,224,1024.0,42002.46,24.369,0.05,1.3,2.04 +mobilenetv3_small_100,224,1024.0,38516.23,26.573,0.06,1.42,2.54 +tinynet_d,152,1024.0,37989.71,26.944,0.05,1.42,2.34 +efficientvit_m1,224,1024.0,37486.44,27.306,0.17,1.33,2.98 +tf_mobilenetv3_small_minimal_100,224,1024.0,33948.13,30.153,0.06,1.41,2.04 +efficientvit_m2,224,1024.0,33551.67,30.51,0.2,1.47,4.19 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+focalnet_xlarge_fl3,384,192.0,65.92,2912.463,185.61,223.99,408.79 +maxvit_xlarge_tf_384,384,96.0,64.9,1479.208,283.86,498.45,475.32 +focalnet_xlarge_fl4,384,192.0,63.63,3017.361,185.79,242.31,409.03 +beit_large_patch16_512,512,256.0,61.48,4163.85,310.6,227.76,305.67 +volo_d4_448,448,192.0,60.99,3147.895,197.13,527.35,193.41 +regnety_640,384,192.0,60.97,3149.012,188.47,124.83,281.38 +convnextv2_huge,384,96.0,60.92,1575.922,337.96,232.35,660.29 +swinv2_large_window12to24_192to384,384,48.0,60.75,790.151,116.15,407.83,196.74 +eva02_large_patch14_448,448,512.0,59.67,8581.221,310.69,261.32,305.08 +dm_nfnet_f5,544,384.0,58.35,6580.773,290.97,349.71,377.21 +vit_huge_patch14_clip_378,378,512.0,58.14,8806.389,460.13,270.04,632.68 +convmixer_1536_20,224,1024.0,56.99,17967.01,48.68,33.03,51.63 +vit_large_patch14_dinov2,518,384.0,56.83,6757.154,414.89,304.42,304.37 +vit_large_patch14_reg4_dinov2,518,384.0,56.64,6779.944,416.1,305.31,304.37 +maxvit_large_tf_512,512,64.0,54.68,1170.494,225.96,611.85,212.33 +tf_efficientnet_l2,475,96.0,54.05,1776.14,172.11,609.89,480.31 +vit_huge_patch14_clip_quickgelu_378,378,384.0,53.95,7117.573,460.13,270.04,632.68 +vit_huge_patch16_gap_448,448,512.0,52.86,9685.108,494.35,290.02,631.67 +nfnet_f6,576,384.0,52.55,7307.184,378.69,452.2,438.36 +swinv2_cr_giant_224,224,192.0,52.45,3660.551,483.85,309.15,2598.76 +eva_giant_patch14_336,336,512.0,49.65,10312.606,583.14,305.1,1013.01 +swinv2_cr_huge_384,384,96.0,49.62,1934.539,352.04,583.18,657.94 +xcit_large_24_p8_384,384,192.0,45.19,4249.177,415.0,531.74,188.93 +dm_nfnet_f6,576,256.0,44.83,5710.109,378.69,452.2,438.36 +volo_d5_448,448,192.0,42.49,4518.905,315.06,737.92,295.91 +nfnet_f7,608,256.0,41.52,6165.283,480.39,570.85,499.5 +cait_m36_384,384,256.0,33.1,7733.448,173.11,734.79,271.22 +resnetv2_152x4_bit,480,96.0,32.12,2989.13,844.84,414.26,936.53 +maxvit_xlarge_tf_512,512,48.0,30.41,1578.222,505.95,917.77,475.77 +regnety_2560,384,128.0,30.25,4231.43,747.83,296.49,1282.6 +volo_d5_512,512,128.0,29.54,4332.489,425.09,1105.37,296.09 +samvit_base_patch16,1024,16.0,23.81,671.88,371.55,403.08,89.67 +regnety_1280,384,128.0,22.93,5583.053,374.99,210.2,644.81 +efficientnet_l2,800,48.0,19.03,2521.932,479.12,1707.39,480.31 +vit_giant_patch14_dinov2,518,192.0,17.15,11193.542,1553.56,871.89,1136.48 +vit_giant_patch14_reg4_dinov2,518,192.0,17.12,11212.072,1558.09,874.43,1136.48 +swinv2_cr_giant_384,384,32.0,15.04,2127.877,1450.71,1394.86,2598.76 +eva_giant_patch14_560,560,192.0,15.03,12771.913,1618.04,846.56,1014.45 +cait_m48_448,448,128.0,13.96,9172.063,329.4,1708.21,356.46 +samvit_large_patch16,1024,12.0,10.64,1127.934,1317.08,1055.58,308.28 +samvit_huge_patch16,1024,8.0,6.61,1210.638,2741.59,1727.57,637.03 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt240-cu124-rtx3090.csv b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt240-cu124-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..82f713e609739135ce639da5b444b0c94dc5f591 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt240-cu124-rtx3090.csv @@ -0,0 +1,1431 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_efficientnet,160,103646.76,9.87,1024,0.36,0.06,0.55 +test_vit,160,103244.27,9.909,1024,0.37,0.04,0.48 +test_byobnet,160,95063.35,10.761,1024,0.46,0.03,0.43 +tinynet_e,106,76700.93,13.341,1024,2.04,0.03,0.69 +mobilenetv3_small_050,224,57072.67,17.933,1024,1.59,0.03,0.92 +lcnet_035,224,55976.14,18.284,1024,1.64,0.03,1.04 +efficientvit_m0,224,48377.06,21.157,1024,2.35,0.08,0.91 +lcnet_050,224,47344.86,21.619,1024,1.88,0.05,1.26 +mobilenetv3_small_075,224,42488.72,24.091,1024,2.04,0.05,1.3 +mobilenetv3_small_100,224,39045.83,26.216,1024,2.54,0.06,1.42 +tinynet_d,152,38744.45,26.42,1024,2.34,0.05,1.42 +efficientvit_m1,224,37693.15,27.157,1024,2.98,0.17,1.33 +tf_mobilenetv3_small_minimal_100,224,34512.3,29.661,1024,2.04,0.06,1.41 +efficientvit_m2,224,33886.66,30.209,1024,4.19,0.2,1.47 +tf_mobilenetv3_small_075,224,33704.54,30.372,1024,2.04,0.05,1.3 +tf_mobilenetv3_small_100,224,31434.86,32.566,1024,2.54,0.06,1.42 +lcnet_075,224,31075.1,32.942,1024,2.36,0.1,1.99 +mobilenetv4_conv_small,224,30965.44,33.059,1024,3.77,0.19,1.97 +efficientvit_m3,224,29709.67,34.458,1024,6.9,0.27,1.62 +efficientvit_m4,224,28086.43,36.449,1024,8.8,0.3,1.7 +mnasnet_small,224,26858.45,38.112,1024,2.03,0.07,2.16 +regnetx_002,224,24932.59,41.06,1024,2.68,0.2,2.16 +lcnet_100,224,24627.72,41.57,1024,2.95,0.16,2.52 +mobilenetv4_conv_small,256,23522.69,43.523,1024,3.77,0.25,2.57 +regnety_002,224,23069.71,44.375,1024,3.16,0.2,2.17 +levit_128s,224,23026.39,44.461,1024,7.78,0.31,1.88 +repghostnet_050,224,22877.69,44.75,1024,2.31,0.05,2.02 +ghostnet_050,224,22787.91,44.926,1024,2.59,0.05,1.77 +mobilenetv2_035,224,22588.52,45.323,1024,1.68,0.07,2.86 +resnet10t,176,22365.03,45.776,1024,5.44,0.7,1.51 +levit_conv_128s,224,22115.28,46.293,1024,7.78,0.31,1.88 +mnasnet_050,224,21012.53,48.723,1024,2.22,0.11,3.07 +resnet18,160,20850.92,49.101,1024,11.69,0.93,1.27 +tinynet_c,184,19198.02,53.329,1024,2.46,0.11,2.87 +efficientvit_m5,224,19162.34,53.428,1024,12.47,0.53,2.41 +efficientvit_b0,224,19036.4,53.782,1024,3.41,0.1,2.87 +repghostnet_058,224,18156.61,56.388,1024,2.55,0.07,2.59 +semnasnet_050,224,17891.8,57.223,1024,2.08,0.11,3.44 +mobilenetv2_050,224,17837.51,57.397,1024,1.97,0.1,3.64 +regnetx_004,224,17491.27,58.531,1024,5.16,0.4,3.14 +regnetx_004_tv,224,16931.52,60.467,1024,5.5,0.42,3.17 +cs3darknet_focus_s,256,16535.27,61.918,1024,3.27,0.69,2.7 +lcnet_150,224,16211.53,63.155,1024,4.5,0.34,3.79 +gernet_s,224,15890.89,64.43,1024,8.17,0.75,2.65 +cs3darknet_s,256,15705.73,65.189,1024,3.28,0.72,2.97 +levit_128,224,15600.03,65.631,1024,9.21,0.41,2.71 +mobilenetv3_large_075,224,15141.94,67.617,1024,3.99,0.16,4.0 +vit_tiny_r_s16_p8_224,224,15074.81,67.918,1024,6.34,0.44,2.06 +levit_conv_128,224,15050.96,68.026,1024,9.21,0.41,2.71 +repghostnet_080,224,14725.52,69.529,1024,3.28,0.1,3.22 +resnet10t,224,14365.94,71.27,1024,5.44,1.1,2.43 +pit_ti_224,224,14330.5,71.446,1024,4.85,0.7,6.19 +pit_ti_distilled_224,224,14304.16,71.577,1024,5.1,0.71,6.23 +mobilenetv3_rw,224,13802.85,74.178,1024,5.48,0.23,4.41 +tf_efficientnetv2_b0,192,13802.18,74.181,1024,7.14,0.54,3.51 +levit_192,224,13743.1,74.5,1024,10.95,0.66,3.2 +mnasnet_075,224,13653.81,74.988,1024,3.17,0.23,4.77 +hardcorenas_a,224,13581.17,75.389,1024,5.26,0.23,4.38 +mobilenetv3_large_100,224,13556.89,75.524,1024,5.48,0.23,4.41 +vit_small_patch32_224,224,13379.42,76.525,1024,22.88,1.15,2.5 +levit_conv_192,224,13084.23,78.252,1024,10.95,0.66,3.2 +tf_mobilenetv3_large_075,224,12872.36,79.54,1024,3.99,0.16,4.0 +regnety_004,224,12765.55,80.202,1024,4.34,0.41,3.89 +tinynet_b,188,12493.02,81.956,1024,3.73,0.21,4.44 +tf_mobilenetv3_large_minimal_100,224,12397.28,82.589,1024,3.92,0.22,4.4 +nf_regnet_b0,192,12394.96,82.604,1024,8.76,0.37,3.15 +resnet34,160,12381.04,82.698,1024,21.8,1.87,1.91 +hardcorenas_b,224,12362.7,82.82,1024,5.18,0.26,5.09 +hardcorenas_c,224,12318.59,83.117,1024,5.52,0.28,5.01 +mobilenetv1_100,224,12074.33,84.798,1024,4.23,0.58,5.04 +repghostnet_100,224,12055.17,84.933,1024,4.07,0.15,3.98 +resnet14t,176,11947.82,85.696,1024,10.08,1.07,3.61 +mnasnet_100,224,11914.93,85.933,1024,4.38,0.33,5.46 +regnety_006,224,11850.66,86.395,1024,6.06,0.61,4.33 +mobilenetv1_100h,224,11818.6,86.634,1024,5.28,0.63,5.09 +ghostnet_100,224,11799.55,86.773,1024,5.18,0.15,3.55 +ese_vovnet19b_slim_dw,224,11783.68,86.891,1024,1.9,0.4,5.28 +mixer_s32_224,224,11733.67,87.26,1024,19.1,1.0,2.28 +tf_mobilenetv3_large_100,224,11642.43,87.945,1024,5.48,0.23,4.41 +hardcorenas_d,224,11549.38,88.653,1024,7.5,0.3,4.93 +semnasnet_075,224,11368.35,90.064,1024,2.91,0.23,5.54 +mobilenetv2_075,224,11254.61,90.975,1024,2.64,0.22,5.86 +mobilenet_edgetpu_v2_xs,224,11215.06,91.296,1024,4.46,0.7,4.8 +resnet18,224,11188.75,91.511,1024,11.69,1.82,2.48 +spnasnet_100,224,10760.35,95.154,1024,4.42,0.35,6.03 +regnetx_008,224,10657.43,96.071,1024,7.26,0.81,5.15 +tf_efficientnetv2_b1,192,10601.92,96.577,1024,8.14,0.76,4.59 +mobilenetv4_conv_medium,224,10533.33,97.206,1024,9.72,0.84,5.8 +seresnet18,224,10519.36,97.335,1024,11.78,1.82,2.49 +repghostnet_111,224,10479.86,97.701,1024,4.54,0.18,4.38 +deit_tiny_patch16_224,224,10451.69,97.965,1024,5.72,1.26,5.97 +deit_tiny_distilled_patch16_224,224,10407.6,98.38,1024,5.91,1.27,6.01 +vit_tiny_patch16_224,224,10405.73,98.398,1024,5.72,1.26,5.97 +tf_efficientnetv2_b0,224,10314.64,99.267,1024,7.14,0.73,4.77 +hardcorenas_f,224,10206.96,100.314,1024,8.2,0.35,5.57 +legacy_seresnet18,224,10179.01,100.588,1024,11.78,1.82,2.49 +tinynet_a,192,10107.12,101.305,1024,6.19,0.35,5.41 +hardcorenas_e,224,10040.98,101.972,1024,8.07,0.35,5.65 +semnasnet_100,224,10029.01,102.094,1024,3.89,0.32,6.23 +mobilenet_edgetpu_100,224,10018.91,102.197,1024,4.09,1.0,5.75 +levit_256,224,9996.37,102.427,1024,18.89,1.13,4.23 +dla46_c,224,9963.65,102.763,1024,1.3,0.58,4.5 +mobilenetv2_100,224,9887.57,103.554,1024,3.5,0.31,6.68 +repvgg_a0,224,9797.14,104.51,1024,9.11,1.52,3.59 +regnety_008,224,9778.52,104.708,1024,6.26,0.81,5.25 +fbnetc_100,224,9724.0,105.297,1024,5.57,0.4,6.51 +efficientnet_lite0,224,9657.18,106.025,1024,4.65,0.4,6.74 +regnetx_006,224,9637.51,106.242,1024,6.2,0.61,3.98 +levit_conv_256,224,9529.69,107.444,1024,18.89,1.13,4.23 +resnet18d,224,9508.04,107.689,1024,11.71,2.06,3.29 +hgnetv2_b0,224,9407.15,108.843,1024,6.0,0.33,2.12 +pit_xs_224,224,9387.39,109.073,1024,10.62,1.4,7.71 +regnety_008_tv,224,9370.17,109.273,1024,6.43,0.84,5.42 +mobilenetv4_hybrid_medium_075,224,9355.37,109.446,1024,7.31,0.66,5.65 +pit_xs_distilled_224,224,9342.89,109.592,1024,11.0,1.41,7.76 +mobilenetv1_100,256,9270.71,110.445,1024,4.23,0.76,6.59 +ese_vovnet19b_slim,224,9264.21,110.524,1024,3.17,1.69,3.52 +mobilenetv1_125,224,9214.57,111.118,1024,6.27,0.89,6.3 +vit_xsmall_patch16_clip_224,224,9204.49,111.24,1024,8.28,1.79,6.65 +convnext_atto,224,9196.49,111.337,1024,3.7,0.55,3.81 +vit_medium_patch32_clip_224,224,9164.71,111.723,1024,39.69,2.0,3.34 +mobilenetv1_100h,256,9072.78,112.855,1024,5.28,0.82,6.65 +repghostnet_130,224,8966.92,114.188,1024,5.48,0.25,5.24 +ghostnet_130,224,8941.94,114.507,1024,7.36,0.24,4.6 +convnext_atto_ols,224,8902.58,115.013,1024,3.7,0.58,4.11 +regnetz_005,224,8898.94,115.06,1024,7.12,0.52,5.86 +xcit_nano_12_p16_224,224,8661.49,118.214,1024,3.05,0.56,4.17 +levit_256d,224,8476.14,120.799,1024,26.21,1.4,4.93 +edgenext_xx_small,256,8329.49,122.926,1024,1.33,0.26,3.33 +tf_efficientnet_lite0,224,8319.74,123.071,1024,4.65,0.4,6.74 +fbnetv3_b,224,8315.87,123.128,1024,8.6,0.42,6.97 +efficientnet_b0,224,8241.4,124.241,1024,5.29,0.4,6.75 +levit_conv_256d,224,8143.78,125.73,1024,26.21,1.4,4.93 +mnasnet_140,224,8104.58,126.338,1024,7.12,0.6,7.71 +mobilenetv4_conv_medium,256,8092.13,126.533,1024,9.72,1.1,7.58 +convnext_femto,224,8051.49,127.171,1024,5.22,0.79,4.57 +tf_efficientnetv2_b2,208,7901.34,129.588,1024,10.1,1.06,6.0 +mobilevit_xxs,256,7885.22,129.853,1024,1.27,0.42,8.34 +repghostnet_150,224,7853.84,130.372,1024,6.58,0.32,6.0 +convnext_femto_ols,224,7832.99,130.719,1024,5.23,0.82,4.87 +mobilenet_edgetpu_v2_s,224,7762.61,131.904,1024,5.99,1.21,6.6 +rexnetr_100,224,7696.45,133.034,1024,4.88,0.43,7.72 +repvit_m1,224,7673.77,133.431,1024,5.49,0.83,7.45 +resnet14t,224,7579.52,135.091,1024,10.08,1.69,5.8 +mobilenetv4_hybrid_medium,224,7493.91,136.634,1024,11.07,0.98,6.84 +mobilenetv2_110d,224,7490.18,136.702,1024,4.52,0.45,8.71 +cs3darknet_focus_m,256,7460.64,137.244,1024,9.3,1.98,4.89 +repvit_m0_9,224,7284.39,140.555,1024,5.49,0.83,7.45 +rexnet_100,224,7243.9,141.351,1024,4.8,0.41,7.44 +tf_efficientnet_b0,224,7243.0,141.368,1024,5.29,0.4,6.75 +efficientnet_b1_pruned,240,7240.43,141.418,1024,6.33,0.4,6.21 +efficientvit_b1,224,7214.92,141.918,1024,9.1,0.53,7.25 +resnet50,160,7190.62,142.398,1024,25.56,2.1,5.67 +cs3darknet_m,256,7120.83,143.794,1024,9.31,2.08,5.28 +mobilevitv2_050,256,7087.19,144.476,1024,1.37,0.48,8.04 +nf_regnet_b0,256,7083.25,144.556,1024,8.76,0.64,5.58 +hrnet_w18_small,224,7050.65,145.221,1024,13.19,1.61,5.72 +mobilenetv1_125,256,7010.8,146.05,1024,6.27,1.16,8.23 +repvgg_a1,224,6986.39,146.561,1024,14.09,2.64,4.74 +hgnetv2_b1,224,6916.93,148.033,1024,6.34,0.49,2.73 +fbnetv3_d,224,6914.78,148.079,1024,10.31,0.52,8.5 +gernet_m,224,6873.04,148.978,1024,21.14,3.02,5.24 +visformer_tiny,224,6836.16,149.782,1024,10.32,1.27,5.72 +pvt_v2_b0,224,6743.86,151.832,1024,3.67,0.57,7.99 +tf_efficientnetv2_b1,240,6694.16,152.959,1024,8.14,1.21,7.34 +resnet50d,160,6637.34,154.269,1024,25.58,2.22,6.08 +edgenext_xx_small,288,6577.06,155.681,1024,1.33,0.33,4.21 +vit_betwixt_patch32_clip_224,224,6547.37,156.388,1024,61.41,3.09,4.17 +resnet34,224,6529.57,156.815,1024,21.8,3.67,3.74 +ese_vovnet19b_dw,224,6511.74,157.245,1024,6.54,1.34,8.25 +efficientnet_lite1,240,6378.58,160.527,1024,5.42,0.62,10.14 +resnet18,288,6375.26,160.611,1024,11.69,3.01,4.11 +fbnetv3_b,256,6364.95,160.871,1024,8.6,0.55,9.1 +selecsls42,224,6333.78,161.663,1024,30.35,2.94,4.62 +selecsls42b,224,6319.75,162.02,1024,32.46,2.98,4.62 +crossvit_tiny_240,240,6299.42,162.544,1024,7.01,1.57,9.08 +convnext_pico,224,6259.06,163.593,1024,9.05,1.37,6.1 +efficientnet_es_pruned,224,6253.5,163.738,1024,5.44,1.81,8.73 +crossvit_9_dagger_240,240,6253.37,163.741,1024,8.78,1.99,9.97 +seresnet50,160,6246.72,163.91,1024,28.09,2.1,5.69 +efficientnet_b0,256,6246.01,163.934,1024,5.29,0.52,8.81 +efficientnet_es,224,6238.02,164.145,1024,5.44,1.81,8.73 +efficientnet_blur_b0,224,6201.55,165.11,1024,5.29,0.43,8.72 +mobilenetv3_large_150d,224,6128.32,167.083,1024,14.62,, +repvit_m1_0,224,6105.54,167.706,1024,7.3,1.13,8.69 +seresnet34,224,6098.18,167.906,1024,21.96,3.67,3.74 +convnext_pico_ols,224,6085.56,168.258,1024,9.06,1.43,6.5 +resnext50_32x4d,160,6056.31,169.069,1024,25.03,2.17,7.35 +repghostnet_200,224,6053.09,169.16,1024,9.8,0.54,7.96 +mobilenet_edgetpu_v2_m,224,6048.59,169.285,1024,8.46,1.85,8.15 +tiny_vit_5m_224,224,6035.01,169.666,1024,12.08,1.28,11.25 +seresnet18,288,6022.58,170.017,1024,11.78,3.01,4.11 +levit_384,224,6010.01,170.372,1024,39.13,2.36,6.26 +cs3darknet_focus_m,288,5993.42,170.844,1024,9.3,2.51,6.19 +resnet26,224,5987.24,171.021,1024,16.0,2.36,7.35 +dla34,224,5969.98,171.514,1024,15.74,3.07,5.02 +repvit_m2,224,5966.06,171.627,1024,8.8,1.36,9.43 +resnet34d,224,5928.79,172.706,1024,21.82,3.91,4.54 +tf_efficientnet_es,224,5915.64,173.09,1024,5.44,1.81,8.73 +legacy_seresnet34,224,5895.55,173.68,1024,21.96,3.67,3.74 +resnetrs50,160,5890.41,173.832,1024,35.69,2.29,6.2 +crossvit_9_240,240,5878.46,174.185,1024,8.55,1.85,9.52 +efficientnet_b0_g16_evos,224,5867.79,174.502,1024,8.11,1.01,7.42 +ecaresnet50t,160,5866.78,174.532,1024,25.57,2.21,6.04 +resnet50,176,5862.03,174.673,1024,25.56,2.62,6.92 +efficientnet_b1,224,5792.59,176.768,1024,7.79,0.59,9.36 +cs3darknet_m,288,5761.99,177.707,1024,9.31,2.63,6.69 +mobilenetv4_hybrid_medium,256,5760.47,177.753,1024,11.07,1.29,9.01 +levit_conv_384,224,5757.96,177.831,1024,39.13,2.36,6.26 +ghostnetv2_100,224,5689.23,179.979,1024,6.16,0.18,4.55 +tf_efficientnet_lite1,240,5688.37,180.007,1024,5.42,0.62,10.14 +mixnet_s,224,5667.55,180.667,1024,4.13,0.25,6.25 +repvit_m1_1,224,5665.98,180.718,1024,8.8,1.36,9.43 +resnetblur18,224,5629.69,181.883,1024,11.69,2.34,3.39 +efficientvit_b1,256,5607.85,182.591,1024,9.1,0.69,9.46 +convnext_atto,288,5598.29,182.904,1024,3.7,0.91,6.3 +hgnetv2_b0,288,5561.84,184.102,1024,6.0,0.54,3.51 +selecsls60,224,5536.37,184.949,1024,30.67,3.59,5.52 +resnet18d,288,5520.36,185.486,1024,11.71,3.41,5.43 +xcit_tiny_12_p16_224,224,5519.98,185.498,1024,6.72,1.24,6.29 +dla46x_c,224,5516.82,185.602,1024,1.07,0.54,5.66 +selecsls60b,224,5514.38,185.687,1024,32.77,3.63,5.52 +darknet17,256,5477.03,186.952,1024,14.3,3.26,7.18 +skresnet18,224,5436.76,188.337,1024,11.96,1.82,3.24 +regnetz_005,288,5429.12,188.602,1024,7.12,0.86,9.68 +convnext_atto_ols,288,5423.39,188.802,1024,3.7,0.96,6.8 +resmlp_12_224,224,5417.84,188.996,1024,15.35,3.01,5.5 +convnextv2_atto,224,5393.05,189.864,1024,3.71,0.55,3.81 +mobilenet_edgetpu_v2_l,224,5392.86,189.871,1024,10.92,2.55,9.05 +nf_regnet_b2,240,5319.96,192.472,1024,14.31,0.97,7.23 +nf_regnet_b1,256,5313.59,192.704,1024,10.22,0.82,7.27 +repvgg_b0,224,5312.16,192.755,1024,15.82,3.41,6.15 +edgenext_x_small,256,5311.8,192.768,1024,2.34,0.54,5.93 +dla60x_c,224,5286.77,193.68,1024,1.32,0.59,6.01 +tf_mixnet_s,224,5242.96,195.299,1024,4.13,0.25,6.25 +resnet26d,224,5233.24,195.662,1024,16.01,2.6,8.15 +resnetaa34d,224,5231.1,195.743,1024,21.82,4.43,5.07 +vit_tiny_r_s16_p8_384,384,5200.71,196.885,1024,6.36,1.34,6.49 +mobilenetv4_conv_medium,320,5184.61,197.498,1024,9.72,1.71,11.84 +semnasnet_140,224,5180.64,197.649,1024,6.11,0.6,8.87 +fbnetv3_d,256,5175.76,197.836,1024,10.31,0.68,11.1 +vit_base_patch32_clip_224,224,5121.46,199.933,1024,88.22,4.41,5.01 +vit_base_patch32_224,224,5114.3,200.213,1024,88.22,4.41,5.01 +mobilenetv4_conv_aa_medium,256,5106.4,200.523,1024,9.72,1.58,10.3 +gmixer_12_224,224,5100.86,200.741,1024,12.7,2.67,7.26 +regnetz_b16,224,5067.77,202.051,1024,9.72,1.45,9.95 +mobilenetv2_140,224,5065.51,202.141,1024,6.11,0.6,9.57 +mobilenetv4_conv_blur_medium,224,5036.62,203.302,1024,9.72,1.22,8.58 +mixer_s16_224,224,5021.22,203.925,1024,18.53,3.79,5.97 +tiny_vit_11m_224,224,5013.61,204.233,1024,20.35,2.04,13.49 +hgnetv2_b4,224,4999.81,204.797,1024,19.8,2.75,6.7 +eva02_tiny_patch14_224,224,4958.89,206.487,1024,5.5,1.7,9.14 +efficientnet_b1,240,4954.82,206.656,1024,7.79,0.71,10.88 +pit_s_224,224,4950.08,206.854,1024,23.46,2.88,11.56 +resnext50_32x4d,176,4946.85,206.99,1024,25.03,2.71,8.97 +pit_s_distilled_224,224,4922.56,208.011,1024,24.04,2.9,11.64 +darknet21,256,4915.34,208.316,1024,20.86,3.93,7.47 +nf_resnet26,224,4912.43,208.441,1024,16.0,2.41,7.35 +convnext_femto,288,4895.87,209.146,1024,5.22,1.3,7.56 +efficientnet_lite2,260,4895.77,209.15,1024,6.09,0.89,12.9 +gmlp_ti16_224,224,4887.42,209.508,1024,5.87,1.34,7.55 +tf_efficientnetv2_b2,260,4842.96,211.431,1024,10.1,1.72,9.84 +convnext_femto_ols,288,4762.02,215.024,1024,5.23,1.35,8.06 +efficientnet_b2_pruned,260,4761.44,215.051,1024,8.31,0.73,9.13 +efficientnetv2_rw_t,224,4734.23,216.287,1024,13.65,1.93,9.94 +resnext26ts,256,4729.62,216.496,1024,10.3,2.43,10.52 +ecaresnet50d_pruned,224,4721.76,216.858,1024,19.94,2.53,6.43 +mixer_b32_224,224,4721.22,216.883,1024,60.29,3.24,6.29 +sedarknet21,256,4717.55,217.052,1024,20.95,3.93,7.47 +efficientnet_cc_b0_4e,224,4699.43,217.889,1024,13.31,0.41,9.42 +efficientformer_l1,224,4692.55,218.208,1024,12.29,1.3,5.53 +hgnetv2_b2,224,4665.47,219.475,1024,11.22,1.15,4.12 +efficientnet_cc_b0_8e,224,4649.86,220.212,1024,24.01,0.42,9.42 +deit_small_patch16_224,224,4640.58,220.651,1024,22.05,4.61,11.95 +dpn48b,224,4632.75,221.023,1024,9.13,1.69,8.92 +vit_base_patch32_clip_quickgelu_224,224,4629.11,221.199,1024,87.85,4.41,5.01 +vit_small_patch32_384,384,4623.55,221.464,1024,22.92,3.45,8.25 +convnextv2_femto,224,4619.54,221.657,1024,5.23,0.79,4.57 +vit_small_patch16_224,224,4616.6,221.798,1024,22.05,4.61,11.95 +mobilenet_edgetpu_v2_m,256,4603.21,222.443,1024,8.46,2.42,10.65 +mobilevitv2_075,256,4603.11,222.448,1024,2.87,1.05,12.06 +rexnetr_130,224,4600.81,222.56,1024,7.61,0.68,9.81 +deit_small_distilled_patch16_224,224,4599.62,222.617,1024,22.44,4.63,12.02 +gc_efficientnetv2_rw_t,224,4586.63,223.247,1024,13.68,1.94,9.97 +eca_resnext26ts,256,4583.46,223.402,1024,10.3,2.43,10.52 +seresnext26ts,256,4576.49,223.742,1024,10.39,2.43,10.52 +tf_efficientnetv2_b3,240,4549.81,225.054,1024,14.36,1.93,9.95 +legacy_seresnext26_32x4d,224,4538.76,225.601,1024,16.79,2.49,9.39 +gcresnext26ts,256,4524.04,226.336,1024,10.48,2.43,10.53 +tf_efficientnet_b1,240,4521.51,226.463,1024,7.79,0.71,10.88 +gernet_l,256,4517.69,226.654,1024,31.08,4.57,8.0 +resnet101,160,4424.84,231.411,1024,44.55,4.0,8.28 +convnext_nano,224,4416.52,231.847,1024,15.59,2.46,8.37 +tf_efficientnet_lite2,260,4392.9,233.093,1024,6.09,0.89,12.9 +efficientnet_b1,256,4384.99,233.513,1024,7.79,0.77,12.22 +rexnet_130,224,4380.06,233.776,1024,7.56,0.68,9.71 +efficientvit_b1,288,4359.22,234.894,1024,9.1,0.87,11.96 +ghostnetv2_130,224,4355.29,235.105,1024,8.96,0.28,5.9 +mobilenetv4_conv_large,256,4341.06,235.877,1024,32.59,2.86,12.14 +tf_efficientnet_cc_b0_4e,224,4338.57,236.012,1024,13.31,0.41,9.42 +mobilenetv2_120d,224,4324.07,236.803,1024,5.83,0.69,11.97 +tf_efficientnet_cc_b0_8e,224,4315.19,237.291,1024,24.01,0.42,9.42 +deit3_small_patch16_224,224,4283.7,239.035,1024,22.06,4.61,11.95 +vit_relpos_small_patch16_rpn_224,224,4272.39,239.667,1024,21.97,4.59,13.05 +vit_wee_patch16_reg1_gap_256,256,4262.7,240.213,1024,13.42,3.83,13.9 +cs3darknet_focus_l,256,4254.84,240.657,1024,21.15,4.66,8.03 +vit_relpos_small_patch16_224,224,4248.63,241.008,1024,21.98,4.59,13.05 +vit_pwee_patch16_reg1_gap_256,256,4244.05,241.269,1024,15.25,4.37,15.87 +regnetx_016,224,4239.24,241.54,1024,9.19,1.62,7.93 +edgenext_x_small,288,4233.57,241.864,1024,2.34,0.68,7.5 +vit_srelpos_small_patch16_224,224,4224.03,242.412,1024,21.97,4.59,12.16 +nf_regnet_b1,288,4209.65,243.24,1024,10.22,1.02,9.2 +nf_ecaresnet26,224,4182.26,244.834,1024,16.0,2.41,7.36 +efficientnet_b2,256,4174.44,245.292,1024,9.11,0.89,12.81 +nf_seresnet26,224,4174.23,245.305,1024,17.4,2.41,7.36 +mobilenetv4_hybrid_large_075,256,4171.98,245.436,1024,22.75,2.06,11.64 +nf_regnet_b2,272,4148.23,246.842,1024,14.31,1.22,9.27 +cs3darknet_l,256,4138.17,247.442,1024,21.16,4.86,8.55 +convnext_nano_ols,224,4136.46,247.544,1024,15.65,2.65,9.38 +rexnetr_150,224,4135.64,247.594,1024,9.78,0.89,11.13 +ecaresnext50t_32x4d,224,4123.94,248.296,1024,15.41,2.7,10.09 +ecaresnext26t_32x4d,224,4122.74,248.367,1024,15.41,2.7,10.09 +seresnext26t_32x4d,224,4117.08,248.709,1024,16.81,2.7,10.09 +poolformer_s12,224,4088.18,250.468,1024,11.92,1.82,5.53 +repvgg_a2,224,4084.78,250.676,1024,28.21,5.7,6.26 +seresnext26d_32x4d,224,4078.22,251.079,1024,16.81,2.73,10.19 +resnet26t,256,4062.55,252.048,1024,16.01,3.35,10.52 +hgnet_tiny,224,4057.65,252.352,1024,14.74,4.54,6.36 +hgnetv2_b1,288,4056.18,252.444,1024,6.34,0.82,4.51 +flexivit_small,240,4044.24,253.19,1024,22.06,5.35,14.18 +repvit_m3,224,4026.3,254.31,1024,10.68,1.89,13.94 +edgenext_small,256,4025.36,254.377,1024,5.59,1.26,9.07 +mobileone_s1,224,4001.06,255.921,1024,4.83,0.86,9.67 +vit_base_patch32_clip_256,256,3985.68,256.91,1024,87.86,5.76,6.65 +resnetv2_50,224,3927.67,260.703,1024,25.55,4.11,11.11 +eca_botnext26ts_256,256,3922.63,261.039,1024,10.59,2.46,11.6 +regnety_016,224,3879.4,263.947,1024,11.2,1.63,8.04 +pvt_v2_b1,224,3876.64,264.136,1024,14.01,2.12,15.39 +cs3sedarknet_l,256,3859.41,265.314,1024,21.91,4.86,8.56 +mobilenetv4_conv_blur_medium,256,3857.99,265.413,1024,9.72,1.59,11.2 +dpn68,224,3853.11,265.748,1024,12.61,2.35,10.47 +ese_vovnet19b_dw,288,3852.02,265.825,1024,6.54,2.22,13.63 +fbnetv3_g,240,3837.93,266.791,1024,16.62,1.28,14.87 +resnet34,288,3825.08,267.697,1024,21.8,6.07,6.18 +convnext_pico,288,3802.68,269.273,1024,9.05,2.27,10.08 +eca_halonext26ts,256,3798.67,269.558,1024,10.76,2.44,11.46 +ecaresnetlight,224,3794.95,269.822,1024,30.16,4.11,8.42 +dpn68b,224,3764.97,271.97,1024,12.61,2.35,10.47 +efficientnet_em,240,3750.15,273.045,1024,6.9,3.04,14.34 +mixnet_m,224,3745.46,273.387,1024,5.01,0.36,8.19 +hgnetv2_b3,224,3744.03,273.492,1024,16.29,1.78,5.07 +resnet50,224,3729.86,274.531,1024,25.56,4.11,11.11 +efficientnet_b3_pruned,300,3729.22,274.578,1024,9.86,1.04,11.86 +bat_resnext26ts,256,3705.73,276.319,1024,10.73,2.53,12.51 +convnext_pico_ols,288,3702.45,276.564,1024,9.06,2.37,10.74 +rexnet_150,224,3700.42,276.715,1024,9.73,0.9,11.21 +botnet26t_256,256,3668.77,279.103,1024,12.49,3.32,11.98 +hrnet_w18_small_v2,224,3662.73,279.553,1024,15.6,2.62,9.65 +resnest14d,224,3658.62,279.877,1024,10.61,2.76,7.33 +resnet32ts,256,3655.47,280.118,1024,17.96,4.63,11.58 +ecaresnet26t,256,3636.71,281.563,1024,16.01,3.35,10.53 +resnet101,176,3629.67,282.109,1024,44.55,4.92,10.08 +mobilenetv4_hybrid_medium,320,3627.2,282.301,1024,11.07,2.05,14.36 +resnext26ts,288,3614.6,283.286,1024,10.3,3.07,13.31 +resnetv2_50t,224,3605.27,284.018,1024,25.57,4.32,11.82 +resnet33ts,256,3596.87,284.683,1024,19.68,4.76,11.66 +tf_efficientnet_em,240,3593.09,284.981,1024,6.9,3.04,14.34 +resnetv2_50d,224,3590.41,285.193,1024,25.57,4.35,11.92 +seresnet34,288,3587.79,285.399,1024,21.96,6.07,6.18 +regnetv_040,224,3583.87,285.714,1024,20.64,4.0,12.29 +tf_mixnet_m,224,3580.97,285.946,1024,5.01,0.36,8.19 +halonet26t,256,3578.92,286.11,1024,12.48,3.19,11.69 +edgenext_small_rw,256,3571.34,286.716,1024,7.83,1.58,9.51 +ecaresnet101d_pruned,224,3571.05,286.74,1024,24.88,3.48,7.69 +dla60,224,3563.95,287.311,1024,22.04,4.26,10.16 +coat_lite_tiny,224,3562.29,287.445,1024,5.72,1.6,11.65 +regnety_040,224,3558.64,287.74,1024,20.65,4.0,12.29 +resnet50c,224,3552.9,288.205,1024,25.58,4.35,11.92 +convnextv2_pico,224,3531.58,289.945,1024,9.07,1.37,6.1 +vit_small_resnet26d_224,224,3508.49,291.853,1024,63.61,5.07,11.12 +resnet26,288,3505.0,292.144,1024,16.0,3.9,12.15 +eca_resnext26ts,288,3503.71,292.25,1024,10.3,3.07,13.32 +seresnext26ts,288,3501.42,292.44,1024,10.39,3.07,13.32 +tf_efficientnet_b2,260,3496.2,292.88,1024,9.11,1.02,13.83 +resnet34d,288,3493.39,293.114,1024,21.82,6.47,7.51 +eca_resnet33ts,256,3492.62,293.179,1024,19.68,4.76,11.66 +efficientnet_b1,288,3488.73,293.505,1024,7.79,0.97,15.46 +seresnet33ts,256,3486.45,293.698,1024,19.78,4.76,11.66 +gcresnext26ts,288,3458.99,296.03,1024,10.48,3.07,13.33 +tresnet_m,224,3453.12,296.532,1024,31.39,5.75,7.31 +gcresnet33ts,256,3446.19,297.129,1024,19.88,4.76,11.68 +coatnet_nano_cc_224,224,3441.88,297.501,1024,13.76,2.24,15.02 +resnet50t,224,3439.67,297.693,1024,25.57,4.32,11.82 +ghostnetv2_160,224,3435.57,298.048,1024,12.39,0.42,7.23 +resnet50d,224,3420.07,299.4,1024,25.58,4.35,11.92 +efficientvit_b2,224,3405.99,300.636,1024,24.33,1.6,14.62 +vovnet39a,224,3393.85,301.712,1024,22.6,7.09,6.73 +coatnext_nano_rw_224,224,3385.52,302.454,1024,14.7,2.47,12.8 +coatnet_nano_rw_224,224,3380.48,302.905,1024,15.14,2.41,15.41 +legacy_seresnet50,224,3371.1,303.744,1024,28.09,3.88,10.6 +efficientnet_b0_gn,224,3367.52,304.072,1024,5.29,0.42,6.75 +cs3darknet_focus_l,288,3358.65,304.874,1024,21.15,5.9,10.16 +fastvit_t8,256,3353.24,305.366,1024,4.03,0.7,8.63 +convit_tiny,224,3349.24,305.73,1024,5.71,1.26,7.94 +repvit_m1_5,224,3348.79,305.769,1024,14.64,2.31,15.7 +coat_lite_mini,224,3328.7,307.616,1024,11.01,2.0,12.25 +selecsls84,224,3309.96,309.357,1024,50.95,5.9,7.57 +efficientnet_b2,288,3305.5,309.776,1024,9.11,1.12,16.2 +sam2_hiera_tiny,224,3299.25,310.362,1024,26.85,4.91,17.12 +haloregnetz_b,224,3297.58,310.52,1024,11.68,1.97,11.94 +resnetblur18,288,3296.17,310.652,1024,11.69,3.87,5.6 +resnetaa50,224,3294.4,310.82,1024,25.56,5.15,11.64 +levit_512,224,3286.88,311.53,1024,95.17,5.64,10.22 +vit_tiny_patch16_384,384,3281.68,312.024,1024,5.79,4.7,25.39 +ese_vovnet39b,224,3281.48,312.043,1024,24.57,7.09,6.74 +mobilevit_xs,256,3279.06,312.274,1024,2.32,1.05,16.33 +wide_resnet50_2,176,3279.04,312.276,1024,68.88,7.29,8.97 +eca_vovnet39b,224,3274.47,312.712,1024,22.6,7.09,6.74 +convnextv2_atto,288,3271.31,313.015,1024,3.71,0.91,6.3 +mobileone_s2,224,3268.79,313.256,1024,7.88,1.34,11.55 +cs3darknet_l,288,3263.27,313.785,1024,21.16,6.16,10.83 +seresnet50,224,3243.29,315.718,1024,28.09,4.11,11.13 +vit_small_r26_s32_224,224,3242.11,315.833,1024,36.43,3.56,9.85 +regnetx_032,224,3239.67,316.071,1024,15.3,3.2,11.37 +nf_regnet_b3,288,3220.95,317.909,1024,18.59,1.67,11.84 +res2net50_48w_2s,224,3208.95,319.097,1024,25.29,4.18,11.72 +resnetaa34d,288,3173.49,322.664,1024,21.82,7.33,8.38 +dla60x,224,3140.94,326.005,1024,17.35,3.54,13.8 +resnext50_32x4d,224,3135.91,326.53,1024,25.03,4.26,14.4 +mobileone_s0,224,3126.59,327.503,1024,5.29,1.09,15.48 +convnext_tiny,224,3120.16,328.178,1024,28.59,4.47,13.44 +visformer_small,224,3116.52,328.562,1024,40.22,4.88,11.43 +resnet152,160,3116.12,328.603,1024,60.19,5.9,11.51 +levit_conv_512,224,3097.94,330.532,1024,95.17,5.64,10.22 +vit_relpos_base_patch32_plus_rpn_256,256,3096.69,330.659,1024,119.42,7.68,8.01 +resnet26d,288,3085.16,331.901,1024,16.01,4.29,13.48 +skresnet34,224,3069.81,333.561,1024,22.28,3.67,5.13 +levit_512d,224,3062.18,334.39,1024,92.5,5.85,11.3 +resnet50_clip_gap,224,3051.19,335.595,1024,23.53,5.39,12.44 +eca_nfnet_l0,224,3050.64,335.658,1024,24.14,4.35,10.47 +resnetaa50d,224,3048.06,335.941,1024,25.58,5.39,12.44 +regnetz_c16,256,3040.22,336.807,1024,13.46,2.51,16.57 +cs3sedarknet_l,288,3038.16,337.035,1024,21.91,6.16,10.83 +xcit_nano_12_p16_384,384,3035.13,337.372,1024,3.05,1.64,12.15 +tiny_vit_21m_224,224,3034.37,337.457,1024,33.22,4.29,20.08 +densenet121,224,3031.71,337.752,1024,7.98,2.87,6.9 +nfnet_l0,224,3030.88,337.845,1024,35.07,4.36,10.47 +resnetrs50,224,3023.9,338.625,1024,35.69,4.48,12.14 +ecaresnet50t,224,3023.88,338.627,1024,25.57,4.32,11.83 +regnetz_b16,288,3023.06,338.719,1024,9.72,2.39,16.43 +seresnet50t,224,3021.11,338.934,1024,28.1,4.32,11.83 +ecaresnet50d,224,3008.25,340.387,1024,25.58,4.35,11.93 +vit_base_patch32_plus_256,256,3003.41,340.935,1024,119.48,7.79,7.76 +vit_medium_patch16_clip_224,224,3002.97,340.985,1024,38.59,8.0,15.93 +hgnetv2_b4,288,3001.56,341.146,1024,19.8,4.54,11.08 +crossvit_small_240,240,2994.9,341.904,1024,26.86,5.63,18.17 +resnet50s,224,2990.06,342.458,1024,25.68,5.47,13.52 +rexnetr_200,224,2981.52,343.438,1024,16.52,1.59,15.11 +mixnet_l,224,2960.5,345.878,1024,7.33,0.58,10.84 +efficientnet_cc_b1_8e,240,2957.88,346.183,1024,39.72,0.75,15.44 +xcit_tiny_24_p16_224,224,2951.65,346.913,1024,12.12,2.34,11.82 +gcresnext50ts,256,2949.61,347.155,1024,15.67,3.75,15.46 +lambda_resnet26rpt_256,256,2920.38,350.629,1024,10.99,3.16,11.87 +hiera_tiny_224,224,2913.8,351.42,1024,27.91,4.91,17.13 +resnext50d_32x4d,224,2906.09,352.354,1024,25.05,4.5,15.2 +resnet32ts,288,2900.64,353.016,1024,17.96,5.86,14.65 +vit_little_patch16_reg1_gap_256,256,2900.54,353.027,1024,22.52,6.27,18.06 +levit_conv_512d,224,2894.37,353.779,1024,92.5,5.85,11.3 +res2net50_26w_4s,224,2886.15,354.784,1024,25.7,4.28,12.61 +resnetv2_50x1_bit,224,2881.41,355.371,1024,25.55,4.23,11.11 +mobilenetv4_conv_large,320,2873.61,356.335,1024,32.59,4.47,18.97 +tf_efficientnetv2_b3,300,2870.86,356.676,1024,14.36,3.04,15.74 +vit_little_patch16_reg4_gap_256,256,2870.27,356.75,1024,22.52,6.35,18.33 +mobilevitv2_100,256,2869.22,356.881,1024,4.9,1.84,16.08 +coatnet_pico_rw_224,224,2867.77,357.061,1024,10.85,2.05,14.62 +efficientnetv2_rw_t,288,2863.9,357.543,1024,13.65,3.19,16.42 +resnet33ts,288,2854.58,358.712,1024,19.68,6.02,14.75 +cspresnet50,256,2853.48,358.849,1024,21.62,4.54,11.5 +inception_next_tiny,224,2844.01,360.044,1024,28.06,4.19,11.98 +resnet50_clip,224,2836.87,360.947,1024,38.32,6.14,12.98 +res2next50,224,2827.45,362.148,1024,24.67,4.2,13.71 +tf_mixnet_l,224,2826.43,362.284,1024,7.33,0.58,10.84 +dla60_res2next,224,2826.22,362.31,1024,17.03,3.49,13.17 +vit_base_resnet26d_224,224,2826.06,362.331,1024,101.4,6.97,13.16 +hgnetv2_b5,224,2824.85,362.487,1024,39.57,6.56,11.19 +vovnet57a,224,2824.04,362.591,1024,36.64,8.95,7.52 +deit3_medium_patch16_224,224,2819.68,363.151,1024,38.85,8.0,15.93 +convnextv2_femto,288,2809.3,364.492,1024,5.23,1.3,7.56 +res2net50_14w_8s,224,2809.15,364.506,1024,25.06,4.21,13.28 +gcresnet50t,256,2800.68,365.615,1024,25.9,5.42,14.67 +crossvit_15_240,240,2793.66,366.534,1024,27.53,5.81,19.77 +vit_relpos_medium_patch16_rpn_224,224,2791.96,366.75,1024,38.73,7.97,17.02 +seresnext50_32x4d,224,2785.57,367.599,1024,27.56,4.26,14.42 +dla60_res2net,224,2784.11,367.789,1024,20.85,4.15,12.34 +convnext_tiny_hnf,224,2781.72,368.1,1024,28.59,4.47,13.44 +gc_efficientnetv2_rw_t,288,2780.71,368.241,1024,13.68,3.2,16.45 +gcvit_xxtiny,224,2779.24,368.435,1024,12.0,2.14,15.36 +vgg11_bn,224,2779.08,368.458,1024,132.87,7.62,7.44 +legacy_seresnext50_32x4d,224,2778.41,368.545,1024,27.56,4.26,14.42 +tf_efficientnet_cc_b1_8e,240,2776.91,368.745,1024,39.72,0.75,15.44 +vit_relpos_medium_patch16_224,224,2773.12,369.248,1024,38.75,7.97,17.02 +resmlp_24_224,224,2769.63,369.714,1024,30.02,5.96,10.91 +crossvit_15_dagger_240,240,2769.4,369.745,1024,28.21,6.13,20.43 +hgnetv2_b2,288,2767.58,369.987,1024,11.22,1.89,6.8 +eca_resnet33ts,288,2765.99,370.2,1024,19.68,6.02,14.76 +vit_srelpos_medium_patch16_224,224,2763.71,370.506,1024,38.74,7.96,16.21 +seresnet33ts,288,2762.81,370.627,1024,19.78,6.02,14.76 +twins_svt_small,224,2759.48,371.074,1024,24.06,2.94,13.75 +vit_relpos_medium_patch16_cls_224,224,2754.95,371.684,1024,38.76,8.03,18.24 +twins_pcpvt_small,224,2745.95,372.902,1024,24.11,3.83,18.08 +nextvit_small,224,2739.59,373.768,1024,31.76,5.81,18.44 +davit_tiny,224,2738.13,280.473,768,28.36,4.54,18.89 +gcresnet33ts,288,2730.01,375.08,1024,19.88,6.02,14.78 +cspresnet50d,256,2722.26,376.147,1024,21.64,4.86,12.55 +fbnetv3_g,288,2718.22,376.707,1024,16.62,1.77,21.09 +seresnetaa50d,224,2716.73,376.913,1024,28.11,5.4,12.46 +ese_vovnet57b,224,2715.38,377.101,1024,38.61,8.95,7.52 +rexnet_200,224,2713.9,377.301,1024,16.37,1.56,14.91 +resnetblur50,224,2713.18,377.406,1024,25.56,5.16,12.02 +ecaresnet50d_pruned,288,2709.71,377.889,1024,19.94,4.19,10.61 +nfnet_f0,192,2708.66,378.036,1024,71.49,7.21,10.16 +efficientvit_l1,224,2706.35,378.359,1024,52.65,5.27,15.85 +convnext_nano,288,2696.47,379.744,1024,15.59,4.06,13.84 +hgnet_small,224,2694.73,379.99,1024,24.36,8.53,8.79 +res2net50d,224,2694.69,379.989,1024,25.72,4.52,13.41 +cspresnet50w,256,2694.09,380.081,1024,28.12,5.04,12.19 +coatnet_0_rw_224,224,2684.42,381.451,1024,27.44,4.43,18.73 +poolformerv2_s12,224,2665.71,384.127,1024,11.89,1.83,5.53 +mobileone_s3,224,2662.7,384.561,1024,10.17,1.94,13.85 +resnest26d,224,2658.83,385.121,1024,17.07,3.64,9.97 +efficientvit_b2,256,2647.05,386.835,1024,24.33,2.09,19.03 +regnetx_040,224,2643.92,387.289,1024,22.12,3.99,12.2 +regnety_032,224,2640.33,387.82,1024,19.44,3.2,11.26 +resnet51q,256,2620.47,390.76,1024,35.7,6.38,16.55 +repvgg_b1g4,224,2615.17,391.55,1024,39.97,8.15,10.64 +nf_regnet_b3,320,2615.09,391.563,1024,18.59,2.05,14.61 +gmixer_24_224,224,2609.82,392.354,1024,24.72,5.28,14.45 +densenetblur121d,224,2595.94,394.451,1024,8.0,3.11,7.9 +edgenext_small,320,2590.64,395.256,1024,5.59,1.97,14.16 +mobilevit_s,256,2588.55,395.577,1024,5.58,2.03,19.94 +cs3darknet_focus_x,256,2586.12,395.949,1024,35.02,8.03,10.69 +inception_v3,299,2561.31,399.784,1024,23.83,5.73,8.97 +resnet152,176,2560.74,399.874,1024,60.19,7.22,13.99 +nf_ecaresnet50,224,2541.49,402.904,1024,25.56,4.21,11.13 +resnetblur50d,224,2541.42,402.913,1024,25.58,5.4,12.82 +resnet26t,320,2540.06,403.129,1024,16.01,5.24,16.44 +nf_seresnet50,224,2535.8,403.807,1024,28.09,4.21,11.13 +convnextv2_nano,224,2534.59,403.998,1024,15.62,2.46,8.37 +efficientnet_b0_g8_gn,224,2533.38,404.193,1024,6.56,0.66,6.75 +resnetrs101,192,2519.45,406.427,1024,63.62,6.04,12.7 +convnext_nano_ols,288,2512.2,407.6,1024,15.65,4.38,15.5 +vit_medium_patch16_gap_240,240,2499.59,409.656,1024,44.4,9.22,18.81 +densenet169,224,2499.14,409.729,1024,14.15,3.4,7.3 +mobilenetv4_hybrid_medium,384,2489.91,411.248,1024,11.07,3.01,21.18 +xcit_small_12_p16_224,224,2480.75,412.767,1024,26.25,4.82,12.58 +cs3darknet_x,256,2478.79,413.095,1024,35.05,8.38,11.35 +cspresnext50,256,2475.13,413.705,1024,20.57,4.05,15.86 +darknetaa53,256,2444.37,418.912,1024,36.02,7.97,12.39 +darknet53,256,2438.24,419.962,1024,41.61,9.31,12.39 +seresnext26t_32x4d,288,2437.82,420.035,1024,16.81,4.46,16.68 +maxvit_pico_rw_256,256,2433.04,315.645,768,7.46,1.83,22.3 +sehalonet33ts,256,2426.84,421.938,1024,13.69,3.55,14.7 +maxvit_rmlp_pico_rw_256,256,2425.78,316.588,768,7.52,1.85,24.86 +seresnext26d_32x4d,288,2422.58,422.677,1024,16.81,4.51,16.85 +vit_base_r26_s32_224,224,2414.03,424.176,1024,101.38,6.81,12.36 +hgnet_tiny,288,2409.6,424.956,1024,14.74,7.51,10.51 +focalnet_tiny_srf,224,2408.09,425.223,1024,28.43,4.42,16.32 +fastvit_t12,256,2389.89,428.462,1024,7.55,1.42,12.42 +coatnet_rmlp_nano_rw_224,224,2375.1,431.128,1024,15.15,2.62,20.34 +resnet61q,256,2368.57,432.318,1024,36.85,7.8,17.01 +edgenext_base,256,2365.56,432.866,1024,18.51,3.85,15.58 +resnetv2_101,224,2358.08,434.239,1024,44.54,7.83,16.23 +hiera_small_224,224,2356.28,434.571,1024,35.01,6.42,20.75 +nf_resnet50,256,2351.74,435.412,1024,25.56,5.46,14.52 +cs3sedarknet_x,256,2344.23,436.807,1024,35.4,8.38,11.35 +resnet50_mlp,256,2333.79,438.762,1024,26.65,7.05,16.25 +ecaresnet50t,256,2331.13,439.262,1024,25.57,5.64,15.45 +resnetv2_50,288,2330.51,439.377,1024,25.55,6.79,18.37 +xcit_nano_12_p8_224,224,2328.05,439.842,1024,3.05,2.16,15.71 +efficientnet_lite3,300,2314.24,442.468,1024,8.2,1.65,21.85 +resnest50d_1s4x24d,224,2310.08,443.264,1024,25.68,4.43,13.57 +resnet101,224,2287.55,447.629,1024,44.55,7.83,16.23 +dm_nfnet_f0,192,2284.54,448.22,1024,71.49,7.21,10.16 +efficientnetv2_s,288,2283.97,448.33,1024,21.46,4.75,20.13 +ecaresnet26t,320,2283.24,448.474,1024,16.01,5.24,16.44 +pvt_v2_b2,224,2282.41,448.635,1024,25.36,4.05,27.53 +gcresnext50ts,288,2276.49,449.804,1024,15.67,4.75,19.57 +dla102,224,2270.79,450.932,1024,33.27,7.19,14.18 +dpn68b,288,2266.47,451.792,1024,12.61,3.89,17.3 +fastvit_s12,256,2263.12,452.46,1024,9.47,1.82,13.67 +gmlp_s16_224,224,2262.28,452.629,1024,19.42,4.42,15.1 +skresnet50,224,2261.97,452.692,1024,25.8,4.11,12.5 +ecaresnetlight,288,2258.94,453.299,1024,30.16,6.79,13.91 +rdnet_tiny,224,2257.25,453.639,1024,23.86,5.06,15.98 +fastvit_sa12,256,2256.89,453.711,1024,11.58,1.96,14.03 +focalnet_tiny_lrf,224,2250.45,455.008,1024,28.65,4.49,17.76 +regnetx_080,224,2241.1,456.908,1024,39.57,8.02,14.06 +resnetv2_101d,224,2230.6,459.058,1024,44.56,8.07,17.04 +hgnetv2_b3,288,2229.64,459.256,1024,16.29,2.94,8.38 +resnet101c,224,2225.59,460.093,1024,44.57,8.08,17.04 +vit_medium_patch16_gap_256,256,2220.07,461.236,1024,38.86,10.59,22.15 +resnet50,288,2211.87,462.946,1024,25.56,6.8,18.37 +edgenext_small_rw,320,2206.45,464.081,1024,7.83,2.46,14.85 +gcresnet50t,288,2205.25,464.336,1024,25.9,6.86,18.57 +vit_base_resnet50d_224,224,2196.36,466.215,1024,110.97,8.73,16.92 +regnetv_040,288,2184.78,468.687,1024,20.64,6.6,20.3 +eva02_tiny_patch14_336,336,2169.41,472.008,1024,5.76,4.68,27.16 +regnety_040,288,2169.12,472.071,1024,20.65,6.61,20.3 +efficientnetv2_rw_s,288,2168.46,472.214,1024,23.94,4.91,21.41 +eva02_small_patch14_224,224,2168.12,472.287,1024,21.62,6.14,18.28 +resnet101d,224,2168.0,472.315,1024,44.57,8.08,17.04 +vgg13,224,2160.71,473.907,1024,133.05,11.31,12.25 +gcvit_xtiny,224,2157.88,474.529,1024,19.98,2.93,20.26 +mobilevitv2_125,256,2156.6,474.811,1024,7.48,2.86,20.1 +convnextv2_pico,288,2147.68,476.783,1024,9.07,2.27,10.08 +skresnet50d,224,2140.48,478.386,1024,25.82,4.36,13.31 +res2net50_26w_6s,224,2137.67,479.014,1024,37.05,6.33,15.28 +ecaresnet101d_pruned,288,2127.79,481.24,1024,24.88,5.75,12.71 +hrnet_w18_ssld,224,2127.32,481.346,1024,21.3,4.32,16.31 +efficientvit_l2,224,2122.96,482.334,1024,63.71,6.97,19.58 +hieradet_small,256,2119.77,362.292,768,34.72,8.51,27.76 +tf_efficientnet_lite3,300,2115.57,484.019,1024,8.2,1.65,21.85 +hrnet_w18,224,2115.21,484.101,1024,21.3,4.32,16.31 +efficientformer_l3,224,2095.8,488.585,1024,31.41,3.93,12.01 +efficientvit_b2,288,2095.12,488.743,1024,24.33,2.64,24.03 +wide_resnet50_2,224,2093.41,489.145,1024,68.88,11.43,14.4 +sebotnet33ts_256,256,2091.61,244.778,512,13.7,3.89,17.46 +poolformer_s24,224,2090.86,489.741,1024,21.39,3.41,10.68 +resnet51q,288,2086.73,490.708,1024,35.7,8.07,20.94 +vit_medium_patch16_reg1_gap_256,256,2074.81,493.527,1024,38.88,10.63,22.26 +cspdarknet53,256,2060.68,496.914,1024,27.64,6.57,16.81 +nextvit_base,224,2056.15,498.007,1024,44.82,8.29,23.71 +vit_medium_patch16_reg4_gap_256,256,2054.73,498.351,1024,38.88,10.76,22.6 +efficientnet_b3,288,2053.58,498.63,1024,12.23,1.63,21.49 +resnet50t,288,2045.49,500.603,1024,25.57,7.14,19.53 +dla102x,224,2042.36,501.368,1024,26.31,5.89,19.42 +pvt_v2_b2_li,224,2034.87,503.215,1024,22.55,3.91,27.6 +legacy_seresnet101,224,2032.63,503.771,1024,49.33,7.61,15.74 +resnet50d,288,2031.97,503.934,1024,25.58,7.19,19.7 +mixnet_xl,224,2030.61,504.271,1024,11.9,0.93,14.57 +resnet101_clip_gap,224,2015.07,508.159,1024,42.52,9.11,17.56 +resnetaa101d,224,2013.22,508.627,1024,44.57,9.12,17.56 +swin_tiny_patch4_window7_224,224,2008.94,509.712,1024,28.29,4.51,17.06 +repvgg_b1,224,2004.0,510.966,1024,57.42,13.16,10.64 +coatnet_bn_0_rw_224,224,2002.05,511.465,1024,27.44,4.67,22.04 +cs3edgenet_x,256,1996.96,512.768,1024,47.82,11.53,12.92 +coatnet_rmlp_0_rw_224,224,1995.05,513.261,1024,27.45,4.72,24.89 +maxxvit_rmlp_nano_rw_256,256,1993.93,385.157,768,16.78,4.37,26.05 +cs3sedarknet_xdw,256,1988.81,514.859,1024,21.6,5.97,17.18 +seresnet101,224,1987.57,515.192,1024,49.33,7.84,16.27 +resnetaa50,288,1985.73,515.67,1024,25.56,8.52,19.24 +resnet101s,224,1984.65,515.949,1024,44.67,9.19,18.64 +mobilenetv4_conv_large,384,1984.05,516.104,1024,32.59,6.43,27.31 +regnetx_064,224,1979.8,517.214,1024,26.21,6.49,16.37 +regnetz_d32,256,1973.2,518.943,1024,27.58,5.98,23.74 +nest_tiny,224,1968.18,520.267,1024,17.06,5.83,25.48 +tf_efficientnetv2_s,300,1966.15,520.804,1024,21.46,5.35,22.73 +regnetz_d8,256,1964.69,521.191,1024,23.37,3.97,23.74 +regnetz_c16,320,1962.44,521.788,1024,13.46,3.92,25.88 +tresnet_v2_l,224,1957.54,523.094,1024,46.17,8.85,16.34 +cs3darknet_x,288,1954.84,523.818,1024,35.05,10.6,14.36 +ese_vovnet39b,288,1951.57,524.694,1024,24.57,11.71,11.13 +mobileone_s4,224,1950.42,525.005,1024,14.95,3.04,17.74 +crossvit_18_240,240,1944.91,526.492,1024,43.27,9.05,26.26 +nest_tiny_jx,224,1943.83,526.786,1024,17.06,5.83,25.48 +convnext_tiny,288,1942.31,527.195,1024,28.59,7.39,22.21 +resnext101_32x4d,224,1940.6,527.66,1024,44.18,8.01,21.23 +xcit_tiny_12_p16_384,384,1933.82,529.511,1024,6.72,3.64,18.26 +seresnet50,288,1928.14,531.071,1024,28.09,6.8,18.39 +darknetaa53,288,1927.58,531.226,1024,36.02,10.08,15.68 +densenet201,224,1923.55,532.337,1024,20.01,4.34,7.85 +regnetz_040,256,1923.5,532.341,1024,27.12,4.06,24.19 +nf_regnet_b4,320,1923.32,532.402,1024,30.21,3.29,19.88 +resnet101_clip,224,1921.47,532.914,1024,56.26,9.81,18.08 +crossvit_18_dagger_240,240,1916.57,534.275,1024,44.27,9.5,27.03 +darknet53,288,1911.52,535.684,1024,41.61,11.78,15.68 +regnetz_040_h,256,1907.32,536.868,1024,28.94,4.12,24.29 +resnext101_32x8d,176,1906.27,537.164,1024,88.79,10.33,19.37 +halonet50ts,256,1904.72,537.602,1024,22.73,5.3,19.2 +repvit_m2_3,224,1903.75,537.874,1024,23.69,4.57,26.21 +ecaresnet101d,224,1900.31,538.848,1024,44.57,8.08,17.07 +coat_lite_small,224,1894.79,540.417,1024,19.84,3.96,22.09 +vgg13_bn,224,1891.12,541.467,1024,133.05,11.33,12.25 +resnet61q,288,1890.53,541.637,1024,36.85,9.87,21.52 +nf_resnet101,224,1888.17,542.312,1024,44.55,8.01,16.23 +twins_pcpvt_base,224,1884.87,543.263,1024,43.83,6.68,25.25 +convnext_small,224,1878.13,545.211,1024,50.22,8.71,21.56 +cs3se_edgenet_x,256,1863.97,549.353,1024,50.72,11.53,12.94 +resnext50_32x4d,288,1862.16,549.888,1024,25.03,7.04,23.81 +wide_resnet101_2,176,1861.75,550.01,1024,126.89,14.31,13.18 +resmlp_36_224,224,1860.14,550.485,1024,44.69,8.91,16.33 +volo_d1_224,224,1858.16,551.072,1024,26.63,6.94,24.43 +eca_nfnet_l0,288,1849.16,553.756,1024,24.14,7.12,17.29 +nfnet_l0,288,1843.23,555.536,1024,35.07,7.13,17.29 +cs3sedarknet_x,288,1842.15,555.861,1024,35.4,10.6,14.37 +resnetaa50d,288,1841.55,556.042,1024,25.58,8.92,20.57 +resnetv2_50d_gn,224,1829.87,559.59,1024,25.57,4.38,11.92 +swin_s3_tiny_224,224,1829.63,559.665,1024,28.33,4.64,19.13 +sequencer2d_s,224,1819.49,562.785,1024,27.65,4.96,11.31 +nf_resnet50,288,1819.2,562.875,1024,25.56,6.88,18.37 +ecaresnet50t,288,1803.63,567.733,1024,25.57,7.14,19.55 +resnest50d,224,1801.69,568.346,1024,27.48,5.4,14.36 +seresnet50t,288,1799.96,568.892,1024,28.1,7.14,19.55 +densenet121,288,1797.45,569.685,1024,7.98,4.74,11.41 +rexnetr_200,288,1796.89,427.395,768,16.52,2.62,24.96 +ecaresnet50d,288,1794.35,570.669,1024,25.58,7.19,19.72 +res2net101_26w_4s,224,1792.61,571.216,1024,45.21,8.1,18.45 +maxxvitv2_nano_rw_256,256,1787.4,429.664,768,23.7,6.26,23.05 +resnetblur101d,224,1777.64,576.032,1024,44.57,9.12,17.94 +resnet50_gn,224,1775.58,576.701,1024,25.56,4.14,11.11 +cait_xxs24_224,224,1772.36,577.749,1024,11.96,2.53,20.29 +maxvit_nano_rw_256,256,1770.91,433.665,768,15.45,4.46,30.28 +mixer_b16_224,224,1768.46,579.024,1024,59.88,12.62,14.53 +maxvit_rmlp_nano_rw_256,256,1766.45,434.761,768,15.5,4.47,31.92 +vit_large_patch32_224,224,1766.19,579.765,1024,305.51,15.39,13.3 +vit_base_patch32_clip_384,384,1763.15,580.767,1024,88.3,13.06,16.5 +pit_b_224,224,1762.75,580.9,1024,73.76,12.42,32.94 +vit_base_patch32_384,384,1762.38,581.02,1024,88.3,13.06,16.5 +mobilevitv2_150,256,1758.79,436.653,768,10.59,4.09,24.11 +regnety_064,224,1758.25,582.385,1024,30.58,6.39,16.41 +res2net50_26w_8s,224,1757.77,582.547,1024,48.4,8.37,17.95 +pit_b_distilled_224,224,1754.34,583.684,1024,74.79,12.5,33.07 +rexnetr_300,224,1740.52,588.318,1024,34.81,3.39,22.16 +regnetv_064,224,1737.51,589.338,1024,30.58,6.39,16.41 +tresnet_l,224,1735.02,590.182,1024,55.99,10.9,11.9 +resnext50d_32x4d,288,1734.0,590.533,1024,25.05,7.44,25.13 +resnetv2_101x1_bit,224,1730.09,591.866,1024,44.54,8.04,16.23 +resnetv2_50d_frn,224,1729.39,592.103,1024,25.59,4.33,11.92 +vgg16,224,1729.39,592.106,1024,138.36,15.47,13.56 +seresnext101_32x4d,224,1725.09,593.58,1024,48.96,8.02,21.26 +legacy_seresnext101_32x4d,224,1721.49,594.81,1024,48.96,8.02,21.26 +res2net101d,224,1716.39,596.589,1024,45.23,8.35,19.25 +tf_efficientnet_b3,300,1715.74,596.818,1024,12.23,1.87,23.83 +hgnetv2_b5,288,1704.12,600.886,1024,39.57,10.84,18.5 +convnext_tiny_hnf,288,1697.32,603.29,1024,28.59,7.39,22.21 +vitamin_small_224,224,1687.84,606.683,1024,22.03,5.92,26.38 +ese_vovnet39b_evos,224,1686.51,607.159,1024,24.58,7.07,6.74 +regnety_080,224,1686.26,607.249,1024,39.18,8.0,17.97 +coatnet_0_224,224,1678.81,304.968,512,25.04,4.58,24.01 +convnextv2_tiny,224,1674.96,611.347,1024,28.64,4.47,13.44 +regnetz_b16_evos,224,1670.61,612.94,1024,9.74,1.43,9.95 +resnet101d,256,1668.7,613.64,1024,44.57,10.55,22.25 +botnet50ts_256,256,1666.23,307.27,512,22.74,5.54,22.23 +fastvit_mci0,256,1665.78,614.716,1024,11.41,2.42,18.29 +efficientnet_b3,320,1664.16,615.315,1024,12.23,2.01,26.52 +maxvit_tiny_rw_224,224,1656.79,463.537,768,29.06,5.11,33.11 +seresnext50_32x4d,288,1655.64,618.482,1024,27.56,7.04,23.82 +hiera_small_abswin_256,256,1653.07,619.442,1024,34.36,8.29,26.38 +davit_small,224,1646.59,466.407,768,49.75,8.8,30.49 +inception_next_small,224,1645.81,622.174,1024,49.37,8.36,19.27 +nextvit_large,224,1645.06,622.459,1024,57.87,10.78,28.99 +coatnet_rmlp_1_rw_224,224,1642.36,623.482,1024,41.69,7.85,35.47 +seresnetaa50d,288,1641.68,623.736,1024,28.11,8.92,20.59 +swinv2_cr_tiny_224,224,1640.6,624.15,1024,28.33,4.66,28.45 +efficientvit_b3,224,1640.13,624.331,1024,48.65,3.99,26.9 +resnetv2_152,224,1638.56,624.928,1024,60.19,11.55,22.56 +legacy_xception,299,1625.38,472.494,768,22.86,8.4,35.83 +efficientnet_el,300,1618.59,632.638,1024,10.59,8.0,30.7 +resnetblur50,288,1611.97,635.236,1024,25.56,8.52,19.87 +swinv2_cr_tiny_ns_224,224,1611.34,635.485,1024,28.33,4.66,28.45 +efficientnet_el_pruned,300,1610.86,635.676,1024,10.59,8.0,30.7 +pvt_v2_b3,224,1608.94,636.429,1024,45.24,6.92,37.7 +resnet152,224,1606.24,637.501,1024,60.19,11.56,22.56 +regnety_040_sgn,224,1600.6,639.748,1024,20.65,4.03,12.29 +hiera_base_224,224,1599.47,640.202,1024,51.52,9.4,30.42 +hgnet_small,288,1591.36,482.595,768,24.36,14.09,14.53 +vit_small_resnet50d_s16_224,224,1590.51,643.806,1024,57.53,13.48,24.82 +cs3edgenet_x,288,1584.3,646.332,1024,47.82,14.59,16.36 +vit_base_patch16_siglip_gap_224,224,1583.21,646.778,1024,85.8,17.49,23.75 +mvitv2_tiny,224,1582.77,646.956,1024,24.17,4.7,21.16 +mixer_l32_224,224,1581.12,647.634,1024,206.94,11.27,19.86 +deit_base_patch16_224,224,1579.88,648.139,1024,86.57,17.58,23.9 +halo2botnet50ts_256,256,1579.41,648.334,1024,22.64,5.02,21.78 +deit_base_distilled_patch16_224,224,1578.79,648.588,1024,87.34,17.68,24.05 +resnetv2_152d,224,1578.66,648.638,1024,60.2,11.8,23.36 +regnety_032,288,1575.76,649.833,1024,19.44,5.29,18.61 +vit_base_patch16_224_miil,224,1573.4,650.81,1024,94.4,17.59,23.91 +vit_base_patch16_224,224,1572.08,651.357,1024,86.57,17.58,23.9 +resnet152c,224,1571.84,651.453,1024,60.21,11.8,23.36 +regnety_080_tv,224,1571.53,651.584,1024,39.38,8.51,19.73 +nf_seresnet101,224,1570.7,651.927,1024,49.33,8.02,16.27 +vit_base_patch16_clip_224,224,1570.62,651.96,1024,86.57,17.58,23.9 +nf_ecaresnet101,224,1570.49,652.017,1024,44.55,8.01,16.27 +tf_efficientnet_el,300,1566.79,653.554,1024,10.59,8.0,30.7 +vit_base_patch16_siglip_224,224,1566.04,653.867,1024,92.88,17.73,24.06 +mobilenetv4_conv_aa_large,384,1562.44,655.376,1024,32.59,7.07,32.29 +vit_base_mci_224,224,1562.31,655.429,1024,86.35,17.73,24.65 +ese_vovnet99b,224,1559.84,656.466,1024,63.2,16.51,11.27 +vit_base_patch16_gap_224,224,1554.04,658.919,1024,86.57,17.49,25.59 +caformer_s18,224,1550.47,660.434,1024,26.34,4.13,19.39 +rexnet_300,224,1550.16,660.567,1024,34.71,3.44,22.4 +hrnet_w32,224,1547.9,661.528,1024,41.23,8.97,22.02 +efficientvit_l2,256,1547.32,661.778,1024,63.71,9.09,25.49 +repvgg_b2g4,224,1545.72,662.466,1024,61.76,12.63,12.9 +densenetblur121d,288,1544.46,663.005,1024,8.0,5.14,13.06 +resnet152d,224,1541.31,664.361,1024,60.21,11.8,23.36 +convnextv2_nano,288,1537.16,499.612,768,15.62,4.06,13.84 +beit_base_patch16_224,224,1536.2,666.568,1024,86.53,17.58,23.9 +vgg16_bn,224,1534.81,667.174,1024,138.37,15.5,13.56 +rdnet_small,224,1534.32,667.385,1024,50.44,8.74,22.55 +nfnet_f0,256,1532.99,667.963,1024,71.49,12.62,18.05 +vit_medium_patch16_rope_reg1_gap_256,256,1531.11,668.785,1024,38.74,10.63,22.26 +beitv2_base_patch16_224,224,1520.92,673.265,1024,86.53,17.58,23.9 +resnetblur50d,288,1515.28,675.773,1024,25.58,8.92,21.19 +vit_base_patch16_rpn_224,224,1513.98,676.348,1024,86.54,17.49,23.75 +edgenext_base,320,1511.01,677.677,1024,18.51,6.01,24.32 +convit_small,224,1505.94,679.963,1024,27.78,5.76,17.87 +dla169,224,1503.13,681.233,1024,53.39,11.6,20.2 +deit3_base_patch16_224,224,1496.23,684.377,1024,86.59,17.58,23.9 +vit_relpos_base_patch16_rpn_224,224,1482.24,690.829,1024,86.41,17.51,24.97 +vit_relpos_base_patch16_224,224,1477.58,693.015,1024,86.43,17.51,24.97 +xcit_tiny_12_p8_224,224,1475.49,693.994,1024,6.71,4.81,23.6 +skresnext50_32x4d,224,1475.1,694.179,1024,27.48,4.5,17.18 +vit_relpos_base_patch16_clsgap_224,224,1468.63,697.236,1024,86.43,17.6,25.12 +vit_small_patch16_384,384,1467.92,697.574,1024,22.2,15.52,50.78 +ecaresnet50t,320,1467.5,697.775,1024,25.57,8.82,24.13 +vit_relpos_base_patch16_cls_224,224,1467.04,697.995,1024,86.43,17.6,25.12 +mobilevitv2_175,256,1465.89,349.264,512,14.25,5.54,28.13 +vit_small_patch16_36x1_224,224,1461.67,700.555,1024,64.67,13.71,35.69 +gcvit_tiny,224,1457.43,702.592,1024,28.22,4.79,29.82 +convformer_s18,224,1455.15,703.698,1024,26.77,3.96,15.82 +vit_betwixt_patch16_reg1_gap_256,256,1449.59,706.394,1024,60.4,16.32,27.83 +resnetv2_50d_evos,224,1447.75,707.29,1024,25.59,4.33,11.92 +focalnet_small_srf,224,1446.06,708.111,1024,49.89,8.62,26.26 +resnet152s,224,1444.93,708.675,1024,60.32,12.92,24.96 +vgg19,224,1441.62,710.3,1024,143.67,19.63,14.86 +vit_betwixt_patch16_reg4_gap_256,256,1434.73,713.713,1024,60.4,16.52,28.24 +dpn92,224,1422.77,719.712,1024,37.67,6.54,18.21 +regnetx_120,224,1419.34,721.451,1024,46.11,12.13,21.37 +sequencer2d_m,224,1415.18,723.57,1024,38.31,6.55,14.26 +resnetv2_101,288,1410.42,726.012,1024,44.54,12.94,26.83 +vit_small_patch16_18x2_224,224,1410.32,726.067,1024,64.67,13.71,35.69 +dla102x2,224,1408.31,727.1,1024,41.28,9.34,29.91 +legacy_seresnet152,224,1405.68,728.463,1024,66.82,11.33,22.08 +poolformer_s36,224,1403.92,729.375,1024,30.86,5.0,15.82 +densenet161,224,1400.64,731.082,1024,28.68,7.79,11.06 +mobilenetv4_hybrid_large,384,1400.4,731.209,1024,37.76,7.77,34.52 +maxvit_tiny_tf_224,224,1398.62,549.103,768,30.92,5.6,35.78 +vit_base_patch16_clip_quickgelu_224,224,1393.62,734.768,1024,86.19,17.58,23.9 +flexivit_base,240,1387.12,738.211,1024,86.59,20.29,28.36 +seresnet152,224,1385.77,738.93,1024,66.82,11.57,22.61 +mobilenetv4_hybrid_medium,448,1381.87,741.016,1024,11.07,4.2,29.64 +poolformerv2_s24,224,1380.23,741.894,1024,21.34,3.42,10.68 +efficientformerv2_s0,224,1376.45,743.934,1024,3.6,0.41,5.3 +deit3_small_patch16_384,384,1372.21,746.232,1024,22.21,15.52,50.78 +repvgg_b2,224,1368.31,748.357,1024,89.02,20.45,12.9 +resnet101,288,1367.0,749.076,1024,44.55,12.95,26.83 +coatnet_1_rw_224,224,1362.79,751.389,1024,41.72,8.04,34.6 +coatnet_rmlp_1_rw2_224,224,1362.11,751.762,1024,41.72,8.11,40.13 +focalnet_small_lrf,224,1356.15,755.049,1024,50.34,8.74,28.61 +twins_pcpvt_large,224,1355.77,755.28,1024,60.99,9.84,35.82 +inception_v4,299,1352.8,756.936,1024,42.68,12.28,15.09 +maxxvit_rmlp_tiny_rw_256,256,1347.99,569.726,768,29.64,6.66,39.76 +regnetx_160,224,1347.67,759.82,1024,54.28,15.99,25.52 +regnety_120,224,1342.31,762.851,1024,51.82,12.14,21.38 +eca_nfnet_l1,256,1339.38,764.526,1024,41.41,9.62,22.04 +nf_regnet_b4,384,1338.66,764.931,1024,30.21,4.7,28.61 +fastvit_sa24,256,1331.86,768.836,1024,21.55,3.8,24.32 +xcit_small_24_p16_224,224,1319.85,775.833,1024,47.67,9.1,23.64 +vit_base_patch32_clip_448,448,1316.78,777.646,1024,88.34,17.93,23.9 +convnext_base,224,1309.55,781.937,1024,88.59,15.38,28.75 +hrnet_w30,224,1307.0,783.46,1024,37.71,8.15,21.21 +vit_base_patch16_xp_224,224,1302.22,786.337,1024,86.51,17.56,23.9 +mixnet_xxl,224,1300.49,590.535,768,23.96,2.04,23.43 +efficientnet_b4,320,1298.17,591.59,768,19.34,3.13,34.76 +vgg19_bn,224,1292.29,792.382,1024,143.68,19.66,14.86 +dm_nfnet_f0,256,1291.37,792.948,1024,71.49,12.62,18.05 +hrnet_w18_ssld,288,1289.07,794.359,1024,21.3,7.14,26.96 +efficientnetv2_s,384,1287.98,795.031,1024,21.46,8.44,35.77 +efficientvit_b3,256,1282.51,798.417,1024,48.65,5.2,35.01 +mobilevitv2_200,256,1277.35,400.82,512,18.45,7.22,32.15 +maxvit_tiny_rw_256,256,1275.72,601.999,768,29.07,6.74,44.35 +maxvit_rmlp_tiny_rw_256,256,1275.02,602.33,768,29.15,6.77,46.92 +efficientformerv2_s1,224,1269.21,806.789,1024,6.19,0.67,7.66 +hiera_base_plus_224,224,1266.71,808.384,1024,69.9,12.67,37.98 +twins_svt_base,224,1264.21,809.978,1024,56.07,8.59,26.33 +regnetz_d32,320,1259.82,812.801,1024,27.58,9.33,37.08 +regnetz_d8,320,1256.35,815.05,1024,23.37,6.19,37.08 +nest_small,224,1255.15,815.828,1024,38.35,10.35,40.04 +wide_resnet50_2,288,1252.17,817.768,1024,68.88,18.89,23.81 +repvgg_b3g4,224,1248.83,819.957,1024,83.83,17.89,15.1 +vit_mediumd_patch16_reg4_gap_256,256,1247.91,820.557,1024,64.11,17.87,37.57 +tf_efficientnetv2_s,384,1246.18,821.699,1024,21.46,8.44,35.77 +regnetz_040,320,1245.23,411.16,512,27.12,6.35,37.78 +nest_small_jx,224,1245.09,822.421,1024,38.35,10.35,40.04 +hgnetv2_b6,224,1242.11,824.392,1024,75.26,16.88,21.23 +regnetz_040_h,320,1240.62,412.685,512,28.94,6.43,37.94 +swin_small_patch4_window7_224,224,1233.81,829.94,1024,49.61,8.77,27.47 +vit_base_patch16_siglip_gap_256,256,1230.86,831.929,1024,85.84,23.13,33.23 +efficientnetv2_rw_s,384,1225.62,835.482,1024,23.94,8.72,38.03 +efficientvit_l2,288,1223.9,836.657,1024,63.71,11.51,32.19 +resnetaa101d,288,1218.62,840.281,1024,44.57,15.07,29.03 +vit_base_patch16_siglip_256,256,1217.35,841.156,1024,92.93,23.44,33.63 +swinv2_tiny_window8_256,256,1216.18,841.967,1024,28.35,5.96,24.57 +dpn98,224,1216.04,842.062,1024,61.57,11.73,25.2 +resnext101_64x4d,224,1205.34,849.54,1024,83.46,15.52,31.21 +resnest50d_4s2x40d,224,1202.36,851.648,1024,30.42,4.4,17.94 +cs3se_edgenet_x,320,1198.22,854.587,1024,50.72,18.01,20.21 +cait_xxs36_224,224,1195.59,856.47,1024,17.3,3.77,30.34 +resnext101_32x8d,224,1193.92,857.665,1024,88.79,16.48,31.21 +seresnet101,288,1192.86,858.428,1024,49.33,12.95,26.87 +resnet152d,256,1185.38,863.846,1024,60.21,15.41,30.51 +mobilenetv4_conv_large,448,1178.81,868.664,1024,32.59,8.75,37.17 +wide_resnet101_2,224,1175.48,871.125,1024,126.89,22.8,21.23 +mvitv2_small,224,1171.19,874.316,1024,34.87,7.0,28.08 +resnet200,224,1170.95,874.489,1024,64.67,15.07,32.19 +tnt_s_patch16_224,224,1170.11,875.118,1024,23.76,5.24,24.37 +inception_resnet_v2,299,1170.1,875.124,1024,55.84,13.18,25.06 +davit_base,224,1167.76,657.658,768,87.95,15.51,40.66 +resnext101_32x4d,288,1162.51,880.842,1024,44.18,13.24,35.09 +maxvit_tiny_pm_256,256,1161.84,661.01,768,30.09,6.61,47.9 +convnext_small,288,1159.49,883.132,1024,50.22,14.39,35.65 +rexnetr_300,288,1159.12,441.698,512,34.81,5.59,36.61 +coat_tiny,224,1155.52,886.171,1024,5.5,4.35,27.2 +vit_base_patch16_reg4_gap_256,256,1154.66,886.829,1024,86.62,23.5,33.89 +resnetrs101,288,1154.54,886.919,1024,63.62,13.56,28.53 +samvit_base_patch16_224,224,1151.9,888.955,1024,86.46,17.54,24.54 +pvt_v2_b4,224,1149.9,890.501,1024,62.56,10.14,53.74 +mobilenetv4_conv_aa_large,448,1147.75,669.124,768,32.59,9.63,43.94 +coatnet_1_224,224,1146.63,446.514,512,42.23,8.7,39.0 +ecaresnet101d,288,1141.31,897.207,1024,44.57,13.35,28.19 +tresnet_xl,224,1138.85,899.141,1024,78.44,15.2,15.34 +regnety_160,224,1136.53,900.98,1024,83.59,15.96,23.04 +crossvit_base_240,240,1135.75,901.594,1024,105.03,21.22,36.33 +mvitv2_small_cls,224,1134.19,902.839,1024,34.87,7.04,28.17 +efficientnet_lite4,380,1132.94,677.871,768,13.01,4.04,45.66 +seresnext101_64x4d,224,1117.32,916.471,1024,88.23,15.53,31.25 +hiera_base_abswin_256,256,1113.82,919.348,1024,51.27,12.46,40.7 +vit_small_r26_s32_384,384,1112.62,920.336,1024,36.47,10.43,29.85 +eva02_base_patch16_clip_224,224,1111.45,921.303,1024,86.26,17.62,26.32 +seresnext101_32x8d,224,1107.29,924.747,1024,93.57,16.48,31.25 +nfnet_f1,224,1107.23,924.822,1024,132.63,17.87,22.94 +resnetv2_50d_gn,288,1101.48,929.642,1024,25.57,7.24,19.7 +convnext_tiny,384,1100.29,697.987,768,28.59,13.14,39.48 +vit_betwixt_patch16_rope_reg4_gap_256,256,1097.57,932.963,1024,60.23,16.52,28.24 +vit_large_r50_s32_224,224,1097.32,933.175,1024,328.99,19.58,24.41 +pvt_v2_b5,224,1091.19,938.413,1024,81.96,11.76,50.92 +inception_next_base,224,1084.8,943.94,1024,86.67,14.85,25.69 +efficientnetv2_m,320,1081.64,946.698,1024,54.14,11.01,39.97 +seresnext101d_32x8d,224,1078.94,949.057,1024,93.59,16.72,32.05 +resnet50_gn,288,1076.3,951.392,1024,25.56,6.85,18.37 +hrnet_w40,224,1068.64,958.191,1024,57.56,12.75,25.29 +resnetblur101d,288,1065.75,960.81,1024,44.57,15.07,29.65 +regnety_064,288,1059.2,966.744,1024,30.58,10.56,27.11 +hgnet_base,224,1056.26,727.083,768,71.58,25.14,15.47 +resnet101d,320,1056.02,969.668,1024,44.57,16.48,34.77 +tf_efficientnet_lite4,380,1054.8,728.091,768,13.01,4.04,45.66 +regnetz_e8,256,1047.73,977.344,1024,57.7,9.91,40.94 +regnetv_064,288,1046.59,978.402,1024,30.58,10.55,27.11 +xception41p,299,1040.78,737.896,768,26.91,9.25,39.86 +xcit_tiny_24_p16_384,384,1037.06,987.399,1024,12.12,6.87,34.29 +seresnext101_32x4d,288,1034.95,989.41,1024,48.96,13.25,35.12 +vit_small_patch8_224,224,1034.54,989.803,1024,21.67,22.44,80.84 +resnetrs152,256,1034.09,990.227,1024,86.62,15.59,30.83 +repvgg_b3,224,1033.07,991.212,1024,123.09,29.16,15.1 +seresnet152d,256,1030.66,993.53,1024,66.84,15.42,30.56 +rdnet_base,224,1021.45,751.859,768,87.45,15.4,31.14 +convnextv2_tiny,288,1017.72,754.62,768,28.64,7.39,22.21 +convnextv2_small,224,1015.43,1008.427,1024,50.32,8.71,21.56 +volo_d2_224,224,1012.98,1010.867,1024,58.68,14.34,41.34 +regnety_080,288,1011.46,1012.381,1024,39.18,13.22,29.69 +seresnextaa101d_32x8d,224,1009.83,1014.006,1024,93.59,17.25,34.16 +regnetz_b16_evos,288,1009.35,760.872,768,9.74,2.36,16.43 +vit_base_patch16_plus_240,240,1008.65,1015.202,1024,117.56,27.41,33.08 +efficientvit_b3,288,1004.87,1019.022,1024,48.65,6.58,44.2 +swinv2_cr_small_224,224,1004.71,1019.185,1024,49.7,9.07,50.27 +convnext_base,256,1004.59,1019.308,1024,88.59,20.09,37.55 +mobilenetv4_conv_aa_large,480,1000.68,767.464,768,32.59,11.05,50.45 +mobilenetv4_hybrid_large,448,998.89,768.844,768,37.76,10.74,48.61 +focalnet_base_srf,224,997.56,1026.494,1024,88.15,15.28,35.01 +swinv2_cr_small_ns_224,224,993.8,1030.379,1024,49.7,9.08,50.27 +vit_relpos_base_patch16_plus_240,240,992.65,1031.568,1024,117.38,27.3,34.33 +maxvit_rmlp_small_rw_224,224,992.28,773.964,768,64.9,10.75,49.3 +regnetz_c16_evos,256,990.18,775.606,768,13.49,2.48,16.57 +vit_base_r50_s16_224,224,987.34,1037.117,1024,97.89,21.66,35.28 +efficientformer_l7,224,979.35,1045.575,1024,82.23,10.17,24.45 +regnety_040_sgn,288,975.23,1050.001,1024,20.65,6.67,20.3 +poolformer_m36,224,970.83,1054.753,1024,56.17,8.8,22.02 +resnet152,288,962.0,1064.442,1024,60.19,19.11,37.28 +hrnet_w44,224,953.42,1073.994,1024,67.06,14.94,26.92 +tiny_vit_21m_384,384,953.33,537.052,512,21.23,13.77,77.83 +cait_s24_224,224,953.06,1074.42,1024,46.92,9.35,40.58 +eva02_small_patch14_336,336,952.08,1075.525,1024,22.13,15.48,54.33 +efficientnetv2_rw_m,320,945.61,1082.891,1024,53.24,12.72,47.14 +dm_nfnet_f1,224,944.0,1084.735,1024,132.63,17.87,22.94 +fastvit_sa36,256,943.99,1084.735,1024,31.53,5.64,34.61 +coat_lite_medium,224,942.72,1086.203,1024,44.57,9.81,40.06 +fastvit_mci1,256,940.13,1089.203,1024,21.54,4.72,32.84 +gmlp_b16_224,224,938.67,1090.888,1024,73.08,15.78,30.21 +resnet50x4_clip_gap,288,935.82,1094.209,1024,65.62,19.57,34.11 +focalnet_base_lrf,224,935.6,1094.47,1024,88.75,15.43,38.13 +xception41,299,934.86,821.502,768,26.97,9.28,39.86 +hrnet_w48_ssld,224,933.73,1096.663,1024,77.47,17.34,28.56 +hrnet_w48,224,931.73,1098.99,1024,77.47,17.34,28.56 +poolformerv2_s36,224,930.29,1100.715,1024,30.79,5.01,15.82 +nextvit_small,384,927.23,1104.355,1024,31.76,17.26,57.14 +vit_mediumd_patch16_rope_reg1_gap_256,256,926.86,1104.797,1024,63.95,17.65,37.02 +sequencer2d_l,224,919.78,1113.292,1024,54.3,9.74,22.12 +vit_medium_patch16_gap_384,384,912.37,1122.341,1024,39.03,26.08,67.54 +xcit_medium_24_p16_224,224,909.28,1126.152,1024,84.4,16.13,31.71 +coat_mini,224,907.22,1128.715,1024,10.34,6.82,33.68 +swin_s3_small_224,224,903.85,849.69,768,49.74,9.43,37.84 +nest_base,224,893.55,1145.979,1024,67.72,17.96,53.39 +levit_384_s8,224,892.81,573.461,512,39.12,9.98,35.86 +resnet50x4_clip,288,889.34,1151.399,1024,87.14,21.35,35.27 +efficientnet_b4,384,888.84,576.021,512,19.34,4.51,50.04 +maxvit_small_tf_224,224,888.54,576.215,512,68.93,11.66,53.17 +dpn131,224,887.08,1154.334,1024,79.25,16.09,32.97 +nest_base_jx,224,886.64,1154.915,1024,67.72,17.96,53.39 +swin_base_patch4_window7_224,224,882.78,1159.955,1024,87.77,15.47,36.63 +nf_regnet_b5,384,882.37,1160.502,1024,49.74,7.95,42.9 +crossvit_15_dagger_408,408,874.39,1171.086,1024,28.5,21.45,95.05 +resnet200d,256,873.65,1172.086,1024,64.69,20.0,43.09 +gcvit_small,224,870.49,1176.332,1024,51.09,8.57,41.61 +vit_base_patch16_rope_reg1_gap_256,256,869.32,1177.92,1024,86.43,23.22,33.39 +xcit_small_12_p16_384,384,868.03,1179.666,1024,26.25,14.14,36.51 +resnetv2_50d_evos,288,866.61,1181.605,1024,25.59,7.15,19.7 +convnextv2_nano,384,866.13,591.121,512,15.62,7.22,24.61 +resnest101e,256,860.16,1190.459,1024,48.28,13.38,28.66 +eca_nfnet_l1,320,858.93,1192.174,1024,41.41,14.92,34.42 +efficientnet_b3_gn,288,854.4,599.243,512,11.73,1.74,23.35 +tf_efficientnet_b4,380,850.07,602.294,512,19.34,4.49,49.49 +levit_conv_384_s8,224,845.53,605.525,512,39.12,9.98,35.86 +maxxvit_rmlp_small_rw_256,256,843.15,910.843,768,66.01,14.67,58.38 +twins_svt_large,224,838.5,1221.219,1024,99.27,15.15,35.1 +tresnet_m,448,836.12,1224.695,1024,31.39,22.99,29.21 +coatnet_2_rw_224,224,834.11,613.819,512,73.87,15.09,49.22 +seresnet152,288,833.68,1228.27,1024,66.82,19.11,37.34 +xception65p,299,833.2,921.738,768,39.82,13.91,52.48 +eva02_base_patch14_224,224,826.02,1239.671,1024,85.76,23.22,36.55 +caformer_s36,224,824.15,1242.486,1024,39.3,8.0,37.53 +mvitv2_base,224,821.61,1246.318,1024,51.47,10.16,40.5 +swinv2_base_window12_192,192,807.64,1267.884,1024,109.28,11.9,39.72 +regnety_120,288,807.16,951.468,768,51.82,20.06,35.34 +densenet264d,224,799.46,1280.857,1024,72.74,13.57,14.0 +mvitv2_base_cls,224,796.2,1286.102,1024,65.44,10.23,40.65 +xcit_nano_12_p8_384,384,796.01,1286.403,1024,3.05,6.34,46.08 +convnext_base,288,793.66,1290.212,1024,88.59,25.43,47.53 +dpn107,224,793.14,1291.05,1024,86.92,18.38,33.46 +coatnet_rmlp_2_rw_224,224,791.99,646.464,512,73.88,15.18,54.78 +swinv2_tiny_window16_256,256,787.77,649.922,512,28.35,6.68,39.02 +coatnet_2_224,224,782.4,490.788,384,74.68,16.5,52.67 +xcit_tiny_24_p8_224,224,775.98,1319.614,1024,12.11,9.21,45.39 +mobilenetv4_conv_aa_large,544,775.96,659.818,512,32.59,14.19,64.79 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+convnextv2_base,224,731.86,1049.374,768,88.72,15.38,28.75 +poolformer_m48,224,730.8,1401.19,1024,73.47,11.59,29.17 +swin_s3_base_224,224,729.12,1404.418,1024,71.13,13.69,48.26 +vit_so150m_patch16_reg4_gap_256,256,727.23,1408.073,1024,134.13,36.75,53.21 +vitamin_base_224,224,725.26,705.946,512,87.72,22.68,52.77 +swinv2_cr_base_224,224,723.86,1414.624,1024,87.88,15.86,59.66 +volo_d3_224,224,723.01,1416.293,1024,86.33,20.78,60.09 +resnext101_64x4d,288,722.92,1416.456,1024,83.46,25.66,51.59 +hrnet_w64,224,719.74,1422.726,1024,128.06,28.97,35.09 +vit_so150m_patch16_reg4_map_256,256,718.73,1424.714,1024,141.48,37.18,53.68 +efficientvit_l3,224,718.02,1426.124,1024,246.04,27.62,39.16 +swinv2_cr_base_ns_224,224,717.54,1427.086,1024,87.88,15.86,59.66 +convnext_large,224,705.3,1451.859,1024,197.77,34.4,43.13 +efficientnet_b3_g8_gn,288,703.78,727.494,512,14.25,2.59,23.35 +resnet200,288,703.42,1455.726,1024,64.67,24.91,53.21 +efficientvit_l2,384,702.19,729.139,512,63.71,20.45,57.01 +nextvit_base,384,698.47,1466.053,1024,44.82,24.64,73.95 +regnety_320,224,695.21,1472.933,1024,145.05,32.34,30.26 +efficientnet_b3_gn,320,695.09,736.585,512,11.73,2.14,28.83 +coat_small,224,692.64,1478.393,1024,21.69,12.61,44.25 +fastvit_mci2,256,685.53,1493.712,1024,35.82,7.91,43.34 +regnety_160,288,685.03,1121.108,768,83.59,26.37,38.07 +maxxvitv2_rmlp_base_rw_224,224,675.7,1136.584,768,116.09,24.2,62.77 +regnetz_e8,320,668.22,1149.315,768,57.7,15.46,63.94 +efficientformerv2_s2,224,667.74,1533.519,1024,12.71,1.27,11.77 +resnetrs152,320,662.28,1546.154,1024,86.62,24.34,48.14 +xcit_small_12_p8_224,224,660.86,1549.491,1024,26.21,18.69,47.21 +seresnext101_32x8d,288,658.56,1554.9,1024,93.57,27.24,51.63 +seresnet152d,320,657.78,1556.745,1024,66.84,24.09,47.72 +poolformerv2_m36,224,657.37,1557.708,1024,56.08,8.81,22.02 +convnext_small,384,656.34,1170.108,768,50.22,25.58,63.37 +regnetz_d8_evos,256,642.61,1593.488,1024,23.46,4.5,24.92 +resnext101_32x16d,224,642.37,1594.085,1024,194.03,36.27,51.18 +convnext_base,320,641.94,1196.37,768,88.59,31.39,58.68 +seresnext101d_32x8d,288,641.63,1595.896,1024,93.59,27.64,52.95 +regnetx_320,224,641.61,1196.979,768,107.81,31.81,36.3 +efficientnetv2_m,416,641.2,1596.999,1024,54.14,18.6,67.5 +gcvit_base,224,640.97,1597.568,1024,90.32,14.87,55.48 +davit_large,224,640.04,1199.916,768,196.81,34.6,60.99 +vit_betwixt_patch16_reg4_gap_384,384,630.92,1623.018,1024,60.6,39.71,85.28 +regnetz_c16_evos,320,628.83,814.205,512,13.49,3.86,25.88 +mobilevitv2_175,384,624.08,615.295,384,14.25,12.47,63.29 +volo_d1_384,384,619.82,1652.09,1024,26.78,22.75,108.55 +maxvit_rmlp_base_rw_224,224,618.76,1241.17,768,116.14,23.15,92.64 +nf_regnet_b5,456,610.35,838.851,512,49.74,11.7,61.95 +vit_large_patch32_384,384,606.96,1687.093,1024,306.63,45.31,43.86 +xception71,299,606.18,1266.939,768,42.34,18.09,69.92 +crossvit_18_dagger_408,408,605.96,1689.854,1024,44.61,32.47,124.87 +seresnextaa101d_32x8d,288,604.03,1695.268,1024,93.59,28.51,56.44 +eca_nfnet_l2,320,603.44,1696.93,1024,56.72,20.95,47.43 +caformer_m36,224,592.51,1728.235,1024,56.2,13.29,50.48 +levit_512_s8,224,591.37,432.884,256,74.05,21.82,52.28 +ecaresnet200d,288,590.0,1735.592,1024,64.69,25.31,54.59 +seresnet200d,288,589.38,1737.392,1024,71.86,25.32,54.6 +seresnet269d,256,588.18,1740.943,1024,113.67,26.59,53.6 +nfnet_f2,256,586.41,1746.208,1024,193.78,33.76,41.85 +convmixer_768_32,224,584.59,1751.632,1024,21.11,19.55,25.95 +efficientnet_b3_g8_gn,320,582.54,878.901,512,14.25,3.2,28.83 +resnetv2_50x3_bit,224,579.63,1324.974,768,217.32,37.06,33.34 +rdnet_large,224,573.58,892.627,512,186.27,34.74,46.67 +convnextv2_tiny,384,573.01,670.132,384,28.64,13.14,39.48 +hrnet_w48_ssld,288,572.81,1787.672,1024,77.47,28.66,47.21 +resnetrs270,256,572.18,1789.62,1024,129.86,27.06,55.84 +nextvit_large,384,560.44,1827.129,1024,57.87,32.03,90.76 +efficientnetv2_rw_m,416,559.01,1831.797,1024,53.24,21.49,79.62 +convformer_m36,224,558.16,1834.58,1024,57.05,12.89,42.05 +levit_conv_512_s8,224,557.74,458.984,256,74.05,21.82,52.28 +efficientvit_l3,256,556.67,1379.617,768,246.04,36.06,50.98 +mixer_l16_224,224,555.85,1842.224,1024,208.2,44.6,41.69 +resnet200d,320,554.61,1846.322,1024,64.69,31.25,67.33 +efficientnet_b5,416,550.64,929.82,512,30.39,8.27,80.68 +mobilevitv2_200,384,550.58,697.44,384,18.45,16.24,72.34 +vit_mediumd_patch16_reg4_gap_384,384,547.09,1871.701,1024,64.27,43.67,113.51 +nfnet_f1,320,543.04,1885.679,1024,132.63,35.97,46.77 +convnext_large_mlp,256,539.44,1898.249,1024,200.13,44.94,56.33 +maxvit_base_tf_224,224,535.21,956.621,512,119.47,24.04,95.01 +swinv2_base_window8_256,256,534.01,1917.55,1024,87.92,20.37,52.59 +caformer_s18,384,520.36,983.918,512,26.34,13.42,77.34 +xcit_large_24_p16_224,224,515.87,1985.0,1024,189.1,35.86,47.27 +deit_base_patch16_384,384,513.77,1993.08,1024,86.86,55.54,101.56 +deit_base_distilled_patch16_384,384,512.49,1998.091,1024,87.63,55.65,101.82 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+regnety_2560,384,30.14,3185.104,96,1282.6,747.83,296.49 +maxvit_xlarge_tf_512,512,29.4,1632.708,48,475.77,534.14,1413.22 +sam2_hiera_large,1024,24.86,1930.745,48,212.15,907.48,2190.34 +samvit_base_patch16,1024,23.82,671.744,16,89.67,486.43,1343.27 +efficientnet_l2,800,19.37,2478.227,48,480.31,479.12,1707.39 +tf_efficientnet_l2,800,19.15,2506.394,48,480.31,479.12,1707.39 +vit_giant_patch14_dinov2,518,17.69,7234.942,128,1136.48,1784.2,2757.89 +vit_giant_patch14_reg4_dinov2,518,17.67,10868.501,192,1136.48,1790.08,2771.21 +eva_giant_patch14_560,560,16.99,11301.565,192,1014.45,1906.76,2577.17 +swinv2_cr_giant_384,384,14.96,2138.945,32,2598.76,1450.71,1394.86 +cait_m48_448,448,14.26,8978.396,128,356.46,329.41,1708.23 +samvit_large_patch16,1024,10.89,1101.662,12,308.28,1493.86,2553.78 +vit_so400m_patch14_siglip_gap_896,896,10.82,8873.224,96,416.87,2731.49,8492.88 +samvit_huge_patch16,1024,6.7,1194.478,8,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt240-cu124-rtx4090.csv b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt240-cu124-rtx4090.csv new file mode 100644 index 0000000000000000000000000000000000000000..d592a8c8a596736d53f4e01b8df1e3eab3ce6130 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt240-cu124-rtx4090.csv @@ -0,0 +1,1442 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_vit,160,193591.5,2.637,512,0.37,0.04,0.48 +test_efficientnet,160,140505.88,3.636,512,0.36,0.06,0.55 +test_byobnet,160,139342.81,3.665,512,0.46,0.03,0.43 +tinynet_e,106,114149.3,4.476,512,2.04,0.03,0.69 +mobilenetv3_small_050,224,91713.66,5.573,512,1.59,0.03,0.92 +lcnet_035,224,74617.77,6.852,512,1.64,0.03,1.04 +mobilenetv3_small_075,224,64474.01,7.933,512,2.04,0.05,1.3 +lcnet_050,224,64449.16,7.935,512,1.88,0.05,1.26 +mobilenetv3_small_100,224,58931.45,8.678,512,2.54,0.06,1.42 +tinynet_d,152,53815.67,9.505,512,2.34,0.05,1.42 +tf_mobilenetv3_small_minimal_100,224,46935.54,10.899,512,2.04,0.06,1.41 +tf_mobilenetv3_small_075,224,46386.54,11.028,512,2.04,0.05,1.3 +mobilenetv4_conv_small,224,45334.09,11.283,512,3.77,0.19,1.97 +tf_mobilenetv3_small_100,224,43574.18,11.741,512,2.54,0.06,1.42 +efficientvit_m2,224,43163.92,11.852,512,4.19,0.2,1.47 +efficientvit_m1,224,41710.72,12.265,512,2.98,0.17,1.33 +lcnet_075,224,41149.58,12.43,512,2.36,0.1,1.99 +mnasnet_small,224,38517.24,13.282,512,2.03,0.07,2.16 +efficientvit_m0,224,37262.79,13.731,512,2.35,0.08,0.91 +efficientvit_m3,224,37259.36,13.732,512,6.9,0.27,1.62 +efficientvit_m4,224,36637.28,13.966,512,8.8,0.3,1.7 +resnet18,160,35956.09,14.229,512,11.69,0.93,1.27 +regnetx_002,224,35359.25,14.471,512,2.68,0.2,2.16 +resnet10t,176,35271.27,14.507,512,5.44,0.7,1.51 +ghostnet_050,224,33294.15,15.367,512,2.59,0.05,1.77 +mobilenetv4_conv_small,256,32870.69,15.566,512,3.77,0.25,2.57 +repghostnet_050,224,32631.77,15.681,512,2.31,0.05,2.02 +regnety_002,224,32330.37,15.824,512,3.16,0.2,2.17 +levit_128s,224,31791.92,16.095,512,7.78,0.31,1.88 +lcnet_100,224,31652.74,16.165,512,2.95,0.16,2.52 +vit_small_patch32_224,224,31104.08,16.451,512,22.88,1.15,2.5 +levit_conv_128s,224,30791.34,16.618,512,7.78,0.31,1.88 +mobilenetv2_035,224,30231.75,16.927,512,1.68,0.07,2.86 +efficientvit_m5,224,29243.03,17.498,512,12.47,0.53,2.41 +mnasnet_050,224,28381.86,18.03,512,2.22,0.11,3.07 +regnetx_004,224,26873.8,19.042,512,5.16,0.4,3.14 +regnetx_004_tv,224,26191.12,19.533,512,5.5,0.42,3.17 +tinynet_c,184,25374.27,20.162,512,2.46,0.11,2.87 +efficientvit_b0,224,24958.93,20.504,512,3.41,0.1,2.87 +repghostnet_058,224,24922.84,20.532,512,2.55,0.07,2.59 +pit_ti_224,224,24011.49,21.313,512,4.85,0.7,6.19 +pit_ti_distilled_224,224,23895.35,21.415,512,5.1,0.71,6.23 +semnasnet_050,224,23613.68,21.672,512,2.08,0.11,3.44 +mobilenetv2_050,224,23276.13,21.987,512,1.97,0.1,3.64 +mixer_s32_224,224,23222.09,22.038,512,19.1,1.0,2.28 +resnet34,160,22893.41,22.355,512,21.8,1.87,1.91 +gernet_s,224,22652.42,22.589,512,8.17,0.75,2.65 +cs3darknet_focus_s,256,22633.17,22.607,512,3.27,0.69,2.7 +resnet10t,224,21939.38,23.327,512,5.44,1.1,2.43 +levit_128,224,21210.51,24.129,512,9.21,0.41,2.71 +cs3darknet_s,256,21180.55,24.162,512,3.28,0.72,2.97 +levit_conv_128,224,20601.31,24.843,512,9.21,0.41,2.71 +vit_tiny_r_s16_p8_224,224,20597.39,24.845,512,6.34,0.44,2.06 +repghostnet_080,224,19998.72,25.59,512,3.28,0.1,3.22 +lcnet_150,224,19930.25,25.679,512,4.5,0.34,3.79 +tf_efficientnetv2_b0,192,19533.13,26.202,512,7.14,0.54,3.51 +levit_192,224,19368.21,26.425,512,10.95,0.66,3.2 +vit_medium_patch32_clip_224,224,19242.5,26.596,512,39.69,2.0,3.34 +mobilenetv3_large_075,224,19001.45,26.934,512,3.99,0.16,4.0 +levit_conv_192,224,18406.17,27.806,512,10.95,0.66,3.2 +resnet18,224,18247.43,28.048,512,11.69,1.82,2.48 +nf_regnet_b0,192,18063.21,28.334,512,8.76,0.37,3.15 +deit_tiny_patch16_224,224,17596.84,29.086,512,5.72,1.26,5.97 +vit_tiny_patch16_224,224,17589.76,29.098,512,5.72,1.26,5.97 +deit_tiny_distilled_patch16_224,224,17490.74,29.262,512,5.91,1.27,6.01 +regnety_004,224,17337.89,29.52,512,4.34,0.41,3.89 +mobilenetv3_rw,224,17271.2,29.634,512,5.48,0.23,4.41 +mnasnet_075,224,17012.7,30.083,512,3.17,0.23,4.77 +resnet14t,176,16949.17,30.199,512,10.08,1.07,3.61 +mobilenetv3_large_100,224,16891.88,30.3,512,5.48,0.23,4.41 +seresnet18,224,16704.11,30.636,512,11.78,1.82,2.49 +hardcorenas_a,224,16102.68,31.784,512,5.26,0.23,4.38 +legacy_seresnet18,224,16060.08,31.87,512,11.78,1.82,2.49 +tf_mobilenetv3_large_075,224,15808.54,32.378,512,3.99,0.16,4.0 +regnety_006,224,15808.03,32.374,512,6.06,0.61,4.33 +repghostnet_100,224,15795.72,32.403,512,4.07,0.15,3.98 +tinynet_b,188,15674.11,32.653,512,3.73,0.21,4.44 +hardcorenas_b,224,15581.81,32.845,512,5.18,0.26,5.09 +ghostnet_100,224,15502.44,33.013,512,5.18,0.15,3.55 +hardcorenas_c,224,15395.61,33.244,512,5.52,0.28,5.01 +resnet18d,224,15221.89,33.625,512,11.71,2.06,3.29 +vit_xsmall_patch16_clip_224,224,15198.95,33.676,512,8.28,1.79,6.65 +pit_xs_224,224,15188.8,33.697,512,10.62,1.4,7.71 +pit_xs_distilled_224,224,15115.79,33.86,512,11.0,1.41,7.76 +tf_efficientnetv2_b1,192,14975.34,34.18,512,8.14,0.76,4.59 +tf_mobilenetv3_large_minimal_100,224,14968.23,34.196,512,3.92,0.22,4.4 +hardcorenas_d,224,14805.72,34.57,512,7.5,0.3,4.93 +mobilenet_edgetpu_v2_xs,224,14739.85,34.726,512,4.46,0.7,4.8 +mobilenetv1_100,224,14689.46,34.843,512,4.23,0.58,5.04 +mobilenetv1_100h,224,14526.94,35.232,512,5.28,0.63,5.09 +mnasnet_100,224,14430.36,35.465,512,4.38,0.33,5.46 +repvgg_a0,224,14326.74,35.726,512,9.11,1.52,3.59 +ese_vovnet19b_slim_dw,224,14264.24,35.883,512,1.9,0.4,5.28 +mobilenetv4_conv_medium,224,14221.81,35.988,512,9.72,0.84,5.8 +tf_mobilenetv3_large_100,224,14215.1,36.006,512,5.48,0.23,4.41 +regnetx_008,224,14152.21,36.165,512,7.26,0.81,5.15 +levit_256,224,14076.58,36.361,512,18.89,1.13,4.23 +semnasnet_075,224,13892.08,36.844,512,2.91,0.23,5.54 +dla46_c,224,13792.44,37.109,512,1.3,0.58,4.5 +repghostnet_111,224,13783.41,37.136,512,4.54,0.18,4.38 +ese_vovnet19b_slim,224,13770.62,37.17,512,3.17,1.69,3.52 +tf_efficientnetv2_b0,224,13704.01,37.351,512,7.14,0.73,4.77 +vit_betwixt_patch32_clip_224,224,13660.67,37.47,512,61.41,3.09,4.17 +regnetx_006,224,13627.19,37.56,512,6.2,0.61,3.98 +mobilenetv2_075,224,13591.14,37.659,512,2.64,0.22,5.86 +spnasnet_100,224,13506.55,37.897,512,4.42,0.35,6.03 +levit_conv_256,224,13449.58,38.057,512,18.89,1.13,4.23 +mobilenet_edgetpu_100,224,13226.44,38.699,512,4.09,1.0,5.75 +xcit_nano_12_p16_224,224,13143.36,38.944,512,3.05,0.56,4.17 +convnext_atto,224,13136.13,38.966,512,3.7,0.55,3.81 +mobilenetv4_hybrid_medium_075,224,12839.02,39.865,512,7.31,0.66,5.65 +edgenext_xx_small,256,12753.52,40.134,512,1.33,0.26,3.33 +convnext_atto_ols,224,12710.59,40.269,512,3.7,0.58,4.11 +regnety_008,224,12684.45,40.35,512,6.26,0.81,5.25 +tinynet_a,192,12643.44,40.482,512,6.19,0.35,5.41 +levit_256d,224,12538.4,40.823,512,26.21,1.4,4.93 +hardcorenas_f,224,12519.6,40.883,512,8.2,0.35,5.57 +mobilenetv3_large_100,256,12500.8,40.947,512,5.48,0.29,5.75 +hardcorenas_e,224,12262.77,41.738,512,8.07,0.35,5.65 +regnety_008_tv,224,12155.65,42.107,512,6.43,0.84,5.42 +semnasnet_100,224,12053.2,42.467,512,3.89,0.32,6.23 +levit_conv_256d,224,12032.84,42.54,512,26.21,1.4,4.93 +fbnetc_100,224,12028.82,42.553,512,5.57,0.4,6.51 +ghostnet_130,224,11996.72,42.666,512,7.36,0.24,4.6 +regnetz_005,224,11901.9,43.006,512,7.12,0.52,5.86 +mobilenetv2_100,224,11768.03,43.497,512,3.5,0.31,6.68 +repghostnet_130,224,11593.04,44.15,512,5.48,0.25,5.24 +mobilenetv1_125,224,11535.69,44.372,512,6.27,0.89,6.3 +efficientnet_lite0,224,11509.61,44.474,512,4.65,0.4,6.74 +resnet34,224,11246.05,45.512,512,21.8,3.67,3.74 +convnext_femto,224,11240.65,45.537,512,5.22,0.79,4.57 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+selecsls42b,224,10223.02,50.071,512,32.46,2.98,4.62 +seresnet34,224,10212.95,50.12,512,21.96,3.67,3.74 +mobilenetv4_hybrid_medium,224,10180.15,50.283,512,11.07,0.98,6.84 +repvgg_a1,224,10156.41,50.4,512,14.09,2.64,4.74 +resnet50,160,10141.36,50.475,512,25.56,2.1,5.67 +resnet34d,224,10045.08,50.956,512,21.82,3.91,4.54 +repghostnet_150,224,10019.26,51.092,512,6.58,0.32,6.0 +mobilenet_edgetpu_v2_s,224,9959.13,51.393,512,5.99,1.21,6.6 +efficientnet_b0,224,9831.68,52.064,512,5.29,0.4,6.75 +edgenext_xx_small,288,9824.91,52.1,512,1.33,0.33,4.21 +legacy_seresnet34,224,9801.32,52.225,512,21.96,3.67,3.74 +tf_efficientnet_lite0,224,9765.44,52.418,512,4.65,0.4,6.74 +repvit_m1,224,9725.15,52.635,512,5.49,0.83,7.45 +crossvit_9_240,240,9657.56,53.001,512,8.55,1.85,9.52 +seresnet18,288,9580.24,53.432,512,11.78,3.01,4.11 +efficientnet_b1_pruned,240,9566.61,53.501,512,6.33,0.4,6.21 +vit_small_patch32_384,384,9550.92,53.596,512,22.92,3.45,8.25 +pvt_v2_b0,224,9469.13,54.056,512,3.67,0.57,7.99 +resnet50d,160,9423.52,54.321,512,25.58,2.22,6.08 +repvit_m0_9,224,9420.77,54.337,512,5.49,0.83,7.45 +dla34,224,9394.03,54.492,512,15.74,3.07,5.02 +pit_s_224,224,9385.23,54.542,512,23.46,2.88,11.56 +pit_s_distilled_224,224,9330.9,54.86,512,24.04,2.9,11.64 +mnasnet_140,224,9285.01,55.122,512,7.12,0.6,7.71 +crossvit_9_dagger_240,240,9245.69,55.364,512,8.78,1.99,9.97 +nf_regnet_b0,256,9175.43,55.788,512,8.76,0.64,5.58 +efficientvit_b1,224,9080.99,56.37,512,9.1,0.53,7.25 +rexnetr_100,224,9036.3,56.647,512,4.88,0.43,7.72 +visformer_tiny,224,9033.8,56.663,512,10.32,1.27,5.72 +selecsls60,224,8984.73,56.974,512,30.67,3.59,5.52 +rexnet_100,224,8980.27,57.002,512,4.8,0.41,7.44 +resnetaa34d,224,8953.98,57.169,512,21.82,4.43,5.07 +selecsls60b,224,8928.94,57.33,512,32.77,3.63,5.52 +mobilenetv2_110d,224,8887.12,57.601,512,4.52,0.45,8.71 +fbnetv3_d,224,8866.65,57.732,512,10.31,0.52,8.5 +levit_384,224,8827.05,57.993,512,39.13,2.36,6.26 +resnet18d,288,8808.21,58.116,512,11.71,3.41,5.43 +mobilevitv2_050,256,8703.38,58.817,512,1.37,0.48,8.04 +convnext_pico,224,8685.06,58.939,512,9.05,1.37,6.1 +mobilenetv1_125,256,8629.12,59.32,512,6.27,1.16,8.23 +vit_base_patch32_clip_quickgelu_224,224,8623.33,59.362,512,87.85,4.41,5.01 +tf_efficientnetv2_b1,240,8620.85,59.379,512,8.14,1.21,7.34 +cs3darknet_focus_m,288,8566.21,59.756,512,9.3,2.51,6.19 +tf_efficientnet_b0,224,8541.64,59.93,512,5.29,0.4,6.75 +seresnet50,160,8474.8,60.402,512,28.09,2.1,5.69 +resnetblur18,224,8463.51,60.482,512,11.69,2.34,3.39 +resnext50_32x4d,160,8436.12,60.679,512,25.03,2.17,7.35 +convnext_pico_ols,224,8425.05,60.76,512,9.06,1.43,6.5 +levit_conv_384,224,8372.39,61.141,512,39.13,2.36,6.26 +efficientnet_es_pruned,224,8329.12,61.458,512,5.44,1.81,8.73 +efficientnet_es,224,8327.35,61.471,512,5.44,1.81,8.73 +efficientnet_b0_g16_evos,224,8292.21,61.731,512,8.11,1.01,7.42 +semnasnet_140,224,8275.66,61.856,512,6.11,0.6,8.87 +mobilenetv4_conv_blur_medium,224,8246.17,62.078,512,9.72,1.22,8.58 +ese_vovnet19b_dw,224,8222.83,62.253,512,6.54,1.34,8.25 +resnet50,176,8183.47,62.553,512,25.56,2.62,6.92 +dla46x_c,224,8172.45,62.637,512,1.07,0.54,5.66 +cs3darknet_m,288,8120.14,63.039,512,9.31,2.63,6.69 +mobilenet_edgetpu_v2_m,224,8111.09,63.111,512,8.46,1.85,8.15 +hgnetv2_b1,224,8089.51,63.278,512,6.34,0.49,2.73 +resnet26,224,8074.23,63.401,512,16.0,2.36,7.35 +ecaresnet50t,160,8063.67,63.484,512,25.57,2.21,6.04 +vit_base_patch32_clip_256,256,8042.41,63.651,512,87.86,5.76,6.65 +resnetrs50,160,8011.18,63.898,512,35.69,2.29,6.2 +mobilenetv2_140,224,7973.79,64.198,512,6.11,0.6,9.57 +xcit_tiny_12_p16_224,224,7972.61,64.209,512,6.72,1.24,6.29 +mixer_b32_224,224,7946.63,64.415,512,60.29,3.24,6.29 +dla60x_c,224,7932.08,64.535,512,1.32,0.59,6.01 +fbnetv3_b,256,7894.93,64.838,512,8.6,0.55,9.1 +tiny_vit_5m_224,224,7865.63,65.083,512,12.08,1.28,11.25 +resmlp_12_224,224,7865.04,65.084,512,15.35,3.01,5.5 +convnext_atto,288,7834.17,65.338,512,3.7,0.91,6.3 +mixer_s16_224,224,7827.39,65.399,512,18.53,3.79,5.97 +edgenext_x_small,256,7821.76,65.447,512,2.34,0.54,5.93 +repvit_m1_0,224,7763.02,65.941,512,7.3,1.13,8.69 +tf_efficientnet_es,224,7753.47,66.024,512,5.44,1.81,8.73 +ghostnetv2_100,224,7711.41,66.38,512,6.16,0.18,4.55 +vit_small_patch16_224,224,7695.68,66.52,512,22.05,4.61,11.95 +deit_small_patch16_224,224,7692.21,66.546,512,22.05,4.61,11.95 +repvgg_b0,224,7686.96,66.595,512,15.82,3.41,6.15 +deit_small_distilled_patch16_224,224,7646.43,66.946,512,22.44,4.63,12.02 +skresnet18,224,7641.81,66.984,512,11.96,1.82,3.24 +darknet17,256,7605.93,67.302,512,14.3,3.26,7.18 +convnext_atto_ols,288,7572.12,67.606,512,3.7,0.96,6.8 +efficientnet_lite1,240,7533.54,67.95,512,5.42,0.62,10.14 +mobilenetv4_hybrid_medium,256,7461.42,68.608,512,11.07,1.29,9.01 +repvit_m2,224,7412.42,69.062,512,8.8,1.36,9.43 +mixnet_s,224,7370.06,69.451,512,4.13,0.25,6.25 +repghostnet_200,224,7359.51,69.557,512,9.8,0.54,7.96 +vit_pwee_patch16_reg1_gap_256,256,7350.46,69.642,512,15.25,4.37,15.87 +efficientnet_b0,256,7334.29,69.797,512,5.29,0.52,8.81 +efficientnet_blur_b0,224,7255.8,70.553,512,5.29,0.43,8.72 +gmixer_12_224,224,7249.78,70.61,512,12.7,2.67,7.26 +resnet26d,224,7173.64,71.361,512,16.01,2.6,8.15 +mobilenet_edgetpu_v2_l,224,7155.34,71.543,512,10.92,2.55,9.05 +repvit_m1_1,224,7120.93,71.88,512,8.8,1.36,9.43 +hgnetv2_b4,224,7066.56,72.433,512,19.8,2.75,6.7 +nf_regnet_b2,240,7056.07,72.547,512,14.31,0.97,7.23 +rexnetr_130,224,7042.44,72.689,512,7.61,0.68,9.81 +darknet21,256,7025.74,72.86,512,20.86,3.93,7.47 +eva02_tiny_patch14_224,224,7011.67,73.009,512,5.5,1.7,9.14 +gernet_l,256,7010.05,73.027,512,31.08,4.57,8.0 +mobilenetv4_conv_aa_medium,256,6992.95,73.204,512,9.72,1.58,10.3 +vit_tiny_r_s16_p8_384,384,6908.22,74.103,512,6.36,1.34,6.49 +efficientnet_b1,224,6906.68,74.116,512,7.79,0.59,9.36 +nf_regnet_b1,256,6856.88,74.654,512,10.22,0.82,7.27 +sedarknet21,256,6844.16,74.796,512,20.95,3.93,7.47 +deit3_small_patch16_224,224,6842.04,74.819,512,22.06,4.61,11.95 +vit_wee_patch16_reg1_gap_256,256,6834.55,74.901,512,13.42,3.83,13.9 +ecaresnet50d_pruned,224,6821.51,75.045,512,19.94,2.53,6.43 +vit_relpos_small_patch16_rpn_224,224,6816.29,75.1,512,21.97,4.59,13.05 +resnext50_32x4d,176,6815.65,75.11,512,25.03,2.71,8.97 +efficientvit_b1,256,6805.35,75.222,512,9.1,0.69,9.46 +vit_relpos_small_patch16_224,224,6794.12,75.345,512,21.98,4.59,13.05 +tf_mixnet_s,224,6784.77,75.452,512,4.13,0.25,6.25 +vit_srelpos_small_patch16_224,224,6772.14,75.593,512,21.97,4.59,12.16 +convnextv2_atto,224,6744.98,75.897,512,3.71,0.55,3.81 +convnext_femto,288,6744.7,75.901,512,5.22,1.3,7.56 +regnetz_005,288,6732.16,76.038,512,7.12,0.86,9.68 +flexivit_small,240,6705.34,76.346,512,22.06,5.35,14.18 +tiny_vit_11m_224,224,6677.76,76.659,512,20.35,2.04,13.49 +resnet101,160,6654.99,76.921,512,44.55,4.0,8.28 +tf_efficientnet_lite1,240,6635.79,77.145,512,5.42,0.62,10.14 +mobilenetv4_conv_medium,320,6583.91,77.754,512,9.72,1.71,11.84 +efficientnetv2_rw_t,224,6565.65,77.969,512,13.65,1.93,9.94 +convnext_femto_ols,288,6547.48,78.187,512,5.23,1.35,8.06 +rexnet_130,224,6526.53,78.43,512,7.56,0.68,9.71 +resnet34,288,6472.85,79.088,512,21.8,6.07,6.18 +hgnet_tiny,224,6467.95,79.144,512,14.74,4.54,6.36 +hgnetv2_b0,288,6455.26,79.302,512,6.0,0.54,3.51 +regnetz_b16,224,6447.12,79.403,512,9.72,1.45,9.95 +mobilenetv2_120d,224,6441.08,79.477,512,5.83,0.69,11.97 +regnetx_016,224,6438.32,79.508,512,9.19,1.62,7.93 +fbnetv3_d,256,6406.47,79.904,512,10.31,0.68,11.1 +nf_resnet26,224,6382.33,80.21,512,16.0,2.41,7.35 +repvgg_a2,224,6357.34,80.526,512,28.21,5.7,6.26 +convnext_nano,224,6328.61,80.891,512,15.59,2.46,8.37 +gmlp_ti16_224,224,6311.2,81.105,512,5.87,1.34,7.55 +cs3darknet_focus_l,256,6269.67,81.648,512,21.15,4.66,8.03 +efficientnet_cc_b0_4e,224,6251.35,81.891,512,13.31,0.41,9.42 +efficientnet_cc_b0_8e,224,6215.22,82.367,512,24.01,0.42,9.42 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+dpn48b,224,5891.72,86.887,512,9.13,1.69,8.92 +seresnet34,288,5881.41,87.035,512,21.96,6.07,6.18 +seresnext26ts,256,5849.56,87.514,512,10.39,2.43,10.52 +efficientnet_b1,240,5837.99,87.69,512,7.79,0.71,10.88 +resnet34d,288,5818.74,87.977,512,21.82,6.47,7.51 +efficientnet_lite2,260,5818.57,87.978,512,6.09,0.89,12.9 +eca_resnext26ts,256,5802.09,88.223,512,10.3,2.43,10.52 +regnety_016,224,5793.48,88.359,512,11.2,1.63,8.04 +gcresnext26ts,256,5763.43,88.824,512,10.48,2.43,10.53 +vit_tiny_patch16_384,384,5757.61,88.914,512,5.79,4.7,25.39 +efficientnet_b2_pruned,260,5702.14,89.779,512,8.31,0.73,9.13 +vovnet39a,224,5696.62,89.864,512,22.6,7.09,6.73 +convnextv2_femto,224,5691.98,89.939,512,5.23,0.79,4.57 +selecsls84,224,5690.74,89.959,512,50.95,5.9,7.57 +rexnet_150,224,5687.06,90.016,512,9.73,0.9,11.21 +tf_efficientnet_cc_b0_8e,224,5682.69,90.088,512,24.01,0.42,9.42 +ecaresnet101d_pruned,224,5665.08,90.365,512,24.88,3.48,7.69 +tf_efficientnet_cc_b0_4e,224,5643.92,90.706,512,13.31,0.41,9.42 +edgenext_small,256,5629.75,90.933,512,5.59,1.26,9.07 +hgnetv2_b2,224,5618.6,91.112,512,11.22,1.15,4.12 +cs3sedarknet_l,256,5560.82,92.057,512,21.91,4.86,8.56 +mobilevitv2_075,256,5556.74,92.127,512,2.87,1.05,12.06 +mobilenetv3_large_150d,256,5555.16,92.154,512,14.62,1.03,12.35 +resnet26t,256,5542.61,92.362,512,16.01,3.35,10.52 +ecaresnetlight,224,5507.77,92.947,512,30.16,4.11,8.42 +mobilenetv4_hybrid_large_075,256,5460.28,93.755,512,22.75,2.06,11.64 +ecaresnext26t_32x4d,224,5449.2,93.946,512,15.41,2.7,10.09 +seresnext26t_32x4d,224,5447.15,93.982,512,16.81,2.7,10.09 +ecaresnext50t_32x4d,224,5446.35,93.994,512,15.41,2.7,10.09 +tresnet_m,224,5412.07,94.592,512,31.39,5.75,7.31 +resnetv2_50,224,5404.87,94.714,512,25.55,4.11,11.11 +seresnext26d_32x4d,224,5399.24,94.813,512,16.81,2.73,10.19 +resnet101,176,5394.96,94.89,512,44.55,4.92,10.08 +ese_vovnet39b,224,5390.43,94.971,512,24.57,7.09,6.74 +eca_vovnet39b,224,5385.97,95.049,512,22.6,7.09,6.74 +hrnet_w18_small_v2,224,5368.46,95.35,512,15.6,2.62,9.65 +resnetaa34d,288,5316.61,96.29,512,21.82,7.33,8.38 +nf_regnet_b2,272,5315.32,96.311,512,14.31,1.22,9.27 +nf_regnet_b1,288,5308.87,96.43,512,10.22,1.02,9.2 +tf_efficientnet_b1,240,5299.34,96.602,512,7.79,0.71,10.88 +nf_ecaresnet26,224,5277.07,97.012,512,16.0,2.41,7.36 +nf_seresnet26,224,5268.95,97.161,512,17.4,2.41,7.36 +sam2_hiera_tiny,224,5256.88,97.382,512,26.85,4.91,17.12 +convnext_pico,288,5236.48,97.762,512,9.05,2.27,10.08 +wide_resnet50_2,176,5230.3,97.88,512,68.88,7.29,8.97 +efficientvit_b1,288,5209.57,98.268,512,9.1,0.87,11.96 +poolformer_s12,224,5207.23,98.311,512,11.92,1.82,5.53 +pvt_v2_b1,224,5204.63,98.361,512,14.01,2.12,15.39 +vit_small_resnet26d_224,224,5184.59,98.741,512,63.61,5.07,11.12 +vit_medium_patch16_clip_224,224,5164.35,99.127,512,38.59,8.0,15.93 +tf_efficientnet_lite2,260,5161.3,99.182,512,6.09,0.89,12.9 +efficientnet_b1,256,5151.08,99.381,512,7.79,0.77,12.22 +edgenext_small_rw,256,5138.37,99.63,512,7.83,1.58,9.51 +resnet50,224,5088.39,100.61,512,25.56,4.11,11.11 +convnext_pico_ols,288,5080.05,100.773,512,9.06,2.37,10.74 +dpn68,224,5063.18,101.109,512,12.61,2.35,10.47 +resnet32ts,256,5060.8,101.156,512,17.96,4.63,11.58 +dla60,224,5050.11,101.367,512,22.04,4.26,10.16 +efficientnet_em,240,5043.39,101.506,512,6.9,3.04,14.34 +levit_512,224,5027.38,101.83,512,95.17,5.64,10.22 +resnetv2_50t,224,5027.34,101.829,512,25.57,4.32,11.82 +resnet33ts,256,5009.72,102.19,512,19.68,4.76,11.66 +resnetv2_50d,224,4991.23,102.566,512,25.57,4.35,11.92 +mixnet_m,224,4990.08,102.58,512,5.01,0.36,8.19 +vgg11,224,4978.9,102.821,512,132.86,7.61,7.44 +mobilevit_xs,256,4974.99,102.897,512,2.32,1.05,16.33 +eca_botnext26ts_256,256,4972.2,102.961,512,10.59,2.46,11.6 +resnetblur18,288,4952.71,103.365,512,11.69,3.87,5.6 +repvit_m3,224,4952.4,103.372,512,10.68,1.89,13.94 +vit_little_patch16_reg1_gap_256,256,4899.59,104.486,512,22.52,6.27,18.06 +crossvit_small_240,240,4895.55,104.569,512,26.86,5.63,18.17 +regnetv_040,224,4894.19,104.6,512,20.64,4.0,12.29 +efficientnet_b2,256,4887.72,104.739,512,9.11,0.89,12.81 +dpn68b,224,4887.43,104.743,512,12.61,2.35,10.47 +resnest14d,224,4875.39,105.004,512,10.61,2.76,7.33 +cs3darknet_focus_l,288,4864.5,105.235,512,21.15,5.9,10.16 +coatnext_nano_rw_224,224,4863.57,105.26,512,14.7,2.47,12.8 +resnet50c,224,4858.01,105.38,512,25.58,4.35,11.92 +vovnet57a,224,4855.98,105.421,512,36.64,8.95,7.52 +vit_little_patch16_reg4_gap_256,256,4854.62,105.455,512,22.52,6.35,18.33 +ecaresnet26t,256,4847.81,105.603,512,16.01,3.35,10.53 +convnext_tiny,224,4844.53,105.672,512,28.59,4.47,13.44 +coatnet_pico_rw_224,224,4838.76,105.799,512,10.85,2.05,14.62 +regnety_040,224,4833.84,105.901,512,20.65,4.0,12.29 +ese_vovnet19b_dw,288,4833.57,105.914,512,6.54,2.22,13.63 +eca_resnet33ts,256,4814.31,106.335,512,19.68,4.76,11.66 +seresnet33ts,256,4808.69,106.463,512,19.78,4.76,11.66 +tf_mixnet_m,224,4798.12,106.693,512,5.01,0.36,8.19 +eca_halonext26ts,256,4798.11,106.697,512,10.76,2.44,11.46 +tf_efficientnet_em,240,4792.58,106.819,512,6.9,3.04,14.34 +resnet26,288,4751.95,107.731,512,16.0,3.9,12.15 +botnet26t_256,256,4750.9,107.757,512,12.49,3.32,11.98 +resnet50t,224,4740.57,107.989,512,25.57,4.32,11.82 +hgnetv2_b1,288,4740.36,107.996,512,6.34,0.82,4.51 +gcresnet33ts,256,4729.63,108.241,512,19.88,4.76,11.68 +resnet152,160,4727.44,108.291,512,60.19,5.9,11.51 +resnet50d,224,4716.19,108.549,512,25.58,4.35,11.92 +levit_512d,224,4713.6,108.608,512,92.5,5.85,11.3 +fbnetv3_g,240,4705.48,108.797,512,16.62,1.28,14.87 +cs3darknet_l,288,4693.2,109.078,512,21.16,6.16,10.83 +resnext26ts,288,4684.61,109.274,512,10.3,3.07,13.31 +bat_resnext26ts,256,4683.78,109.296,512,10.73,2.53,12.51 +ghostnetv2_160,224,4679.39,109.403,512,12.39,0.42,7.23 +levit_conv_512,224,4673.9,109.533,512,95.17,5.64,10.22 +resnetaa50,224,4670.85,109.603,512,25.56,5.15,11.64 +vit_relpos_medium_patch16_rpn_224,224,4656.87,109.924,512,38.73,7.97,17.02 +deit3_medium_patch16_224,224,4656.19,109.948,512,38.85,8.0,15.93 +vit_relpos_medium_patch16_224,224,4644.51,110.223,512,38.75,7.97,17.02 +vit_srelpos_medium_patch16_224,224,4622.27,110.755,512,38.74,7.96,16.21 +halonet26t,256,4618.99,110.833,512,12.48,3.19,11.69 +mobileone_s1,224,4610.41,111.041,512,4.83,0.86,9.67 +vit_relpos_medium_patch16_cls_224,224,4602.22,111.238,512,38.76,8.03,18.24 +hgnetv2_b3,224,4595.96,111.389,512,16.29,1.78,5.07 +ese_vovnet57b,224,4572.59,111.957,512,38.61,8.95,7.52 +crossvit_15_240,240,4566.76,112.099,512,27.53,5.81,19.77 +efficientnet_b3_pruned,300,4556.39,112.357,512,9.86,1.04,11.86 +convit_tiny,224,4550.29,112.508,512,5.71,1.26,7.94 +vit_small_r26_s32_224,224,4547.12,112.585,512,36.43,3.56,9.85 +coat_lite_tiny,224,4545.49,112.628,512,5.72,1.6,11.65 +seresnext26ts,288,4515.14,113.381,512,10.39,3.07,13.32 +rexnetr_200,224,4479.28,114.291,512,16.52,1.59,15.11 +eca_resnext26ts,288,4469.0,114.55,512,10.3,3.07,13.32 +hiera_tiny_224,224,4466.63,114.616,512,27.91,4.91,17.13 +gcresnext26ts,288,4454.09,114.939,512,10.48,3.07,13.33 +mobilenetv4_hybrid_medium,320,4442.05,115.247,512,11.07,2.05,14.36 +legacy_seresnet50,224,4439.83,115.307,512,28.09,3.88,10.6 +coatnet_nano_cc_224,224,4438.79,115.334,512,13.76,2.24,15.02 +coatnet_nano_rw_224,224,4405.71,116.2,512,15.14,2.41,15.41 +res2net50_48w_2s,224,4402.09,116.289,512,25.29,4.18,11.72 +levit_conv_512d,224,4399.04,116.376,512,92.5,5.85,11.3 +skresnet34,224,4398.47,116.391,512,22.28,3.67,5.13 +hgnet_small,224,4394.42,116.497,512,24.36,8.53,8.79 +crossvit_15_dagger_240,240,4382.25,116.812,512,28.21,6.13,20.43 +regnetx_032,224,4377.92,116.931,512,15.3,3.2,11.37 +vit_base_resnet26d_224,224,4364.66,117.293,512,101.4,6.97,13.16 +inception_v3,299,4356.9,117.497,512,23.83,5.73,8.97 +resnetaa50d,224,4354.22,117.574,512,25.58,5.39,12.44 +resnet50_clip_gap,224,4352.18,117.625,512,23.53,5.39,12.44 +cs3sedarknet_l,288,4319.09,118.528,512,21.91,6.16,10.83 +visformer_small,224,4314.89,118.648,512,40.22,4.88,11.43 +tiny_vit_21m_224,224,4303.48,118.951,512,33.22,4.29,20.08 +xcit_tiny_24_p16_224,224,4297.86,119.115,512,12.12,2.34,11.82 +coat_lite_mini,224,4294.71,119.204,512,11.01,2.0,12.25 +rexnet_200,224,4286.19,119.439,512,16.37,1.56,14.91 +seresnet50,224,4275.51,119.739,512,28.09,4.11,11.13 +vgg11_bn,224,4269.7,119.903,512,132.87,7.62,7.44 +resnet26d,288,4243.79,120.634,512,16.01,4.29,13.48 +repvit_m1_5,224,4236.5,120.842,512,14.64,2.31,15.7 +hgnetv2_b5,224,4234.4,120.895,512,39.57,6.56,11.19 +resnext50_32x4d,224,4219.51,121.328,512,25.03,4.26,14.4 +hgnetv2_b4,288,4217.35,121.378,512,19.8,4.54,11.08 +dla60x,224,4205.32,121.737,512,17.35,3.54,13.8 +convnextv2_pico,224,4204.11,121.769,512,9.07,1.37,6.1 +efficientvit_b2,224,4171.44,122.725,512,24.33,1.6,14.62 +resnet50s,224,4166.01,122.887,512,25.68,5.47,13.52 +haloregnetz_b,224,4138.03,123.716,512,11.68,1.97,11.94 +resnet50_clip,224,4125.54,124.093,512,38.32,6.14,12.98 +vit_medium_patch16_gap_240,240,4100.63,124.847,512,44.4,9.22,18.81 +tf_efficientnet_b2,260,4099.99,124.866,512,9.11,1.02,13.83 +fastvit_t8,256,4096.16,124.98,512,4.03,0.7,8.63 +mobilevitv2_100,256,4094.56,125.029,512,4.9,1.84,16.08 +cs3darknet_focus_x,256,4093.78,125.053,512,35.02,8.03,10.69 +twins_svt_small,224,4054.15,126.278,512,24.06,2.94,13.75 +davit_tiny,224,4047.96,126.469,512,28.36,4.54,18.89 +convnextv2_atto,288,4046.67,126.51,512,3.71,0.91,6.3 +nf_regnet_b3,288,4045.22,126.557,512,18.59,1.67,11.84 +ecaresnet50t,224,4041.51,126.672,512,25.57,4.32,11.83 +ecaresnet50d_pruned,288,4041.28,126.68,512,19.94,4.19,10.61 +efficientnet_b0_gn,224,4037.71,126.792,512,5.29,0.42,6.75 +resnetrs50,224,4033.27,126.931,512,35.69,4.48,12.14 +seresnet50t,224,4031.57,126.983,512,28.1,4.32,11.83 +efficientnet_b1,288,4029.31,127.056,512,7.79,0.97,15.46 +resnetv2_50x1_bit,224,4026.74,127.136,512,25.55,4.23,11.11 +resmlp_24_224,224,4020.57,127.334,512,30.02,5.96,10.91 +ecaresnet50d,224,4020.32,127.34,512,25.58,4.35,11.93 +nfnet_f0,192,4017.91,127.416,512,71.49,7.21,10.16 +eca_nfnet_l0,224,4007.54,127.747,512,24.14,4.35,10.47 +nfnet_l0,224,3992.6,128.226,512,35.07,4.36,10.47 +resnet32ts,288,3982.87,128.533,512,17.96,5.86,14.65 +cspresnet50,256,3967.99,129.011,512,21.62,4.54,11.5 +resnext50d_32x4d,224,3958.52,129.328,512,25.05,4.5,15.2 +efficientnet_cc_b1_8e,240,3942.69,129.849,512,39.72,0.75,15.44 +resnet33ts,288,3941.35,129.892,512,19.68,6.02,14.75 +convnext_tiny_hnf,224,3937.17,130.024,512,28.59,4.47,13.44 +cs3darknet_x,256,3921.43,130.548,512,35.05,8.38,11.35 +densenet121,224,3896.08,131.4,512,7.98,2.87,6.9 +regnety_032,224,3890.61,131.586,512,19.44,3.2,11.26 +efficientnet_b0_g8_gn,224,3878.78,131.986,512,6.56,0.66,6.75 +res2net50_26w_4s,224,3870.41,132.27,512,25.7,4.28,12.61 +repvgg_b1g4,224,3868.8,132.327,512,39.97,8.15,10.64 +res2next50,224,3866.11,132.415,512,24.67,4.2,13.71 +dla60_res2net,224,3861.83,132.566,512,20.85,4.15,12.34 +efficientnet_b2,288,3845.58,133.127,512,9.11,1.12,16.2 +dla60_res2next,224,3838.79,133.361,512,17.03,3.49,13.17 +cspresnet50w,256,3836.45,133.436,512,28.12,5.04,12.19 +mobilevit_s,256,3831.13,133.628,512,5.58,2.03,19.94 +resnet152,176,3829.91,133.671,512,60.19,7.22,13.99 +convnext_nano,288,3829.8,133.676,512,15.59,4.06,13.84 +regnetz_b16,288,3824.0,133.877,512,9.72,2.39,16.43 +nextvit_small,224,3822.2,133.942,512,31.76,5.81,18.44 +mobileone_s2,224,3813.62,134.241,512,7.88,1.34,11.55 +twins_pcpvt_small,224,3810.76,134.343,512,24.11,3.83,18.08 +lambda_resnet26rpt_256,256,3805.0,134.547,512,10.99,3.16,11.87 +cspresnet50d,256,3804.58,134.553,512,21.64,4.86,12.55 +res2net50_14w_8s,224,3794.91,134.892,512,25.06,4.21,13.28 +efficientvit_l1,224,3793.97,134.935,512,52.65,5.27,15.85 +maxvit_pico_rw_256,256,3785.35,135.245,512,7.46,1.83,22.3 +regnetz_c16,256,3784.6,135.273,512,13.46,2.51,16.57 +efficientnetv2_rw_t,288,3781.16,135.396,512,13.65,3.19,16.42 +seresnet33ts,288,3779.91,135.437,512,19.78,6.02,14.76 +coatnet_rmlp_nano_rw_224,224,3777.49,135.526,512,15.15,2.62,20.34 +maxvit_rmlp_pico_rw_256,256,3774.75,135.625,512,7.52,1.85,24.86 +regnetx_040,224,3770.28,135.785,512,22.12,3.99,12.2 +resnetblur50,224,3766.24,135.931,512,25.56,5.16,12.02 +hgnet_tiny,288,3750.33,136.507,512,14.74,7.51,10.51 +seresnetaa50d,224,3749.26,136.547,512,28.11,5.4,12.46 +gcresnext50ts,256,3748.73,136.567,512,15.67,3.75,15.46 +gcresnet50t,256,3737.26,136.986,512,25.9,5.42,14.67 +tf_efficientnetv2_b3,300,3732.39,137.164,512,14.36,3.04,15.74 +gcresnet33ts,288,3730.72,137.226,512,19.88,6.02,14.78 +mobilenetv4_conv_large,320,3729.46,137.261,512,32.59,4.47,18.97 +gcvit_xxtiny,224,3724.74,137.446,512,12.0,2.14,15.36 +xcit_nano_12_p16_384,384,3719.39,137.643,512,3.05,1.64,12.15 +mixnet_l,224,3706.35,138.118,512,7.33,0.58,10.84 +gmixer_24_224,224,3700.37,138.35,512,24.72,5.28,14.45 +tf_efficientnet_cc_b1_8e,240,3698.23,138.433,512,39.72,0.75,15.44 +mobileone_s0,224,3695.13,138.546,512,5.29,1.09,15.48 +hiera_small_224,224,3688.01,138.815,512,35.01,6.42,20.75 +inception_next_tiny,224,3669.77,139.505,512,28.06,4.19,11.98 +darknet53,256,3656.81,139.987,512,41.61,9.31,12.39 +eca_resnet33ts,288,3656.44,140.002,512,19.68,6.02,14.76 +resnet51q,256,3652.14,140.176,512,35.7,6.38,16.55 +coatnet_0_rw_224,224,3650.72,140.225,512,27.44,4.43,18.73 +legacy_seresnext50_32x4d,224,3646.33,140.404,512,27.56,4.26,14.42 +res2net50d,224,3644.05,140.488,512,25.72,4.52,13.41 +seresnext50_32x4d,224,3641.64,140.579,512,27.56,4.26,14.42 +vit_medium_patch16_gap_256,256,3640.49,140.628,512,38.86,10.59,22.15 +gc_efficientnetv2_rw_t,288,3639.71,140.657,512,13.68,3.2,16.45 +vit_base_patch32_384,384,3635.68,140.814,512,88.3,13.06,16.5 +vit_base_patch32_clip_384,384,3633.18,140.91,512,88.3,13.06,16.5 +resnetrs101,192,3609.77,141.824,512,63.62,6.04,12.7 +darknetaa53,256,3608.53,141.868,512,36.02,7.97,12.39 +cs3sedarknet_x,256,3607.12,141.918,512,35.4,8.38,11.35 +tf_mixnet_l,224,3591.49,142.547,512,7.33,0.58,10.84 +edgenext_small,320,3557.62,143.904,512,5.59,1.97,14.16 +resnetblur50d,224,3555.32,143.996,512,25.58,5.4,12.82 +vit_base_r26_s32_224,224,3550.43,144.195,512,101.38,6.81,12.36 +convnext_nano_ols,288,3546.6,144.351,512,15.65,4.38,15.5 +mobilenetv3_large_150d,320,3539.35,144.647,512,14.62,1.61,19.29 +vit_medium_patch16_reg1_gap_256,256,3508.51,145.916,512,38.88,10.63,22.26 +vgg13,224,3498.85,146.321,512,133.05,11.31,12.25 +resnet26t,320,3498.14,146.349,512,16.01,5.24,16.44 +resnest26d,224,3480.3,147.101,512,17.07,3.64,9.97 +vit_medium_patch16_reg4_gap_256,256,3477.69,147.213,512,38.88,10.76,22.6 +vit_large_patch32_224,224,3463.8,147.8,512,305.51,15.39,13.3 +resnetv2_101,224,3460.77,147.93,512,44.54,7.83,16.23 +xcit_small_12_p16_224,224,3440.46,148.803,512,26.25,4.82,12.58 +hieradet_small,256,3437.33,148.94,512,34.72,8.51,27.76 +convnextv2_femto,288,3432.62,149.144,512,5.23,1.3,7.56 +resnet61q,256,3430.48,149.237,512,36.85,7.8,17.01 +efficientnet_lite3,300,3422.52,149.581,512,8.2,1.65,21.85 +densenetblur121d,224,3387.08,151.149,512,8.0,3.11,7.9 +edgenext_base,256,3353.12,152.68,512,18.51,3.85,15.58 +resnet101,224,3320.94,154.16,512,44.55,7.83,16.23 +vit_base_resnet50d_224,224,3313.04,154.527,512,110.97,8.73,16.92 +cspresnext50,256,3309.23,154.7,512,20.57,4.05,15.86 +resnet50_mlp,256,3304.29,154.934,512,26.65,7.05,16.25 +crossvit_18_240,240,3297.72,155.242,512,43.27,9.05,26.26 +resnetv2_101d,224,3282.49,155.964,512,44.56,8.07,17.04 +wide_resnet50_2,224,3280.9,156.042,512,68.88,11.43,14.4 +hgnetv2_b2,288,3279.23,156.12,512,11.22,1.89,6.8 +edgenext_small_rw,320,3277.42,156.207,512,7.83,2.46,14.85 +dla102,224,3273.77,156.379,512,33.27,7.19,14.18 +regnetx_080,224,3273.2,156.408,512,39.57,8.02,14.06 +focalnet_tiny_srf,224,3262.86,156.903,512,28.43,4.42,16.32 +cs3edgenet_x,256,3259.76,157.05,512,47.82,11.53,12.92 +densenet169,224,3257.61,157.156,512,14.15,3.4,7.3 +repvgg_b1,224,3252.46,157.405,512,57.42,13.16,10.64 +ecaresnetlight,288,3245.31,157.753,512,30.16,6.79,13.91 +coatnet_bn_0_rw_224,224,3244.97,157.769,512,27.44,4.67,22.04 +hrnet_w18_ssld,224,3243.03,157.865,512,21.3,4.32,16.31 +fbnetv3_g,288,3240.2,158.003,512,16.62,1.77,21.09 +nf_regnet_b3,320,3237.1,158.154,512,18.59,2.05,14.61 +seresnext26t_32x4d,288,3235.31,158.238,512,16.81,4.46,16.68 +coatnet_rmlp_0_rw_224,224,3235.0,158.255,512,27.45,4.72,24.89 +mobilevitv2_125,256,3226.23,158.687,512,7.48,2.86,20.1 +hrnet_w18,224,3222.49,158.851,512,21.3,4.32,16.31 +resnet101c,224,3220.87,158.948,512,44.57,8.08,17.04 +eva02_small_patch14_224,224,3219.95,158.996,512,21.62,6.14,18.28 +dm_nfnet_f0,192,3211.06,159.436,512,71.49,7.21,10.16 +seresnext26d_32x4d,288,3205.61,159.698,512,16.81,4.51,16.85 +pit_b_224,224,3203.54,159.809,512,73.76,12.42,32.94 +ese_vovnet39b,288,3197.44,160.116,512,24.57,11.71,11.13 +resnetv2_50,288,3191.29,160.421,512,25.55,6.79,18.37 +pit_b_distilled_224,224,3189.97,160.488,512,74.79,12.5,33.07 +nf_seresnet50,224,3183.77,160.801,512,28.09,4.21,11.13 +nf_ecaresnet50,224,3182.04,160.888,512,25.56,4.21,11.13 +poolformerv2_s12,224,3173.35,161.331,512,11.89,1.83,5.53 +crossvit_18_dagger_240,240,3169.03,161.547,512,44.27,9.5,27.03 +efficientvit_b2,256,3168.98,161.551,512,24.33,2.09,19.03 +ecaresnet101d_pruned,288,3165.16,161.747,512,24.88,5.75,12.71 +wide_resnet101_2,176,3159.16,162.057,512,126.89,14.31,13.18 +resnet101d,224,3154.71,162.283,512,44.57,8.08,17.04 +mobileone_s3,224,3137.93,163.149,512,10.17,1.94,13.85 +rdnet_tiny,224,3116.85,164.255,512,23.86,5.06,15.98 +ecaresnet50t,256,3090.6,165.649,512,25.57,5.64,15.45 +cs3darknet_x,288,3089.12,165.725,512,35.05,10.6,14.36 +pvt_v2_b2,224,3075.64,166.454,512,25.36,4.05,27.53 +ecaresnet26t,320,3068.22,166.857,512,16.01,5.24,16.44 +convnextv2_nano,224,3060.53,167.274,512,15.62,2.46,8.37 +gmlp_s16_224,224,3054.3,167.617,512,19.42,4.42,15.1 +eva02_tiny_patch14_336,336,3045.61,168.099,512,5.76,4.68,27.16 +tf_efficientnet_lite3,300,3034.72,168.698,512,8.2,1.65,21.85 +nf_resnet50,256,3032.36,168.828,512,25.56,5.46,14.52 +resnest50d_1s4x24d,224,3022.48,169.381,512,25.68,4.43,13.57 +efficientnetv2_s,288,3014.62,169.825,512,21.46,4.75,20.13 +efficientvit_l2,224,3006.41,170.285,512,63.71,6.97,19.58 +sehalonet33ts,256,3003.76,170.438,512,13.69,3.55,14.7 +resnet50,288,3001.95,170.541,512,25.56,6.8,18.37 +rexnetr_300,224,2999.93,170.656,512,34.81,3.39,22.16 +resnetaa101d,224,2990.5,171.195,512,44.57,9.12,17.56 +resnet101_clip_gap,224,2990.13,171.215,512,42.52,9.11,17.56 +focalnet_tiny_lrf,224,2989.28,171.265,512,28.65,4.49,17.76 +mobilenetv4_hybrid_medium,384,2986.24,171.437,512,11.07,3.01,21.18 +convnext_small,224,2979.27,171.842,512,50.22,8.71,21.56 +skresnet50,224,2972.51,172.23,512,25.8,4.11,12.5 +nextvit_base,224,2953.28,173.349,512,44.82,8.29,23.71 +res2net50_26w_6s,224,2950.38,173.519,512,37.05,6.33,15.28 +convnext_tiny,288,2942.29,174.001,512,28.59,7.39,22.21 +vgg13_bn,224,2930.98,174.674,512,133.05,11.33,12.25 +gcresnet50t,288,2928.81,174.801,512,25.9,6.86,18.57 +regnetv_040,288,2924.99,175.029,512,20.64,6.6,20.3 +cs3se_edgenet_x,256,2921.53,175.231,512,50.72,11.53,12.94 +gcresnext50ts,288,2907.55,176.079,512,15.67,4.75,19.57 +vgg16,224,2904.86,176.241,512,138.36,15.47,13.56 +gcvit_xtiny,224,2901.49,176.449,512,19.98,2.93,20.26 +resnet101s,224,2898.69,176.618,512,44.67,9.19,18.64 +dpn68b,288,2892.89,176.97,512,12.61,3.89,17.3 +resnext101_32x8d,176,2887.72,177.288,512,88.79,10.33,19.37 +efficientnet_b3,288,2886.07,177.389,512,12.23,1.63,21.49 +resnet101_clip,224,2884.25,177.502,512,56.26,9.81,18.08 +resnet51q,288,2880.39,177.741,512,35.7,8.07,20.94 +tresnet_v2_l,224,2874.87,178.082,512,46.17,8.85,16.34 +regnety_040,288,2873.78,178.146,512,20.65,6.61,20.3 +darknet53,288,2865.69,178.639,512,41.61,11.78,15.68 +efficientnetv2_rw_s,288,2865.52,178.661,512,23.94,4.91,21.41 +vit_base_patch16_siglip_gap_224,224,2859.98,179.01,512,85.8,17.49,23.75 +fastvit_t12,256,2859.48,179.037,512,7.55,1.42,12.42 +vit_base_patch16_224_miil,224,2846.99,179.826,512,94.4,17.59,23.91 +deit_base_patch16_224,224,2846.84,179.834,512,86.57,17.58,23.9 +skresnet50d,224,2843.8,180.027,512,25.82,4.36,13.31 +vit_base_patch16_224,224,2843.0,180.077,512,86.57,17.58,23.9 +vit_base_patch16_clip_224,224,2840.98,180.206,512,86.57,17.58,23.9 +xcit_nano_12_p8_224,224,2838.89,180.339,512,3.05,2.16,15.71 +mixer_b16_224,224,2837.28,180.433,512,59.88,12.62,14.53 +vitamin_small_224,224,2831.28,180.823,512,22.03,5.92,26.38 +vit_base_patch16_siglip_224,224,2830.29,180.883,512,92.88,17.73,24.06 +darknetaa53,288,2829.22,180.948,512,36.02,10.08,15.68 +deit_base_distilled_patch16_224,224,2828.65,180.986,512,87.34,17.68,24.05 +legacy_seresnet101,224,2827.17,181.086,512,49.33,7.61,15.74 +regnetx_064,224,2812.17,182.051,512,26.21,6.49,16.37 +resnet50t,288,2809.9,182.199,512,25.57,7.14,19.53 +vit_base_mci_224,224,2809.18,182.246,512,86.35,17.73,24.65 +cs3sedarknet_x,288,2809.03,182.251,512,35.4,10.6,14.37 +nest_tiny,224,2808.71,182.275,512,17.06,5.83,25.48 +resnet50d,288,2794.35,183.212,512,25.58,7.19,19.7 +volo_d1_224,224,2792.28,183.348,512,26.63,6.94,24.43 +resnetaa50,288,2787.79,183.642,512,25.56,8.52,19.24 +maxxvit_rmlp_nano_rw_256,256,2787.18,183.665,512,16.78,4.37,26.05 +dla102x,224,2778.92,184.23,512,26.31,5.89,19.42 +vit_base_patch16_gap_224,224,2767.32,185.003,512,86.57,17.49,25.59 +sequencer2d_s,224,2764.98,185.16,512,27.65,4.96,11.31 +nest_tiny_jx,224,2762.24,185.343,512,17.06,5.83,25.48 +seresnet101,224,2762.13,185.343,512,49.33,7.84,16.27 +efficientformer_l3,224,2749.8,186.18,512,31.41,3.93,12.01 +resnext101_32x4d,224,2746.89,186.38,512,44.18,8.01,21.23 +swin_tiny_patch4_window7_224,224,2744.11,186.57,512,28.29,4.51,17.06 +cs3sedarknet_xdw,256,2728.51,187.612,512,21.6,5.97,17.18 +fastvit_sa12,256,2725.03,187.874,512,11.58,1.96,14.03 +ese_vovnet99b,224,2723.96,187.941,512,63.2,16.51,11.27 +fastvit_s12,256,2721.82,188.087,512,9.47,1.82,13.67 +resnet61q,288,2721.04,188.149,512,36.85,9.87,21.52 +twins_pcpvt_base,224,2714.36,188.612,512,43.83,6.68,25.25 +beit_base_patch16_224,224,2706.18,189.182,512,86.53,17.58,23.9 +vit_base_patch32_clip_448,448,2701.0,189.546,512,88.34,17.93,23.9 +resmlp_36_224,224,2696.43,189.864,512,44.69,8.91,16.33 +tresnet_l,224,2694.34,190.016,512,55.99,10.9,11.9 +beitv2_base_patch16_224,224,2693.77,190.055,512,86.53,17.58,23.9 +rexnetr_200,288,2689.11,190.383,512,16.52,2.62,24.96 +rexnet_300,224,2684.56,190.707,512,34.71,3.44,22.4 +pvt_v2_b2_li,224,2681.31,190.936,512,22.55,3.91,27.6 +maxxvitv2_nano_rw_256,256,2667.75,191.907,512,23.7,6.26,23.05 +mobilevitv2_150,256,2662.33,192.301,512,10.59,4.09,24.11 +ecaresnet101d,224,2660.63,192.423,512,44.57,8.08,17.07 +cspdarknet53,256,2655.45,192.786,512,27.64,6.57,16.81 +poolformer_s24,224,2646.59,193.442,512,21.39,3.41,10.68 +vit_base_patch16_rpn_224,224,2645.68,193.504,512,86.54,17.49,23.75 +cait_xxs24_224,224,2634.11,194.359,512,11.96,2.53,20.29 +mobilenetv4_conv_large,384,2634.1,194.358,512,32.59,6.43,27.31 +mixnet_xl,224,2620.78,195.34,512,11.9,0.93,14.57 +deit3_base_patch16_224,224,2617.49,195.593,512,86.59,17.58,23.9 +maxvit_tiny_rw_224,224,2616.24,195.687,512,29.06,5.11,33.11 +vit_relpos_base_patch16_rpn_224,224,2614.87,195.79,512,86.41,17.51,24.97 +vit_relpos_base_patch16_224,224,2608.2,196.291,512,86.43,17.51,24.97 +resnetaa50d,288,2606.94,196.383,512,25.58,8.92,20.57 +vit_relpos_base_patch16_clsgap_224,224,2596.29,197.191,512,86.43,17.6,25.12 +res2net101_26w_4s,224,2594.73,197.298,512,45.21,8.1,18.45 +resnetblur101d,224,2592.77,197.455,512,44.57,9.12,17.94 +vit_relpos_base_patch16_cls_224,224,2592.66,197.467,512,86.43,17.6,25.12 +hgnet_small,288,2591.82,197.53,512,24.36,14.09,14.53 +tf_efficientnetv2_s,300,2589.86,197.681,512,21.46,5.35,22.73 +resnetv2_101x1_bit,224,2583.09,198.195,512,44.54,8.04,16.23 +regnetz_d32,256,2582.87,198.213,512,27.58,5.98,23.74 +hgnetv2_b3,288,2571.48,199.079,512,16.29,2.94,8.38 +vit_betwixt_patch16_reg1_gap_256,256,2567.82,199.378,512,60.4,16.32,27.83 +nf_resnet101,224,2567.19,199.426,512,44.55,8.01,16.23 +regnetz_d8,256,2566.72,199.463,512,23.37,3.97,23.74 +sebotnet33ts_256,256,2559.67,200.012,512,13.7,3.89,17.46 +convnextv2_pico,288,2545.21,201.143,512,9.07,2.27,10.08 +vit_betwixt_patch16_reg4_gap_256,256,2540.19,201.546,512,60.4,16.52,28.24 +seresnet50,288,2537.63,201.746,512,28.09,6.8,18.39 +hgnetv2_b5,288,2531.16,202.26,512,39.57,10.84,18.5 +mixer_l32_224,224,2528.06,202.513,512,206.94,11.27,19.86 +cs3edgenet_x,288,2527.64,202.544,512,47.82,14.59,16.36 +davit_small,224,2525.76,202.697,512,49.75,8.8,30.49 +regnetz_040,256,2513.52,203.672,512,27.12,4.06,24.19 +vit_small_patch16_384,384,2513.48,203.688,512,22.2,15.52,50.78 +regnetz_040_h,256,2511.58,203.823,512,28.94,4.12,24.29 +xcit_tiny_12_p16_384,384,2508.59,204.083,512,6.72,3.64,18.26 +efficientvit_b2,288,2500.66,204.73,512,24.33,2.64,24.03 +densenet201,224,2499.62,204.817,512,20.01,4.34,7.85 +resnext50_32x4d,288,2498.32,204.925,512,25.03,7.04,23.81 +res2net101d,224,2492.38,205.404,512,45.23,8.35,19.25 +flexivit_base,240,2489.7,205.633,512,86.59,20.29,28.36 +vgg19,224,2485.75,205.96,512,143.67,19.63,14.86 +vit_small_resnet50d_s16_224,224,2485.28,206.0,512,57.53,13.48,24.82 +vgg16_bn,224,2469.22,207.339,512,138.37,15.5,13.56 +coat_lite_small,224,2466.91,207.531,512,19.84,3.96,22.09 +hiera_base_224,224,2456.41,208.411,512,51.52,9.4,30.42 +swin_s3_tiny_224,224,2454.84,208.551,512,28.33,4.64,19.13 +res2net50_26w_8s,224,2448.88,209.049,512,48.4,8.37,17.95 +regnetz_c16,320,2432.34,210.483,512,13.46,3.92,25.88 +eca_nfnet_l0,288,2420.75,211.492,512,24.14,7.12,17.29 +resnetv2_152,224,2415.11,211.982,512,60.19,11.55,22.56 +nfnet_l0,288,2412.74,212.191,512,35.07,7.13,17.29 +nextvit_large,224,2408.12,212.596,512,57.87,10.78,28.99 +repvit_m2_3,224,2406.51,212.743,512,23.69,4.57,26.21 +regnety_064,224,2405.63,212.821,512,30.58,6.39,16.41 +ecaresnet50t,288,2404.89,212.887,512,25.57,7.14,19.55 +regnetv_064,224,2402.43,213.102,512,30.58,6.39,16.41 +resnet101d,256,2401.57,213.179,512,44.57,10.55,22.25 +seresnet50t,288,2398.87,213.42,512,28.1,7.14,19.55 +halonet50ts,256,2395.15,213.751,512,22.73,5.3,19.2 +nf_regnet_b4,320,2392.51,213.987,512,30.21,3.29,19.88 +ecaresnet50d,288,2392.47,213.991,512,25.58,7.19,19.72 +convnext_tiny_hnf,288,2383.27,214.808,512,28.59,7.39,22.21 +regnety_080,224,2362.59,216.697,512,39.18,8.0,17.97 +nf_resnet50,288,2360.57,216.882,512,25.56,6.88,18.37 +tf_efficientnet_b3,300,2360.52,216.883,512,12.23,1.87,23.83 +seresnext101_32x4d,224,2356.8,217.226,512,48.96,8.02,21.26 +resnext50d_32x4d,288,2354.66,217.422,512,25.05,7.44,25.13 +legacy_seresnext101_32x4d,224,2353.24,217.559,512,48.96,8.02,21.26 +resnet152,224,2347.77,218.062,512,60.19,11.56,22.56 +vit_small_patch16_36x1_224,224,2346.24,218.206,512,64.67,13.71,35.69 +resnest50d,224,2340.99,218.697,512,27.48,5.4,14.36 +mobileone_s4,224,2340.57,218.737,512,14.95,3.04,17.74 +repvgg_b2,224,2337.11,219.056,512,89.02,20.45,12.9 +hiera_small_abswin_256,256,2334.48,219.308,512,34.36,8.29,26.38 +hrnet_w32,224,2333.41,219.405,512,41.23,8.97,22.02 +efficientnet_b3,320,2331.82,219.556,512,12.23,2.01,26.52 +vit_base_patch16_clip_quickgelu_224,224,2329.86,219.738,512,86.19,17.58,23.9 +resnetv2_152d,224,2328.25,219.891,512,60.2,11.8,23.36 +efficientvit_l2,256,2320.46,220.631,512,63.71,9.09,25.49 +resnetv2_50d_gn,224,2319.35,220.732,512,25.57,4.38,11.92 +inception_v4,299,2318.78,220.781,512,42.68,12.28,15.09 +regnety_032,288,2316.01,221.053,512,19.44,5.29,18.61 +resnet152c,224,2297.8,222.806,512,60.21,11.8,23.36 +repvgg_b2g4,224,2294.12,223.163,512,61.76,12.63,12.9 +densenet121,288,2267.9,225.743,512,7.98,4.74,11.41 +vit_base_patch16_xp_224,224,2265.93,225.941,512,86.51,17.56,23.9 +legacy_xception,299,2265.86,225.947,512,22.86,8.4,35.83 +nfnet_f0,256,2265.35,225.998,512,71.49,12.62,18.05 +resnet152d,224,2264.89,226.044,512,60.21,11.8,23.36 +coatnet_rmlp_1_rw_224,224,2261.48,226.371,512,41.69,7.85,35.47 +mobilevitv2_175,256,2260.07,226.526,512,14.25,5.54,28.13 +pvt_v2_b3,224,2255.37,227.0,512,45.24,6.92,37.7 +deit3_small_patch16_384,384,2253.41,227.196,512,22.21,15.52,50.78 +xception41p,299,2252.41,227.29,512,26.91,9.25,39.86 +seresnetaa50d,288,2250.46,227.494,512,28.11,8.92,20.59 +efficientnet_el,300,2247.58,227.785,512,10.59,8.0,30.7 +vit_medium_patch16_rope_reg1_gap_256,256,2246.95,227.851,512,38.74,10.63,22.26 +efficientnet_el_pruned,300,2246.64,227.882,512,10.59,8.0,30.7 +ese_vovnet39b_evos,224,2241.07,228.448,512,24.58,7.07,6.74 +resnetblur50,288,2230.72,229.506,512,25.56,8.52,19.87 +inception_next_small,224,2228.86,229.7,512,49.37,8.36,19.27 +swinv2_cr_tiny_224,224,2222.44,230.365,512,28.33,4.66,28.45 +vit_small_patch16_18x2_224,224,2220.98,230.512,512,64.67,13.71,35.69 +maxvit_nano_rw_256,256,2207.5,231.919,512,15.45,4.46,30.28 +dla169,224,2206.1,232.067,512,53.39,11.6,20.2 +maxvit_rmlp_nano_rw_256,256,2204.07,232.283,512,15.5,4.47,31.92 +vit_base_patch16_siglip_gap_256,256,2196.58,233.076,512,85.84,23.13,33.23 +regnetx_160,224,2194.16,233.329,512,54.28,15.99,25.52 +swinv2_cr_tiny_ns_224,224,2192.78,233.479,512,28.33,4.66,28.45 +regnety_080_tv,224,2184.79,234.333,512,39.38,8.51,19.73 +efficientvit_b3,224,2179.4,234.913,512,48.65,3.99,26.9 +resnet50_gn,224,2179.17,234.937,512,25.56,4.14,11.11 +vit_base_patch16_siglip_256,256,2176.19,235.258,512,92.93,23.44,33.63 +seresnext50_32x4d,288,2170.0,235.928,512,27.56,7.04,23.82 +rdnet_small,224,2169.02,236.035,512,50.44,8.74,22.55 +sequencer2d_m,224,2166.41,236.323,512,38.31,6.55,14.26 +tf_efficientnet_el,300,2156.43,237.414,512,10.59,8.0,30.7 +mobilenetv4_hybrid_medium,448,2154.42,237.637,512,11.07,4.2,29.64 +coatnet_1_rw_224,224,2142.22,238.989,512,41.72,8.04,34.6 +edgenext_base,320,2141.55,239.066,512,18.51,6.01,24.32 +vgg19_bn,224,2133.62,239.953,512,143.68,19.66,14.86 +resnet152s,224,2129.43,240.425,512,60.32,12.92,24.96 +mvitv2_tiny,224,2125.43,240.877,512,24.17,4.7,21.16 +vit_base_patch16_reg4_gap_256,256,2118.83,241.627,512,86.62,23.5,33.89 +resnetblur50d,288,2115.44,242.017,512,25.58,8.92,21.19 +vit_mediumd_patch16_reg4_gap_256,256,2111.48,242.47,512,64.11,17.87,37.57 +convnextv2_tiny,224,2107.29,242.943,512,28.64,4.47,13.44 +mobilenetv4_conv_aa_large,384,2105.52,243.157,512,32.59,7.07,32.29 +maxvit_tiny_tf_224,224,2103.05,243.442,512,30.92,5.6,35.78 +convnext_base,224,2096.16,244.241,512,88.59,15.38,28.75 +resnetv2_50d_frn,224,2077.9,246.382,512,25.59,4.33,11.92 +botnet50ts_256,256,2077.88,246.39,512,22.74,5.54,22.23 +regnetx_120,224,2069.68,247.365,512,46.11,12.13,21.37 +coatnet_rmlp_1_rw2_224,224,2068.04,247.549,512,41.72,8.11,40.13 +convit_small,224,2065.19,247.902,512,27.78,5.76,17.87 +hrnet_w30,224,2062.37,248.224,512,37.71,8.15,21.21 +nf_ecaresnet101,224,2050.3,249.705,512,44.55,8.01,16.27 +nf_seresnet101,224,2045.44,250.297,512,49.33,8.02,16.27 +coatnet_0_224,224,2040.72,250.864,512,25.04,4.58,24.01 +resnetv2_101,288,2033.05,251.823,512,44.54,12.94,26.83 +dpn92,224,2023.11,253.058,512,37.67,6.54,18.21 +focalnet_small_srf,224,2004.67,255.387,512,49.89,8.62,26.26 +crossvit_base_240,240,1979.19,258.671,512,105.03,21.22,36.33 +densenetblur121d,288,1978.97,258.705,512,8.0,5.14,13.06 +fastvit_mci0,256,1974.2,259.33,512,11.41,2.42,18.29 +twins_svt_base,224,1973.81,259.382,512,56.07,8.59,26.33 +hgnetv2_b6,224,1973.41,259.428,512,75.26,16.88,21.23 +legacy_seresnet152,224,1972.16,259.595,512,66.82,11.33,22.08 +hiera_base_plus_224,224,1970.81,259.766,512,69.9,12.67,37.98 +ecaresnet50t,320,1958.88,261.354,512,25.57,8.82,24.13 +regnety_040_sgn,224,1956.43,261.685,512,20.65,4.03,12.29 +wide_resnet50_2,288,1954.98,261.88,512,68.88,18.89,23.81 +resnet101,288,1954.77,261.909,512,44.55,12.95,26.83 +twins_pcpvt_large,224,1953.99,262.014,512,60.99,9.84,35.82 +wide_resnet101_2,224,1953.76,262.043,512,126.89,22.8,21.23 +densenet161,224,1946.37,263.038,512,28.68,7.79,11.06 +dla102x2,224,1944.36,263.309,512,41.28,9.34,29.91 +seresnet152,224,1938.57,264.096,512,66.82,11.57,22.61 +halo2botnet50ts_256,256,1933.79,264.747,512,22.64,5.02,21.78 +regnety_120,224,1926.91,265.695,512,51.82,12.14,21.38 +gcvit_tiny,224,1921.55,266.438,512,28.22,4.79,29.82 +mobilenetv4_conv_large,448,1919.71,266.685,512,32.59,8.75,37.17 +repvgg_b3g4,224,1914.94,267.358,512,83.83,17.89,15.1 +skresnext50_32x4d,224,1910.2,268.02,512,27.48,4.5,17.18 +xcit_tiny_12_p8_224,224,1904.82,268.773,512,6.71,4.81,23.6 +caformer_s18,224,1897.73,269.774,512,26.34,4.13,19.39 +regnetz_b16_evos,224,1888.78,271.059,512,9.74,1.43,9.95 +hgnet_base,224,1879.47,272.399,512,71.58,25.14,15.47 +maxxvit_rmlp_tiny_rw_256,256,1876.98,272.766,512,29.64,6.66,39.76 +xception41,299,1871.32,273.587,512,26.97,9.28,39.86 +cs3se_edgenet_x,320,1859.22,275.361,512,50.72,18.01,20.21 +hrnet_w18_ssld,288,1859.2,275.374,512,21.3,7.14,26.96 +tresnet_xl,224,1856.4,275.789,512,78.44,15.2,15.34 +convnextv2_nano,288,1850.39,276.675,512,15.62,4.06,13.84 +focalnet_small_lrf,224,1837.71,278.592,512,50.34,8.74,28.61 +xcit_small_24_p16_224,224,1834.87,279.023,512,47.67,9.1,23.64 +efficientvit_l2,288,1824.85,280.553,512,63.71,11.51,32.19 +nest_small,224,1822.19,280.962,512,38.35,10.35,40.04 +convnext_small,288,1809.03,283.01,512,50.22,14.39,35.65 +repvgg_b3,224,1809.03,283.005,512,123.09,29.16,15.1 +resnext101_64x4d,224,1807.89,283.188,512,83.46,15.52,31.21 +vit_base_patch16_plus_240,240,1806.76,283.363,512,117.56,27.41,33.08 +vit_relpos_base_patch16_plus_240,240,1802.12,284.093,512,117.38,27.3,34.33 +nest_small_jx,224,1801.11,284.248,512,38.35,10.35,40.04 +samvit_base_patch16_224,224,1797.25,284.868,512,86.46,17.54,24.54 +vit_small_patch8_224,224,1797.1,284.885,512,21.67,22.44,80.84 +resnext101_32x8d,224,1792.69,285.588,512,88.79,16.48,31.21 +resnetaa101d,288,1777.27,288.066,512,44.57,15.07,29.03 +poolformer_s36,224,1773.95,288.607,512,30.86,5.0,15.82 +vit_large_r50_s32_224,224,1771.48,289.009,512,328.99,19.58,24.41 +cait_xxs36_224,224,1769.48,289.336,512,17.3,3.77,30.34 +efficientnet_b4,320,1763.52,290.311,512,19.34,3.13,34.76 +convformer_s18,224,1757.98,291.227,512,26.77,3.96,15.82 +davit_base,224,1755.64,291.611,512,87.95,15.51,40.66 +dpn98,224,1751.0,292.387,512,61.57,11.73,25.2 +dm_nfnet_f0,256,1740.31,294.174,512,71.49,12.62,18.05 +mobilenetv4_hybrid_large,384,1735.11,295.061,512,37.76,7.77,34.52 +eca_nfnet_l1,256,1730.61,295.834,512,41.41,9.62,22.04 +resnetv2_50d_evos,224,1724.23,296.927,512,25.59,4.33,11.92 +hrnet_w40,224,1723.16,297.088,512,57.56,12.75,25.29 +resnet152d,256,1717.79,298.038,512,60.21,15.41,30.51 +tnt_s_patch16_224,224,1716.84,298.207,512,23.76,5.24,24.37 +eva02_base_patch16_clip_224,224,1710.74,299.27,512,86.26,17.62,26.32 +swin_small_patch4_window7_224,224,1706.58,299.999,512,49.61,8.77,27.47 +inception_resnet_v2,299,1690.61,302.813,512,55.84,13.18,25.06 +vit_betwixt_patch16_rope_reg4_gap_256,256,1681.28,304.517,512,60.23,16.52,28.24 +efficientvit_b3,256,1678.52,305.014,512,48.65,5.2,35.01 +regnety_160,224,1676.75,305.336,512,83.59,15.96,23.04 +convnext_tiny,384,1663.32,307.803,512,28.59,13.14,39.48 +xception65p,299,1663.22,307.807,512,39.82,13.91,52.48 +regnetz_d32,320,1656.99,308.976,512,27.58,9.33,37.08 +efficientformerv2_s0,224,1656.06,309.153,512,3.6,0.41,5.3 +efficientnetv2_s,384,1651.63,309.97,512,21.46,8.44,35.77 +resnet200,224,1649.35,310.41,512,64.67,15.07,32.19 +nf_regnet_b4,384,1647.49,310.762,512,30.21,4.7,28.61 +regnetz_d8,320,1642.91,311.627,512,23.37,6.19,37.08 +nfnet_f1,224,1642.07,311.777,512,132.63,17.87,22.94 +seresnet101,288,1638.36,312.492,512,49.33,12.95,26.87 +tf_efficientnetv2_s,384,1634.05,313.314,512,21.46,8.44,35.77 +regnetz_040,320,1629.96,314.078,512,27.12,6.35,37.78 +resnext101_32x4d,288,1628.74,314.339,512,44.18,13.24,35.09 +seresnext101_64x4d,224,1627.86,314.507,512,88.23,15.53,31.25 +maxvit_tiny_rw_256,256,1627.49,314.58,512,29.07,6.74,44.35 +maxvit_rmlp_tiny_rw_256,256,1624.07,315.242,512,29.15,6.77,46.92 +regnetz_040_h,320,1623.42,315.367,512,28.94,6.43,37.94 +poolformerv2_s24,224,1621.47,315.747,512,21.34,3.42,10.68 +pvt_v2_b5,224,1619.65,316.103,512,81.96,11.76,50.92 +seresnext101_32x8d,224,1619.59,316.115,512,93.57,16.48,31.25 +resnest50d_4s2x40d,224,1617.32,316.558,512,30.42,4.4,17.94 +efficientnetv2_rw_s,384,1616.73,316.671,512,23.94,8.72,38.03 +pvt_v2_b4,224,1616.7,316.679,512,62.56,10.14,53.74 +convnext_base,256,1615.94,316.827,512,88.59,20.09,37.55 +mvitv2_small,224,1605.45,318.899,512,34.87,7.0,28.08 +coat_tiny,224,1603.76,319.236,512,5.5,4.35,27.2 +mobilevitv2_200,256,1595.83,320.821,512,18.45,7.22,32.15 +swinv2_tiny_window8_256,256,1595.55,320.875,512,28.35,5.96,24.57 +fastvit_sa24,256,1588.77,322.245,512,21.55,3.8,24.32 +resnetrs101,288,1587.5,322.503,512,63.62,13.56,28.53 +vit_base_r50_s16_224,224,1585.43,322.924,512,97.89,21.66,35.28 +seresnext101d_32x8d,224,1581.7,323.686,512,93.59,16.72,32.05 +mvitv2_small_cls,224,1581.51,323.728,512,34.87,7.04,28.17 +ecaresnet101d,288,1578.08,324.428,512,44.57,13.35,28.19 +inception_next_base,224,1571.68,325.752,512,86.67,14.85,25.69 +mixnet_xxl,224,1570.27,326.029,512,23.96,2.04,23.43 +vit_medium_patch16_gap_384,384,1558.61,328.483,512,39.03,26.08,67.54 +mobilenetv4_conv_aa_large,448,1557.91,328.629,512,32.59,9.63,43.94 +vit_small_r26_s32_384,384,1549.48,330.418,512,36.47,10.43,29.85 +efficientformerv2_s1,224,1538.12,332.859,512,6.19,0.67,7.66 +volo_d2_224,224,1537.61,332.969,512,58.68,14.34,41.34 +resnetblur101d,288,1534.28,333.687,512,44.57,15.07,29.65 +resnet101d,320,1514.34,338.086,512,44.57,16.48,34.77 +rdnet_base,224,1513.06,338.372,512,87.45,15.4,31.14 +hiera_base_abswin_256,256,1498.55,341.631,512,51.27,12.46,40.7 +maxvit_rmlp_small_rw_224,224,1496.71,342.069,512,64.9,10.75,49.3 +seresnextaa101d_32x8d,224,1495.01,342.457,512,93.59,17.25,34.16 +coatnet_1_224,224,1490.49,343.476,512,42.23,8.7,39.0 +hrnet_w48_ssld,224,1486.26,344.473,512,77.47,17.34,28.56 +hrnet_w48,224,1482.42,345.349,512,77.47,17.34,28.56 +efficientnetv2_m,320,1471.33,347.961,512,54.14,11.01,39.97 +regnetz_e8,256,1443.64,354.642,512,57.7,9.91,40.94 +resnetrs152,256,1435.38,356.681,512,86.62,15.59,30.83 +seresnet152d,256,1433.76,357.085,512,66.84,15.42,30.56 +focalnet_base_srf,224,1432.2,357.455,512,88.15,15.28,35.01 +eva02_small_patch14_336,336,1426.95,358.791,512,22.13,15.48,54.33 +regnety_064,288,1425.13,359.247,512,30.58,10.56,27.11 +maxvit_tiny_pm_256,256,1425.01,359.276,512,30.09,6.61,47.9 +regnetv_064,288,1424.51,359.406,512,30.58,10.55,27.11 +regnety_080,288,1420.63,360.386,512,39.18,13.22,29.69 +resnet50x4_clip_gap,288,1412.45,362.468,512,65.62,19.57,34.11 +tiny_vit_21m_384,384,1405.09,364.373,512,21.23,13.77,77.83 +seresnext101_32x4d,288,1404.5,364.525,512,48.96,13.25,35.12 +crossvit_15_dagger_408,408,1400.47,365.57,512,28.5,21.45,95.05 +sequencer2d_l,224,1398.42,366.113,512,54.3,9.74,22.12 +xception65,299,1384.96,369.654,512,39.92,13.96,52.48 +resnet152,288,1381.72,370.537,512,60.19,19.11,37.28 +resnetv2_50d_gn,288,1381.53,370.586,512,25.57,7.24,19.7 +rexnetr_300,288,1381.25,370.662,512,34.81,5.59,36.61 +swinv2_cr_small_224,224,1376.99,371.809,512,49.7,9.07,50.27 +gmlp_b16_224,224,1375.86,372.108,512,73.08,15.78,30.21 +twins_svt_large,224,1368.81,374.033,512,99.27,15.15,35.1 +swinv2_cr_small_ns_224,224,1365.84,374.842,512,49.7,9.08,50.27 +mobilenetv4_conv_aa_large,480,1358.03,377.001,512,32.59,11.05,50.45 +resnet50x4_clip,288,1357.9,377.035,512,87.14,21.35,35.27 +vit_mediumd_patch16_rope_reg1_gap_256,256,1357.84,377.052,512,63.95,17.65,37.02 +vit_so150m_patch16_reg4_gap_256,256,1351.9,378.708,512,134.13,36.75,53.21 +hrnet_w44,224,1349.33,379.407,512,67.06,14.94,26.92 +cait_s24_224,224,1347.27,380.003,512,46.92,9.35,40.58 +xcit_tiny_24_p16_384,384,1345.07,380.635,512,12.12,6.87,34.29 +vit_so150m_patch16_reg4_map_256,256,1342.76,381.288,512,141.48,37.18,53.68 +vit_base_patch16_rope_reg1_gap_256,256,1339.91,382.096,512,86.43,23.22,33.39 +dm_nfnet_f1,224,1329.53,385.08,512,132.63,17.87,22.94 +tresnet_m,448,1324.12,386.655,512,31.39,22.99,29.21 +efficientnet_lite4,380,1322.67,387.08,512,13.01,4.04,45.66 +focalnet_base_lrf,224,1320.54,387.704,512,88.75,15.43,38.13 +efficientvit_b3,288,1314.64,389.442,512,48.65,6.58,44.2 +eva02_base_patch14_224,224,1312.14,390.184,512,85.76,23.22,36.55 +efficientformer_l7,224,1309.92,390.849,512,82.23,10.17,24.45 +nest_base,224,1307.57,391.551,512,67.72,17.96,53.39 +resnet50_gn,288,1305.16,392.272,512,25.56,6.85,18.37 +nest_base_jx,224,1294.67,395.451,512,67.72,17.96,53.39 +nextvit_small,384,1284.43,398.604,512,31.76,17.26,57.14 +convnextv2_small,224,1282.44,399.217,512,50.32,8.71,21.56 +xcit_medium_24_p16_224,224,1280.1,399.955,512,84.4,16.13,31.71 +efficientnetv2_rw_m,320,1275.83,401.294,512,53.24,12.72,47.14 +convnextv2_tiny,288,1271.87,402.531,512,28.64,7.39,22.21 +convnext_base,288,1268.11,403.734,512,88.59,25.43,47.53 +mobilenetv4_hybrid_large,448,1265.42,404.591,512,37.76,10.74,48.61 +coat_lite_medium,224,1251.87,408.976,512,44.57,9.81,40.06 +coat_mini,224,1250.83,409.311,512,10.34,6.82,33.68 +maxxvit_rmlp_small_rw_256,256,1243.18,411.831,512,66.01,14.67,58.38 +dpn131,224,1241.67,412.321,512,79.25,16.09,32.97 +poolformer_m36,224,1239.54,413.041,512,56.17,8.8,22.02 +efficientvit_l3,224,1238.99,413.221,512,246.04,27.62,39.16 +swin_base_patch4_window7_224,224,1237.04,413.872,512,87.77,15.47,36.63 +tf_efficientnet_lite4,380,1223.65,418.405,512,13.01,4.04,45.66 +resnet200d,256,1220.57,419.459,512,64.69,20.0,43.09 +convnext_large,224,1210.69,422.882,512,197.77,34.4,43.13 +vit_large_patch32_384,384,1200.4,426.508,512,306.63,45.31,43.86 +maxvit_small_tf_224,224,1189.65,430.365,512,68.93,11.66,53.17 +swin_s3_small_224,224,1180.79,433.592,512,49.74,9.43,37.84 +xcit_small_12_p16_384,384,1174.92,435.759,512,26.25,14.14,36.51 +hgnetv2_b6,288,1173.05,436.453,512,75.26,27.9,35.09 +regnety_040_sgn,288,1170.65,437.346,512,20.65,6.67,20.3 +levit_384_s8,224,1165.28,329.517,384,39.12,9.98,35.86 +regnety_120,288,1157.95,442.145,512,51.82,20.06,35.34 +seresnet152,288,1150.95,444.835,512,66.82,19.11,37.34 +resnest101e,256,1145.83,446.818,512,48.28,13.38,28.66 +gcvit_small,224,1144.63,447.289,512,51.09,8.57,41.61 +regnetz_b16_evos,288,1135.7,450.806,512,9.74,2.36,16.43 +coatnet_2_rw_224,224,1135.63,450.831,512,73.87,15.09,49.22 +hrnet_w64,224,1135.29,450.949,512,128.06,28.97,35.09 +mvitv2_base,224,1134.0,451.484,512,51.47,10.16,40.5 +vit_betwixt_patch16_reg4_gap_384,384,1127.26,454.181,512,60.6,39.71,85.28 +fastvit_sa36,256,1120.28,457.014,512,31.53,5.64,34.61 +levit_conv_384_s8,224,1119.98,342.844,384,39.12,9.98,35.86 +tnt_b_patch16_224,224,1117.46,458.164,512,65.41,14.09,39.01 +regnety_320,224,1117.25,458.249,512,145.05,32.34,30.26 +mvitv2_base_cls,224,1116.06,458.742,512,65.44,10.23,40.65 +dpn107,224,1115.38,459.002,512,86.92,18.38,33.46 +densenet264d,224,1113.18,459.927,512,72.74,13.57,14.0 +efficientnet_b3_gn,288,1108.62,461.817,512,11.73,1.74,23.35 +regnetz_c16_evos,256,1107.52,462.279,512,13.49,2.48,16.57 +eca_nfnet_l1,320,1106.04,462.894,512,41.41,14.92,34.42 +volo_d3_224,224,1105.28,463.214,512,86.33,20.78,60.09 +hgnet_base,288,1104.26,463.623,512,71.58,41.55,25.57 +fastvit_mci1,256,1101.38,464.855,512,21.54,4.72,32.84 +convit_base,224,1094.41,467.818,512,86.54,17.52,31.77 +nf_regnet_b5,384,1094.33,467.849,512,49.74,7.95,42.9 +poolformerv2_s36,224,1088.71,470.265,512,30.79,5.01,15.82 +resnet152d,320,1084.29,472.178,512,60.21,24.08,47.67 +resnext101_64x4d,288,1081.56,473.371,512,83.46,25.66,51.59 +efficientnet_b3_g8_gn,288,1073.57,476.896,512,14.25,2.59,23.35 +coatnet_2_224,224,1069.58,359.001,384,74.68,16.5,52.67 +xception71,299,1060.08,482.964,512,42.34,18.09,69.92 +swinv2_base_window12_192,192,1057.24,484.264,512,109.28,11.9,39.72 +vitamin_base_224,224,1057.15,484.302,512,87.72,22.68,52.77 +davit_large,224,1055.3,485.153,512,196.81,34.6,60.99 +legacy_senet154,224,1054.93,485.323,512,115.09,20.77,38.69 +senet154,224,1054.21,485.652,512,115.09,20.77,38.69 +mobilenetv4_conv_aa_large,544,1053.47,485.995,512,32.59,14.19,64.79 +convnextv2_nano,384,1044.07,490.365,512,15.62,7.22,24.61 +resnext101_32x16d,224,1041.98,491.351,512,194.03,36.27,51.18 +efficientnet_b4,384,1041.96,491.357,512,19.34,4.51,50.04 +coatnet_rmlp_2_rw_224,224,1039.54,492.496,512,73.88,15.18,54.78 +regnetx_320,224,1036.62,493.887,512,107.81,31.81,36.3 +convnext_base,320,1033.25,495.508,512,88.59,31.39,58.68 +resnetv2_50x1_bit,448,1033.24,495.51,512,25.55,16.62,44.46 +maxxvitv2_rmlp_base_rw_224,224,1030.26,496.942,512,116.09,24.2,62.77 +resnetv2_50d_evos,288,1024.95,499.517,512,25.59,7.15,19.7 +convnext_small,384,1023.25,500.352,512,50.22,25.58,63.37 +crossvit_18_dagger_408,408,1022.68,500.623,512,44.61,32.47,124.87 +ecaresnet200d,256,1012.11,505.857,512,64.69,20.0,43.15 +maxvit_rmlp_small_rw_256,256,1011.88,505.971,512,64.9,14.15,66.09 +resnetrs200,256,1010.68,506.576,512,93.21,20.18,43.42 +seresnet200d,256,1009.34,507.25,512,71.86,20.01,43.15 +regnety_160,288,1007.2,508.318,512,83.59,26.37,38.07 +xcit_tiny_24_p8_224,224,1006.77,508.541,512,12.11,9.21,45.39 +swinv2_cr_base_224,224,1003.7,510.098,512,87.88,15.86,59.66 +resnetv2_50x3_bit,224,999.54,512.217,512,217.32,37.06,33.34 +mobilevitv2_175,384,997.46,256.635,256,14.25,12.47,63.29 +swinv2_cr_base_ns_224,224,996.85,513.602,512,87.88,15.86,59.66 +tf_efficientnetv2_m,384,995.99,514.039,512,54.14,15.85,57.52 +nextvit_base,384,991.15,516.552,512,44.82,24.64,73.95 +swinv2_small_window8_256,256,991.15,516.555,512,49.73,11.58,40.14 +swinv2_cr_small_ns_256,256,989.97,517.174,512,49.7,12.07,76.21 +tf_efficientnet_b4,380,989.8,517.261,512,19.34,4.49,49.49 +caformer_s36,224,984.99,519.776,512,39.3,8.0,37.53 +maxvit_rmlp_base_rw_224,224,982.44,521.136,512,116.14,23.15,92.64 +swin_s3_base_224,224,980.38,522.23,512,71.13,13.69,48.26 +swinv2_tiny_window16_256,256,979.79,522.545,512,28.35,6.68,39.02 +resnet200,288,975.16,525.017,512,64.67,24.91,53.21 +seresnext101_32x8d,288,971.5,527.002,512,93.57,27.24,51.63 +xcit_nano_12_p8_384,384,969.96,527.836,512,3.05,6.34,46.08 +efficientvit_l2,384,967.67,529.083,512,63.71,20.45,57.01 +volo_d1_384,384,963.34,531.465,512,26.78,22.75,108.55 +coat_small,224,963.19,531.55,512,21.69,12.61,44.25 +convnextv2_base,224,961.57,532.445,512,88.72,15.38,28.75 +mobilevitv2_150,384,958.44,400.634,384,10.59,9.2,54.25 +seresnext101d_32x8d,288,949.26,539.328,512,93.59,27.64,52.95 +mixer_l16_224,224,944.93,541.817,512,208.2,44.6,41.69 +vit_base_patch16_siglip_gap_384,384,944.73,541.939,512,86.09,55.43,101.3 +vit_base_patch16_384,384,941.77,543.639,512,86.86,55.54,101.56 +deit_base_patch16_384,384,941.44,543.829,512,86.86,55.54,101.56 +deit_base_distilled_patch16_384,384,940.85,544.169,512,87.63,55.65,101.82 +vit_base_patch16_clip_384,384,939.44,544.987,512,86.86,55.54,101.56 +vit_mediumd_patch16_reg4_gap_384,384,937.89,545.887,512,64.27,43.67,113.51 +vit_base_patch16_siglip_384,384,935.86,547.069,512,93.18,56.12,102.2 +poolformer_m48,224,931.73,549.496,512,73.47,11.59,29.17 +regnetz_e8,320,928.33,551.508,512,57.7,15.46,63.94 +convnext_large_mlp,256,924.56,553.757,512,200.13,44.94,56.33 +fastvit_ma36,256,923.01,554.689,512,44.07,7.88,41.09 +efficientvit_l3,256,921.63,555.515,512,246.04,36.06,50.98 +convformer_s36,224,921.06,555.861,512,40.01,7.67,30.5 +convmixer_1024_20_ks9_p14,224,918.33,557.51,512,24.38,5.55,5.51 +resnetrs152,320,913.9,560.222,512,86.62,24.34,48.14 +seresnet152d,320,908.95,563.261,512,66.84,24.09,47.72 +rdnet_large,224,908.27,563.694,512,186.27,34.74,46.67 +eva_large_patch14_196,196,906.91,564.536,512,304.14,61.57,63.52 +vit_large_patch16_224,224,903.87,566.434,512,304.33,61.6,63.52 +seresnextaa101d_32x8d,288,901.06,568.175,512,93.59,28.51,56.44 +xcit_small_12_p8_224,224,893.66,572.906,512,26.21,18.69,47.21 +hrnet_w48_ssld,288,876.92,583.844,512,77.47,28.66,47.21 +nfnet_f2,256,871.75,587.309,512,193.78,33.76,41.85 +beit_large_patch16_224,224,871.03,587.791,512,304.43,61.6,63.52 +efficientnetv2_m,416,869.46,588.822,512,54.14,18.6,67.5 +deit3_base_patch16_384,384,868.27,589.658,512,86.88,55.54,101.56 +beit_base_patch16_384,384,868.09,589.786,512,86.74,55.54,101.56 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+vit_so400m_patch14_siglip_384,384,169.04,3028.862,512,428.23,335.4,452.89 +resnet50x64_clip_gap,448,168.37,2280.66,384,365.03,253.96,233.22 +vit_so400m_patch14_siglip_gap_384,384,168.28,3042.431,512,412.99,333.46,451.19 +convnextv2_huge,288,166.55,1152.798,192,660.29,190.1,130.7 +mvitv2_huge_cls,224,166.12,2311.572,384,694.8,120.67,243.63 +resnet50x64_clip,448,163.96,1561.362,256,420.38,265.02,239.13 +vitamin_large2_384,384,163.86,781.139,128,333.97,234.44,440.16 +vitamin_large_384,384,163.84,781.221,128,333.71,234.44,440.16 +volo_d4_448,448,162.45,3151.648,512,193.41,197.13,527.35 +xcit_small_24_p8_384,384,160.35,2394.77,384,47.63,105.24,265.91 +swinv2_cr_large_384,384,155.84,821.327,128,196.68,108.96,404.96 +focalnet_large_fl3,384,153.3,1669.857,256,239.13,105.06,168.04 +cait_s36_384,384,150.49,3402.164,512,68.37,47.99,367.4 +resnetv2_101x3_bit,448,150.3,1277.431,192,387.93,280.33,194.78 +davit_base_fl,768,149.95,853.596,128,90.37,190.32,530.15 +vit_huge_patch14_clip_336,336,148.98,3436.652,512,632.46,390.97,407.54 +focalnet_large_fl4,384,146.26,1750.32,256,239.32,105.2,181.78 +beit_large_patch16_512,512,143.19,3575.759,512,305.67,362.24,656.39 +dm_nfnet_f5,416,140.91,3633.497,512,377.21,170.71,204.56 +maxvit_base_tf_512,512,140.65,682.546,96,119.88,138.02,703.99 +sam2_hiera_base_plus,896,138.32,462.665,64,68.68,227.48,828.88 +nfnet_f4,512,135.39,2836.163,384,316.07,216.26,262.26 +convmixer_1536_20,224,135.19,3787.319,512,51.63,48.68,33.03 +vitamin_xlarge_384,384,130.77,978.777,128,436.06,306.38,493.46 +vit_gigantic_patch14_224,224,130.62,3919.717,512,1844.44,483.95,275.37 +vit_gigantic_patch14_clip_224,224,130.29,3929.635,512,1844.91,483.96,275.37 +nfnet_f6,448,127.99,4000.364,512,438.36,229.7,273.62 +efficientnet_b8,672,123.17,1039.173,128,87.41,63.48,442.89 +swinv2_base_window12to24_192to384,384,119.99,533.358,64,87.92,55.25,280.36 +tf_efficientnet_b8,672,119.33,1072.656,128,87.41,63.48,442.89 +vit_so400m_patch14_siglip_gap_448,448,116.66,3291.487,384,413.33,487.18,764.26 +vit_huge_patch14_clip_378,378,116.09,4410.348,512,632.68,503.79,572.79 +regnety_1280,384,115.79,1105.427,128,644.81,374.99,210.2 +xcit_medium_24_p8_384,384,113.78,2250.01,256,84.32,186.67,354.73 +dm_nfnet_f4,512,110.56,3473.149,384,316.07,216.26,262.26 +focalnet_xlarge_fl3,384,108.72,1765.946,192,408.79,185.61,223.99 +vit_huge_patch16_gap_448,448,107.19,3582.45,384,631.67,544.7,636.83 +vit_large_patch14_dinov2,518,106.66,3600.098,384,304.37,507.15,1058.82 +volo_d5_448,448,106.54,3604.236,384,295.91,315.06,737.92 +vit_large_patch14_reg4_dinov2,518,106.3,3612.324,384,304.37,508.9,1064.02 +dm_nfnet_f6,448,104.84,4883.385,512,438.36,229.7,273.62 +maxvit_xlarge_tf_384,384,104.53,918.413,96,475.32,292.78,668.76 +focalnet_xlarge_fl4,384,103.87,1848.501,192,409.03,185.79,242.31 +vit_huge_patch14_clip_quickgelu_378,378,103.58,3707.381,384,632.68,503.79,572.79 +eva02_large_patch14_448,448,102.65,3740.795,384,305.08,362.33,689.95 +nfnet_f5,544,100.78,3810.204,384,377.21,290.97,349.71 +eva_giant_patch14_336,336,100.31,5104.356,512,1013.01,620.64,550.67 +maxvit_large_tf_512,512,99.37,644.068,64,212.33,244.75,942.15 +nfnet_f7,480,97.93,3921.33,384,499.5,300.08,355.86 +convnextv2_huge,384,93.62,1025.37,96,660.29,337.96,232.35 +swinv2_cr_giant_224,224,88.64,2165.946,192,2598.76,483.85,309.15 +dm_nfnet_f5,544,82.3,4666.041,384,377.21,290.97,349.71 +tf_efficientnet_l2,475,82.12,1558.754,128,480.31,172.11,609.89 +volo_d5_512,512,81.71,3133.006,256,296.09,425.09,1105.37 +nfnet_f6,576,77.44,4958.912,384,438.36,378.69,452.2 +swinv2_large_window12to24_192to384,384,76.53,627.149,48,196.74,116.15,407.83 +swinv2_cr_huge_384,384,73.82,866.901,64,657.94,352.04,583.18 +xcit_large_24_p8_384,384,68.02,2822.791,192,188.93,415.0,531.82 +cait_m36_384,384,64.68,3958.009,256,271.22,173.11,734.81 +dm_nfnet_f6,576,63.38,4039.147,256,438.36,378.69,452.2 +nfnet_f7,608,61.1,4189.78,256,499.5,480.39,570.85 +regnety_2560,384,60.04,1598.871,96,1282.6,747.83,296.49 +davit_huge_fl,768,58.91,1086.416,64,360.64,744.84,1060.3 +maxvit_xlarge_tf_512,512,58.65,818.415,48,475.77,534.14,1413.22 +resnetv2_152x4_bit,480,57.16,1679.394,96,936.53,844.84,414.26 +convnextv2_huge,512,52.17,920.112,48,660.29,600.81,413.07 +sam2_hiera_large,1024,45.19,1062.166,48,212.15,907.48,2190.34 +samvit_base_patch16,1024,33.25,481.156,16,89.67,486.43,1343.27 +eva_giant_patch14_560,560,33.13,5795.849,192,1014.45,1906.76,2577.17 +vit_giant_patch14_dinov2,518,32.93,3886.558,128,1136.48,1784.2,2757.89 +vit_giant_patch14_reg4_dinov2,518,32.91,3889.85,128,1136.48,1790.08,2771.21 +efficientnet_l2,800,29.86,1607.312,48,480.31,479.12,1707.39 +tf_efficientnet_l2,800,29.23,1642.279,48,480.31,479.12,1707.39 +cait_m48_448,448,27.4,4671.94,128,356.46,329.41,1708.23 +swinv2_cr_giant_384,384,23.87,1340.314,32,2598.76,1450.71,1394.86 +vit_so400m_patch14_siglip_gap_896,896,21.24,4519.479,96,416.87,2731.49,8492.88 +samvit_large_patch16,1024,16.18,741.849,12,308.28,1493.86,2553.78 +samvit_huge_patch16,1024,10.84,737.698,8,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt221-cpu-i9_10940x-dynamo.csv b/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt221-cpu-i9_10940x-dynamo.csv new file mode 100644 index 0000000000000000000000000000000000000000..b93ee9e14f03c548bd89df0fb0550f4b3484b171 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt221-cpu-i9_10940x-dynamo.csv @@ -0,0 +1,1322 @@ +model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count +tf_mobilenetv3_small_minimal_100,309.77,3.161,1,224,2.04 +lcnet_035,308.98,3.161,1,224,1.64 +resnet10t,300.12,3.231,1,176,5.44 +lcnet_050,295.81,3.311,1,224,1.88 +tinynet_e,273.01,3.592,1,106,2.04 +mobilenetv3_small_050,270.37,3.63,1,224,1.59 +lcnet_075,266.53,3.674,1,224,2.36 +mobilenetv2_035,249.23,3.942,1,224,1.68 +lcnet_100,244.53,3.999,1,224,2.95 +mobilenetv3_small_100,225.97,4.345,1,224,2.54 +mobilenetv3_small_075,220.29,4.46,1,224,2.04 +tf_mobilenetv3_small_100,219.41,4.475,1,224,2.54 +lcnet_150,219.1,4.466,1,224,4.5 +mobilenetv2_050,214.82,4.575,1,224,1.97 +tf_mobilenetv3_small_075,208.46,4.716,1,224,2.04 +mnasnet_050,205.77,4.783,1,224,2.22 +regnetx_002,198.21,4.962,1,224,2.68 +ese_vovnet19b_slim,192.31,5.101,1,224,3.17 +tinynet_d,191.92,5.128,1,152,2.34 +resnet18,187.86,5.216,1,160,11.69 +mobilenetv2_075,187.52,5.242,1,224,2.64 +mnasnet_small,187.12,5.265,1,224,2.03 +efficientvit_b0,184.09,5.346,1,224,3.41 +ese_vovnet19b_slim_dw,184.05,5.341,1,224,1.9 +mobilenetv2_100,182.7,5.378,1,224,3.5 +hgnetv2_b0,180.83,5.43,1,224,6.0 +mnasnet_075,180.52,5.443,1,224,3.17 +hardcorenas_a,179.9,5.461,1,224,5.26 +mobilenetv3_large_075,176.23,5.58,1,224,3.99 +efficientnet_lite0,174.03,5.647,1,224,4.65 +levit_128s,173.36,5.665,1,224,7.78 +mnasnet_100,173.17,5.679,1,224,4.38 +semnasnet_050,171.62,5.745,1,224,2.08 +gernet_s,167.59,5.865,1,224,8.17 +mobilenetv3_large_100,165.26,5.952,1,224,5.48 +cs3darknet_s,164.21,5.995,1,256,3.28 +mobilenetv3_rw,162.02,6.071,1,224,5.48 +cs3darknet_focus_s,161.22,6.111,1,256,3.27 +mobilenetv2_140,159.18,6.179,1,224,6.11 +tf_mobilenetv3_large_minimal_100,159.13,6.186,1,224,3.92 +resnet10t,155.59,6.322,1,224,5.44 +regnety_002,154.79,6.375,1,224,3.16 +tf_mobilenetv3_large_075,150.25,6.551,1,224,3.99 +resnet14t,149.95,6.563,1,176,10.08 +hardcorenas_b,146.58,6.72,1,224,5.18 +hardcorenas_c,146.24,6.738,1,224,5.52 +fbnetc_100,143.95,6.847,1,224,5.57 +regnetx_004_tv,143.93,6.85,1,224,5.5 +levit_conv_128s,142.89,6.888,1,224,7.78 +tf_mobilenetv3_large_100,142.19,6.933,1,224,5.48 +semnasnet_075,141.97,6.949,1,224,2.91 +semnasnet_100,141.36,6.977,1,224,3.89 +hgnetv2_b0,141.33,6.974,1,288,6.0 +mobilenetv2_110d,139.05,7.092,1,224,4.52 +spnasnet_100,138.75,7.11,1,224,4.42 +dla46_c,138.38,7.137,1,224,1.3 +hgnetv2_b1,138.06,7.135,1,224,6.34 +efficientnet_es,137.5,7.174,1,224,5.44 +efficientnet_es_pruned,137.05,7.197,1,224,5.44 +tf_efficientnet_es,135.86,7.256,1,224,5.44 +resnet14t,135.07,7.296,1,224,10.08 +dla46x_c,134.66,7.334,1,224,1.07 +efficientnet_lite1,134.08,7.36,1,240,5.42 +regnetx_004,134.07,7.361,1,224,5.16 +regnetx_006,133.51,7.39,1,224,6.2 +semnasnet_140,133.03,7.417,1,224,6.11 +vit_tiny_r_s16_p8_224,132.94,7.419,1,224,6.34 +mnasnet_140,132.56,7.439,1,224,7.12 +regnetx_008,131.77,7.489,1,224,7.26 +mixer_s32_224,130.18,7.575,1,224,19.1 +tf_efficientnet_lite0,129.71,7.61,1,224,4.65 +tinynet_c,129.52,7.631,1,184,2.46 +levit_192,128.69,7.669,1,224,10.95 +mobilevitv2_050,127.38,7.758,1,256,1.37 +resnext26ts,126.34,7.809,1,256,10.3 +repghostnet_058,125.0,7.916,1,224,2.55 +efficientvit_m1,124.63,7.943,1,224,2.98 +mobileone_s1,123.39,8.002,1,224,4.83 +levit_conv_128,122.84,8.041,1,224,9.21 +vit_tiny_patch16_224,122.27,8.075,1,224,5.72 +repghostnet_080,121.61,8.134,1,224,3.28 +efficientvit_m2,120.55,8.204,1,224,4.19 +repghostnet_050,120.14,8.244,1,224,2.31 +gmixer_12_224,119.61,8.256,1,224,12.7 +efficientnet_lite2,118.78,8.318,1,260,6.09 +edgenext_xx_small,118.74,8.34,1,288,1.33 +convnext_atto,118.59,8.331,1,224,3.7 +levit_conv_192,117.23,8.428,1,224,10.95 +resnet18,117.15,8.431,1,224,11.69 +ese_vovnet19b_dw,116.09,8.507,1,224,6.54 +poolformer_s12,116.09,8.504,1,224,11.92 +hgnetv2_b1,115.23,8.574,1,288,6.34 +repghostnet_100,115.12,8.593,1,224,4.07 +mobileone_s2,115.04,8.591,1,224,7.88 +efficientformer_l1,114.17,8.656,1,224,12.29 +regnety_008,114.08,8.665,1,224,6.26 +pit_ti_distilled_224,113.8,8.689,1,224,5.1 +eca_resnext26ts,113.67,8.691,1,256,10.3 +resnext26ts,113.57,8.698,1,288,10.3 +convnextv2_atto,113.43,8.717,1,224,3.71 +hardcorenas_e,112.85,8.76,1,224,8.07 +pit_ti_224,112.61,8.783,1,224,4.85 +regnety_008_tv,112.43,8.792,1,224,6.43 +hardcorenas_f,111.54,8.864,1,224,8.2 +mobilenetv2_120d,111.47,8.87,1,224,5.83 +tf_efficientnet_lite1,110.97,8.904,1,240,5.42 +rexnetr_100,110.89,8.92,1,224,4.88 +repghostnet_111,110.87,8.924,1,224,4.54 +resnet34,110.55,8.941,1,160,21.8 +regnetx_016,110.01,8.986,1,224,9.19 +tinynet_b,109.25,9.056,1,188,3.73 +vit_small_patch32_224,109.13,9.057,1,224,22.88 +convnext_atto_ols,109.08,9.061,1,224,3.7 +resnet18d,108.71,9.091,1,224,11.71 +repghostnet_130,108.47,9.122,1,224,5.48 +poolformerv2_s12,107.65,9.183,1,224,11.89 +seresnext26ts,107.11,9.232,1,256,10.39 +convnext_femto_ols,106.07,9.323,1,224,5.23 +mobilevitv2_075,106.07,9.326,1,256,2.87 +hardcorenas_d,105.79,9.356,1,224,7.5 +dla60x_c,105.71,9.368,1,224,1.32 +ecaresnext26t_32x4d,105.69,9.351,1,224,15.41 +regnety_004,105.63,9.369,1,224,4.34 +legacy_seresnext26_32x4d,104.98,9.42,1,224,16.79 +rexnetr_130,104.91,9.431,1,224,7.61 +resnest14d,104.32,9.477,1,224,10.61 +gernet_m,104.16,9.496,1,224,21.14 +deit_tiny_distilled_patch16_224,103.75,9.535,1,224,5.91 +ecaresnext50t_32x4d,103.7,9.532,1,224,15.41 +gmlp_ti16_224,103.63,9.549,1,224,5.87 +mobileone_s3,103.43,9.566,1,224,10.17 +regnety_006,103.41,9.567,1,224,6.06 +fastvit_t8,102.84,9.625,1,256,4.03 +edgenext_x_small,102.14,9.7,1,256,2.34 +legacy_seresnet18,101.61,9.742,1,224,11.78 +visformer_tiny,101.0,9.794,1,224,10.32 +convnext_femto,100.98,9.799,1,224,5.22 +eca_resnext26ts,100.84,9.809,1,288,10.3 +repghostnet_150,100.56,9.847,1,224,6.58 +resnet26,100.24,9.873,1,224,16.0 +rexnet_130,100.02,9.897,1,224,7.56 +ghostnet_050,99.95,9.921,1,224,2.59 +repvgg_a0,99.88,9.911,1,224,9.11 +tf_efficientnet_lite2,99.83,9.913,1,260,6.09 +resmlp_12_224,99.81,9.912,1,224,15.35 +efficientnet_b0,99.79,9.92,1,224,5.29 +levit_128,99.57,9.926,1,224,9.21 +seresnet18,99.43,9.958,1,224,11.78 +edgenext_xx_small,99.11,10.004,1,256,1.33 +hgnetv2_b2,99.01,9.995,1,224,11.22 +pit_xs_224,98.69,10.031,1,224,10.62 +efficientvit_m3,98.64,10.045,1,224,6.9 +rexnet_100,98.61,10.039,1,224,4.8 +seresnet18,98.27,10.075,1,288,11.78 +seresnext26d_32x4d,98.23,10.069,1,224,16.81 +rexnetr_150,98.17,10.081,1,224,9.78 +botnet26t_256,97.98,10.099,1,256,12.49 +seresnext26t_32x4d,97.89,10.103,1,224,16.81 +eca_botnext26ts_256,97.8,10.118,1,256,10.59 +resnetblur18,97.7,10.13,1,224,11.69 +resnet18d,96.95,10.212,1,288,11.71 +tf_efficientnetv2_b0,96.59,10.256,1,192,7.14 +levit_256,96.35,10.273,1,224,18.89 +efficientnet_em,96.25,10.288,1,240,6.9 +rexnet_150,95.9,10.322,1,224,9.73 +halonet26t,95.84,10.328,1,256,12.48 +mixer_s16_224,95.44,10.369,1,224,18.53 +seresnext26ts,94.94,10.43,1,288,10.39 +levit_conv_256,94.65,10.461,1,224,18.89 +efficientvit_b1,94.33,10.5,1,256,9.1 +cs3darknet_focus_m,94.26,10.506,1,288,9.3 +eca_halonext26ts,94.18,10.514,1,256,10.76 +cs3darknet_focus_m,93.99,10.537,1,256,9.3 +hgnet_tiny,93.7,10.567,1,224,14.74 +gcresnext26ts,93.68,10.57,1,256,10.48 +resnet26d,93.37,10.604,1,224,16.01 +repghostnet_200,93.05,10.645,1,224,9.8 +edgenext_x_small,92.43,10.725,1,288,2.34 +cs3darknet_m,92.2,10.746,1,288,9.31 +mobilevitv2_100,91.89,10.775,1,256,4.9 +efficientvit_b1,91.28,10.851,1,224,9.1 +ese_vovnet19b_dw,91.06,10.875,1,288,6.54 +efficientnet_lite3,90.69,10.918,1,300,8.2 +efficientvit_m4,90.48,10.956,1,224,8.8 +cs3darknet_m,90.33,10.969,1,256,9.31 +convnext_pico_ols,90.01,11.004,1,224,9.06 +tf_efficientnetv2_b0,89.68,11.051,1,224,7.14 +convnext_atto,89.44,11.077,1,288,3.7 +resnetblur18,89.14,11.115,1,288,11.69 +lambda_resnet26t,89.02,11.126,1,256,10.96 +convnextv2_femto,88.99,11.135,1,288,5.23 +resnext50_32x4d,88.42,11.202,1,160,25.03 +fastvit_t12,88.28,11.221,1,256,7.55 +efficientnet_b0_g16_evos,88.06,11.258,1,224,8.11 +ghostnet_100,87.95,11.268,1,224,5.18 +convnextv2_atto,87.77,11.29,1,288,3.71 +lambda_resnet26rpt_256,87.55,11.317,1,256,10.99 +resnet26t,87.18,11.365,1,256,16.01 +resnext50_32x4d,86.94,11.395,1,176,25.03 +tf_efficientnet_em,86.66,11.438,1,240,6.9 +tf_efficientnet_lite3,86.35,11.474,1,300,8.2 +efficientvit_b1,86.24,11.493,1,288,9.1 +efficientvit_m0,85.82,11.565,1,224,2.35 +repvgg_a1,85.61,11.575,1,224,14.09 +pit_xs_distilled_224,85.6,11.564,1,224,11.0 +tf_efficientnet_b0,85.5,11.591,1,224,5.29 +convnextv2_femto,84.79,11.69,1,224,5.23 +mobilevitv2_125,84.78,11.688,1,256,7.48 +gcresnext26ts,84.57,11.723,1,288,10.48 +ghostnet_130,84.43,11.743,1,224,7.36 +resnet26,84.24,11.764,1,288,16.0 +efficientnet_cc_b0_4e,84.1,11.789,1,224,13.31 +mobilevit_xxs,83.93,11.822,1,256,1.27 +edgenext_small,83.4,11.884,1,256,5.59 +convnext_atto_ols,82.48,12.023,1,288,3.7 +resnet18,82.07,12.078,1,288,11.69 +rexnet_200,81.94,12.099,1,224,16.37 +resnet33ts,81.85,12.111,1,256,19.68 +rexnetr_200,81.41,12.177,1,224,16.52 +seresnext26d_32x4d,81.39,12.178,1,288,16.81 +hgnetv2_b2,81.32,12.191,1,288,11.22 +deit_tiny_patch16_224,81.06,12.197,1,224,5.72 +dla34,80.92,12.257,1,224,15.74 +vit_tiny_r_s16_p8_384,80.91,12.256,1,384,6.36 +resnet34,80.89,12.257,1,224,21.8 +efficientformerv2_s0,80.24,12.365,1,224,3.6 +hgnetv2_b3,80.24,12.357,1,224,16.29 +regnetx_032,80.0,12.395,1,224,15.3 +ecaresnet26t,79.04,12.546,1,256,16.01 +resnet26d,78.81,12.583,1,288,16.01 +tf_efficientnetv2_b1,78.59,12.626,1,192,8.14 +mobileone_s4,78.54,12.626,1,224,14.95 +darknet17,78.53,12.631,1,256,14.3 +resnet32ts,78.49,12.634,1,288,17.96 +efficientnet_cc_b0_8e,78.27,12.671,1,224,24.01 +fastvit_sa12,78.11,12.697,1,256,11.58 +regnetz_005,78.08,12.705,1,224,7.12 +tf_efficientnetv2_b2,77.95,12.727,1,208,10.1 +seresnext26t_32x4d,77.84,12.74,1,288,16.81 +xcit_nano_12_p16_224,76.77,12.935,1,224,3.05 +convnext_pico,76.48,12.968,1,224,9.05 +resnext50_32x4d,76.13,13.026,1,224,25.03 +edgenext_small_rw,76.06,13.038,1,256,7.83 +tf_efficientnetv2_b1,75.93,13.072,1,240,8.14 +efficientnet_b1_pruned,75.54,13.138,1,240,6.33 +tf_efficientnet_cc_b0_4e,75.53,13.139,1,224,13.31 +resnet34d,75.17,13.196,1,224,21.82 +skresnet18,74.63,13.294,1,224,11.96 +tinynet_a,74.63,13.286,1,192,6.19 +resnet33ts,74.49,13.318,1,288,19.68 +efficientvit_m5,73.98,13.416,1,224,12.47 +fbnetv3_b,73.94,13.426,1,224,8.6 +resnest26d,73.62,13.478,1,224,17.07 +eca_resnet33ts,73.59,13.48,1,256,19.68 +resnext50d_32x4d,73.4,13.513,1,224,25.05 +regnetz_b16,73.29,13.544,1,224,9.72 +convnext_pico_ols,73.18,13.56,1,288,9.06 +fastvit_s12,73.13,13.565,1,256,9.47 +nf_regnet_b0,73.1,13.577,1,192,8.76 +repvgg_b0,73.06,13.582,1,224,15.82 +resnet50d,72.99,13.592,1,160,25.58 +resnet26t,72.97,13.595,1,320,16.01 +gernet_l,72.73,13.644,1,256,31.08 +resnet50,72.62,13.661,1,160,25.56 +resnetaa34d,72.41,13.706,1,224,21.82 +regnetz_005,71.79,13.827,1,288,7.12 +dla60x,71.78,13.825,1,224,17.35 +resnet32ts,71.73,13.835,1,256,17.96 +vit_tiny_patch16_384,71.63,13.85,1,384,5.79 +tf_efficientnet_cc_b0_8e,71.62,13.862,1,224,24.01 +edgenext_small,71.48,13.881,1,320,5.59 +levit_256d,71.24,13.93,1,224,26.21 +pvt_v2_b0,71.09,13.963,1,224,3.67 +levit_384,70.96,13.987,1,224,39.13 +convnext_femto,70.73,14.033,1,288,5.22 +cspresnext50,70.51,14.072,1,256,20.57 +resnet50,70.29,14.117,1,176,25.56 +efficientnet_b1,70.28,14.123,1,224,7.79 +nf_regnet_b0,70.25,14.133,1,256,8.76 +efficientnet_b1,70.19,14.139,1,256,7.79 +rexnetr_200,70.18,14.142,1,288,16.52 +efficientnet_b2_pruned,70.05,14.176,1,260,8.31 +regnetx_040,69.8,14.216,1,224,22.12 +levit_conv_256d,69.66,14.25,1,224,26.21 +repvit_m1,69.25,14.34,1,224,5.49 +pit_s_224,68.96,14.396,1,224,23.46 +tf_efficientnetv2_b2,68.65,14.465,1,260,10.1 +convit_tiny,68.61,14.477,1,224,5.71 +hgnetv2_b4,68.28,14.537,1,224,19.8 +fbnetv3_b,68.27,14.548,1,256,8.6 +repvit_m1_1,68.27,14.547,1,224,8.8 +pit_s_distilled_224,68.06,14.584,1,224,24.04 +convnext_nano_ols,68.05,14.592,1,224,15.65 +mobilevitv2_150,67.96,14.608,1,256,10.59 +hgnet_tiny,67.68,14.668,1,288,14.74 +convnextv2_nano,67.59,14.691,1,224,15.62 +fbnetv3_d,67.58,14.695,1,224,10.31 +repvit_m2,67.43,14.729,1,224,8.8 +mobilevit_xs,67.34,14.746,1,256,2.32 +efficientnet_b2,67.32,14.752,1,256,9.11 +eca_resnet33ts,67.3,14.753,1,288,19.68 +dpn68,67.16,14.781,1,224,12.61 +repvit_m1_0,67.13,14.798,1,224,7.3 +seresnet34,67.11,14.799,1,224,21.96 +darknet21,67.05,14.81,1,256,20.86 +crossvit_tiny_240,66.95,14.831,1,240,7.01 +resnet34,66.86,14.855,1,288,21.8 +res2net50_48w_2s,66.42,14.951,1,224,25.29 +deit_small_patch16_224,66.3,14.97,1,224,22.05 +edgenext_small_rw,66.23,14.99,1,320,7.83 +convnext_femto_ols,66.17,15.011,1,288,5.23 +xcit_nano_12_p16_384,66.05,15.042,1,384,3.05 +xcit_tiny_12_p16_224,65.97,15.061,1,224,6.72 +eca_vovnet39b,65.95,15.057,1,224,22.6 +hgnetv2_b3,65.91,15.062,1,288,16.29 +nf_ecaresnet26,65.82,15.089,1,224,16.0 +dpn48b,65.5,15.164,1,224,9.13 +nf_resnet26,65.5,15.158,1,224,16.0 +tiny_vit_5m_224,65.5,15.16,1,224,12.08 +repvit_m0_9,65.43,15.189,1,224,5.49 +resnet34d,65.35,15.193,1,288,21.82 +crossvit_9_240,65.34,15.205,1,240,8.55 +vit_small_patch16_224,65.24,15.219,1,224,22.05 +vit_small_patch32_384,65.21,15.227,1,384,22.92 +legacy_seresnet34,65.08,15.266,1,224,21.96 +ecaresnet26t,64.74,15.341,1,320,16.01 +efficientformerv2_s1,64.64,15.371,1,224,6.19 +levit_conv_384,64.63,15.366,1,224,39.13 +deit_small_distilled_patch16_224,64.61,15.372,1,224,22.44 +resnet50,64.51,15.39,1,224,25.56 +fbnetv3_d,64.45,15.413,1,256,10.31 +gcresnet33ts,64.41,15.421,1,256,19.88 +xcit_nano_12_p8_224,64.37,15.435,1,224,3.05 +mixer_b32_224,64.25,15.457,1,224,60.29 +ecaresnet50t,64.06,15.502,1,160,25.57 +deit3_small_patch16_224,64.01,15.52,1,224,22.06 +rexnetr_300,63.86,15.548,1,224,34.81 +resnetaa50,63.85,15.555,1,224,25.56 +resnet50c,63.76,15.578,1,224,25.58 +dla60,63.69,15.594,1,224,22.04 +eva02_tiny_patch14_336,63.67,15.6,1,336,5.76 +resnet50d,63.52,15.634,1,224,25.58 +resnetaa34d,63.47,15.648,1,288,21.82 +sehalonet33ts,63.29,15.693,1,256,13.69 +tf_efficientnet_b1,63.15,15.73,1,240,7.79 +eva02_tiny_patch14_224,63.08,15.756,1,224,5.5 +convnext_nano,63.02,15.761,1,224,15.59 +crossvit_9_dagger_240,63.01,15.768,1,240,8.78 +resnet50t,62.97,15.772,1,224,25.57 +regnetz_b16,62.94,15.784,1,288,9.72 +mobilevitv2_175,62.88,15.796,1,256,14.25 +gmixer_24_224,62.87,15.797,1,224,24.72 +ese_vovnet39b,62.86,15.8,1,224,24.57 +resnetv2_50,62.7,15.843,1,224,25.55 +convnextv2_pico,62.49,15.9,1,224,9.07 +regnety_016,62.47,15.905,1,224,11.2 +convmixer_1024_20_ks9_p14,62.44,15.912,1,224,24.38 +regnety_032,62.22,15.964,1,224,19.44 +poolformerv2_s24,62.2,15.972,1,224,21.34 +sebotnet33ts_256,62.0,16.018,1,256,13.7 +resnext50_32x4d,61.88,16.049,1,288,25.03 +resnetv2_50t,61.87,16.056,1,224,25.57 +sedarknet21,61.82,16.069,1,256,20.95 +poolformer_s24,61.78,16.078,1,224,21.39 +legacy_seresnext50_32x4d,61.71,16.1,1,224,27.56 +seresnet50,61.4,16.177,1,160,28.09 +ecaresnet50d_pruned,61.19,16.232,1,288,19.94 +seresnet33ts,61.19,16.237,1,288,19.78 +regnetx_064,61.04,16.277,1,224,26.21 +regnetz_c16,60.87,16.326,1,256,13.46 +cs3darknet_focus_l,60.81,16.338,1,256,21.15 +resnetv2_50d,60.71,16.364,1,224,25.57 +rexnet_300,60.64,16.383,1,224,34.71 +hgnetv2_b4,60.48,16.423,1,288,19.8 +dpn68b,60.42,16.449,1,224,12.61 +resnetaa50d,60.42,16.443,1,224,25.58 +tf_efficientnetv2_b3,60.41,16.451,1,240,14.36 +efficientnet_b2,60.08,16.541,1,288,9.11 +resnext50d_32x4d,60.02,16.55,1,288,25.05 +flexivit_small,59.88,16.587,1,240,22.06 +gmlp_s16_224,59.87,16.6,1,224,19.42 +resnet50s,59.72,16.639,1,224,25.68 +cspresnet50,59.68,16.646,1,256,21.62 +tf_efficientnet_b2,59.68,16.655,1,260,9.11 +resnet51q,59.45,16.712,1,256,35.7 +tf_mixnet_s,59.43,16.728,1,224,4.13 +efficientformer_l3,59.35,16.753,1,224,31.41 +hgnet_small,59.1,16.812,1,224,24.36 +bat_resnext26ts,59.07,16.823,1,256,10.73 +ecaresnet50d_pruned,59.0,16.833,1,224,19.94 +convnext_tiny,58.9,16.87,1,224,28.59 +efficientnet_lite4,58.86,16.881,1,380,13.01 +gcresnet33ts,58.84,16.884,1,288,19.88 +seresnet33ts,58.51,16.988,1,256,19.78 +efficientvit_b2,58.48,16.994,1,224,24.33 +seresnet34,58.46,17.003,1,288,21.96 +visformer_small,58.42,17.01,1,224,40.22 +cspresnet50d,58.36,17.027,1,256,21.64 +resnetrs50,58.34,17.031,1,160,35.69 +coat_lite_tiny,58.24,17.063,1,224,5.72 +nf_regnet_b1,58.24,17.068,1,256,10.22 +resnetblur50,58.22,17.07,1,224,25.56 +efficientvit_b2,58.16,17.085,1,288,24.33 +efficientnet_el_pruned,57.87,17.175,1,300,10.59 +tf_efficientnet_el,57.67,17.232,1,300,10.59 +nf_seresnet26,57.65,17.239,1,224,17.4 +cs3darknet_l,57.64,17.242,1,256,21.16 +repvgg_a2,57.52,17.278,1,224,28.21 +inception_next_tiny,57.48,17.288,1,224,28.06 +coatnet_pico_rw_224,57.46,17.295,1,224,10.85 +efficientnet_el,57.41,17.313,1,300,10.59 +nf_regnet_b1,57.28,17.357,1,288,10.22 +ecaresnetlight,56.96,17.449,1,224,30.16 +hrnet_w18_small,56.78,17.506,1,224,13.19 +xcit_tiny_12_p16_384,56.22,17.686,1,384,6.72 +resnetblur50d,56.13,17.711,1,224,25.58 +ecaresnet50t,56.01,17.75,1,224,25.57 +convnext_pico,55.92,17.778,1,288,9.05 +efficientnet_cc_b1_8e,55.92,17.777,1,240,39.72 +ghostnetv2_100,55.86,17.804,1,224,6.16 +regnetv_040,55.71,17.841,1,224,20.64 +tf_efficientnet_lite4,55.69,17.848,1,380,13.01 +cs3darknet_focus_l,55.54,17.901,1,288,21.15 +resnet50,55.49,17.917,1,288,25.56 +efficientnet_b3_pruned,55.48,17.922,1,300,9.86 +ghostnetv2_160,55.39,17.953,1,224,12.39 +haloregnetz_b,55.34,17.964,1,224,11.68 +ecaresnet50d,55.26,17.988,1,224,25.58 +nf_regnet_b2,55.2,18.012,1,272,14.31 +tf_efficientnet_cc_b1_8e,55.14,18.033,1,240,39.72 +ghostnetv2_130,55.06,18.063,1,224,8.96 +tiny_vit_11m_224,54.89,18.114,1,224,20.35 +resnest50d_1s4x24d,54.69,18.175,1,224,25.68 +vit_srelpos_small_patch16_224,54.55,18.224,1,224,21.97 +cs3darknet_l,54.51,18.243,1,288,21.16 +mixnet_s,54.48,18.254,1,224,4.13 +convnextv2_pico,54.46,18.255,1,288,9.07 +coat_lite_mini,54.28,18.316,1,224,11.01 +regnetz_b16_evos,54.27,18.323,1,224,9.74 +seresnet50,54.11,18.374,1,224,28.09 +legacy_seresnet50,54.04,18.401,1,224,28.09 +resnetv2_50d_frn,53.81,18.476,1,224,25.59 +gcresnext50ts,53.8,18.481,1,256,15.67 +convnext_tiny_hnf,53.73,18.504,1,224,28.59 +efficientvit_b2,53.38,18.629,1,256,24.33 +resnet50t,53.07,18.736,1,288,25.57 +convnextv2_nano,52.9,18.794,1,288,15.62 +seresnext50_32x4d,52.77,18.838,1,224,27.56 +resnet50d,52.69,18.872,1,288,25.58 +resnet51q,52.69,18.872,1,288,35.7 +regnety_040,52.62,18.895,1,224,20.65 +tf_efficientnetv2_b3,52.42,18.971,1,300,14.36 +eva02_small_patch14_224,52.38,18.988,1,224,21.62 +resmlp_24_224,52.29,19.017,1,224,30.02 +nf_regnet_b2,52.27,19.021,1,240,14.31 +efficientnet_b3,52.2,19.054,1,288,12.23 +ecaresnet50t,52.11,19.079,1,288,25.57 +convnextv2_tiny,52.1,19.083,1,224,28.64 +mobilevitv2_200,51.97,19.13,1,256,18.45 +regnetz_c16,51.96,19.141,1,320,13.46 +fbnetv3_g,51.93,19.15,1,240,16.62 +seresnet50t,51.84,19.184,1,224,28.1 +resnest50d_4s2x40d,51.75,19.211,1,224,30.42 +vit_relpos_small_patch16_rpn_224,51.59,19.279,1,224,21.97 +twins_pcpvt_small,51.53,19.299,1,224,24.11 +ecaresnet50t,51.5,19.31,1,256,25.57 +nest_tiny,51.49,19.315,1,224,17.06 +resnet61q,51.38,19.356,1,256,36.85 +xcit_tiny_12_p8_224,51.12,19.456,1,224,6.71 +resnetaa50,51.04,19.488,1,288,25.56 +efficientnetv2_rw_t,50.79,19.588,1,224,13.65 +vit_small_resnet26d_224,50.39,19.736,1,224,63.61 +fbnetv3_g,50.35,19.756,1,288,16.62 +vit_relpos_small_patch16_224,50.18,19.818,1,224,21.98 +nest_tiny_jx,50.12,19.842,1,224,17.06 +seresnext50_32x4d,50.07,19.866,1,288,27.56 +resnetrs50,50.06,19.868,1,224,35.69 +seresnetaa50d,49.92,19.926,1,224,28.11 +gcresnext50ts,49.84,19.958,1,288,15.67 +regnety_032,49.4,20.136,1,288,19.44 +resnest50d,49.3,20.178,1,224,27.48 +coatnet_bn_0_rw_224,49.25,20.193,1,224,27.44 +regnetx_080,49.22,20.207,1,224,39.57 +regnety_040,49.22,20.213,1,288,20.65 +vovnet39a,49.21,20.212,1,224,22.6 +ecaresnetlight,49.2,20.22,1,288,30.16 +repvit_m3,48.97,20.319,1,224,10.68 +deit3_medium_patch16_224,48.76,20.407,1,224,38.85 +vovnet57a,48.68,20.434,1,224,36.64 +ese_vovnet39b,48.64,20.447,1,288,24.57 +edgenext_base,48.62,20.459,1,320,18.51 +rexnetr_300,48.55,20.49,1,288,34.81 +resnetaa50d,48.42,20.545,1,288,25.58 +regnetz_b16_evos,48.25,20.622,1,288,9.74 +twins_svt_small,48.23,20.629,1,224,24.06 +cspdarknet53,48.09,20.684,1,256,27.64 +lambda_resnet50ts,47.99,20.727,1,256,21.54 +tf_efficientnet_b3,47.96,20.744,1,300,12.23 +dla102x,47.88,20.779,1,224,26.31 +resnetblur50,47.82,20.806,1,288,25.56 +efficientnet_b3,47.81,20.812,1,320,12.23 +nf_regnet_b3,47.8,20.816,1,288,18.59 +dpn68b,47.67,20.87,1,288,12.61 +nextvit_small,47.64,20.884,1,224,31.76 +regnetv_040,47.57,20.914,1,288,20.64 +ecaresnet50d,47.14,21.106,1,288,25.58 +res2net50_26w_4s,47.12,21.115,1,224,25.7 +resnetv2_50,47.07,21.139,1,288,25.55 +vit_base_patch32_224,47.01,21.161,1,224,88.22 +resnetblur50d,46.99,21.17,1,288,25.58 +cs3sedarknet_l,46.91,21.21,1,256,21.91 +botnet50ts_256,46.78,21.266,1,256,22.74 +res2next50,46.65,21.326,1,224,24.67 +resnet61q,46.55,21.374,1,288,36.85 +convnext_nano,46.5,21.397,1,288,15.59 +mobilevit_s,46.41,21.44,1,256,5.58 +tiny_vit_21m_224,46.36,21.465,1,224,33.22 +ese_vovnet57b,46.33,21.472,1,224,38.61 +coatnet_0_rw_224,46.3,21.486,1,224,27.44 +res2net50d,46.29,21.499,1,224,25.72 +vit_base_patch32_clip_quickgelu_224,46.24,21.52,1,224,87.85 +convnext_nano_ols,46.22,21.535,1,288,15.65 +regnety_080,46.14,21.566,1,224,39.18 +vit_base_patch32_clip_224,46.06,21.604,1,224,88.22 +coatnet_0_224,46.05,21.609,1,224,25.04 +coatnet_nano_cc_224,46.01,21.631,1,224,13.76 +eca_nfnet_l0,45.97,21.644,1,224,24.14 +fastvit_sa24,45.96,21.652,1,256,21.55 +regnety_080_tv,45.96,21.647,1,224,39.38 +mobileone_s0,45.88,21.695,1,224,5.29 +pvt_v2_b1,45.54,21.85,1,224,14.01 +pvt_v2_b2_li,45.54,21.854,1,224,22.55 +legacy_xception,45.5,21.868,1,299,22.86 +dla60_res2next,45.49,21.875,1,224,17.03 +efficientnetv2_rw_t,45.15,22.048,1,288,13.65 +convnext_tiny,45.14,22.046,1,288,28.59 +crossvit_15_240,45.08,22.079,1,240,27.53 +regnetz_c16_evos,45.08,22.077,1,256,13.49 +vit_base_patch32_clip_256,44.73,22.25,1,256,87.86 +dla60_res2net,44.68,22.274,1,224,20.85 +regnety_064,44.51,22.357,1,224,30.58 +resnet101,44.47,22.382,1,160,44.55 +tresnet_m,44.45,22.384,1,224,31.39 +convit_small,44.44,22.401,1,224,27.78 +hgnet_small,44.41,22.409,1,288,24.36 +swin_s3_tiny_224,44.37,22.427,1,224,28.33 +poolformerv2_s36,44.28,22.475,1,224,30.79 +edgenext_base,44.14,22.539,1,256,18.51 +skresnet34,44.12,22.561,1,224,22.28 +skresnet50d,44.09,22.573,1,224,25.82 +seresnet50t,44.06,22.589,1,288,28.1 +vit_medium_patch16_gap_240,44.06,22.584,1,240,44.4 +mvitv2_tiny,44.03,22.604,1,224,24.17 +seresnet50,43.99,22.623,1,288,28.09 +dla102,43.92,22.659,1,224,33.27 +volo_d1_224,43.89,22.678,1,224,26.63 +crossvit_small_240,43.82,22.717,1,240,26.86 +nf_regnet_b3,43.8,22.729,1,320,18.59 +regnetv_064,43.75,22.747,1,224,30.58 +gc_efficientnetv2_rw_t,43.67,22.802,1,224,13.68 +skresnet50,43.6,22.83,1,224,25.8 +davit_tiny,43.54,22.855,1,224,28.36 +coatnet_nano_rw_224,43.41,22.927,1,224,15.14 +halonet50ts,43.38,22.944,1,256,22.73 +gcvit_xxtiny,43.37,22.961,1,224,12.0 +nfnet_l0,43.29,22.99,1,224,35.07 +tnt_s_patch16_224,43.16,23.057,1,224,23.76 +poolformer_s36,43.12,23.089,1,224,30.86 +cs3sedarknet_l,42.91,23.198,1,288,21.91 +focalnet_tiny_srf,42.82,23.243,1,224,28.43 +vit_srelpos_medium_patch16_224,42.76,23.275,1,224,38.74 +darknetaa53,42.55,23.393,1,256,36.02 +seresnetaa50d,42.5,23.422,1,288,28.11 +vit_medium_patch16_gap_256,42.42,23.467,1,256,38.86 +resnet101,42.27,23.552,1,176,44.55 +coatnet_rmlp_nano_rw_224,42.23,23.574,1,224,15.15 +resnext101_32x4d,42.11,23.639,1,224,44.18 +mixnet_m,42.06,23.673,1,224,5.01 +tf_mixnet_m,41.96,23.726,1,224,5.01 +cs3darknet_focus_x,41.88,23.77,1,256,35.02 +mobilevitv2_150,41.85,23.785,1,384,10.59 +cs3sedarknet_xdw,41.8,23.817,1,256,21.6 +regnetx_120,41.78,23.827,1,224,46.11 +nf_seresnet50,41.51,23.981,1,224,28.09 +ecaresnet50t,41.38,24.05,1,320,25.57 +mixer_b16_224,41.38,24.054,1,224,59.88 +crossvit_15_dagger_240,41.36,24.075,1,240,28.21 +vit_relpos_medium_patch16_rpn_224,41.3,24.105,1,224,38.73 +swinv2_cr_tiny_ns_224,41.2,24.165,1,224,28.33 +maxvit_pico_rw_256,41.14,24.206,1,256,7.46 +regnetz_040,41.13,24.202,1,256,27.12 +swinv2_cr_tiny_224,41.08,24.228,1,224,28.33 +regnety_040_sgn,41.06,24.249,1,224,20.65 +caformer_s18,41.01,24.277,1,224,26.34 +xception41p,40.98,24.291,1,299,26.91 +coatnet_rmlp_0_rw_224,40.92,24.326,1,224,27.45 +nf_resnet50,40.88,24.358,1,256,25.56 +vit_base_patch32_plus_256,40.85,24.373,1,256,119.48 +cs3darknet_x,40.8,24.4,1,256,35.05 +xception41,40.78,24.416,1,299,26.97 +efficientvit_l1,40.76,24.423,1,224,52.65 +lamhalobotnet50ts_256,40.65,24.492,1,256,22.57 +nf_ecaresnet50,40.52,24.572,1,224,25.56 +cait_xxs24_224,40.5,24.591,1,224,11.96 +regnetz_040_h,40.36,24.669,1,256,28.94 +efficientformerv2_s2,40.22,24.759,1,224,12.71 +darknetaa53,40.2,24.77,1,288,36.02 +densenetblur121d,40.15,24.804,1,224,8.0 +gcresnet50t,40.12,24.822,1,256,25.9 +swin_tiny_patch4_window7_224,40.06,24.855,1,224,28.29 +inception_v3,39.82,25.009,1,299,23.83 +eca_nfnet_l0,39.61,25.137,1,288,24.14 +convnextv2_tiny,39.59,25.153,1,288,28.64 +efficientnet_b0_gn,39.48,25.225,1,224,5.29 +repvit_m1_5,39.48,25.224,1,224,14.64 +cspresnet50w,39.38,25.285,1,256,28.12 +regnetz_d8,38.99,25.538,1,256,23.37 +ecaresnet101d_pruned,38.92,25.592,1,224,24.88 +darknet53,38.87,25.617,1,256,41.61 +halo2botnet50ts_256,38.72,25.719,1,256,22.64 +nf_resnet50,38.46,25.896,1,288,25.56 +efficientnet_b4,38.45,25.9,1,320,19.34 +efficientnet_b0_g8_gn,38.42,25.922,1,224,6.56 +vit_relpos_medium_patch16_cls_224,38.33,25.983,1,224,38.76 +tf_mixnet_l,38.28,26.022,1,224,7.33 +regnetz_c16_evos,38.24,26.043,1,320,13.49 +skresnext50_32x4d,38.13,26.115,1,224,27.48 +resnet101,38.06,26.169,1,224,44.55 +resnet101c,37.88,26.292,1,224,44.57 +nfnet_l0,37.8,26.352,1,288,35.07 +resnet101d,37.73,26.399,1,224,44.57 +mixnet_l,37.53,26.547,1,224,7.33 +regnetz_d32,37.49,26.567,1,256,27.58 +crossvit_18_240,37.48,26.576,1,240,43.27 +efficientnetv2_s,37.46,26.592,1,288,21.46 +maxxvitv2_nano_rw_256,37.26,26.731,1,256,23.7 +cs3darknet_x,37.22,26.755,1,288,35.05 +ecaresnet101d_pruned,37.16,26.807,1,288,24.88 +coatnext_nano_rw_224,37.09,26.854,1,224,14.7 +ese_vovnet39b_evos,37.08,26.863,1,224,24.58 +xcit_small_12_p16_224,36.97,26.945,1,224,26.25 +regnety_120,36.94,26.957,1,224,51.82 +gcresnet50t,36.9,26.992,1,288,25.9 +resnetv2_101,36.81,27.061,1,224,44.54 +levit_512,36.8,27.068,1,224,95.17 +resnetv2_101d,36.79,27.075,1,224,44.56 +regnety_080,36.56,27.243,1,288,39.18 +regnetz_d8_evos,36.49,27.305,1,256,23.46 +maxvit_rmlp_pico_rw_256,36.46,27.332,1,256,7.52 +dla102x2,36.45,27.33,1,224,41.28 +resnetaa101d,36.43,27.345,1,224,44.57 +densenet121,36.42,27.35,1,224,7.98 +resnet101s,36.35,27.401,1,224,44.67 +levit_conv_512,36.19,27.525,1,224,95.17 +regnety_064,36.1,27.593,1,288,30.58 +vit_relpos_medium_patch16_224,36.08,27.607,1,224,38.75 +selecsls60,35.99,27.68,1,224,30.67 +convnext_tiny_hnf,35.91,27.736,1,288,28.59 +xcit_nano_12_p8_384,35.86,27.782,1,384,3.05 +xcit_tiny_24_p16_224,35.79,27.846,1,224,12.12 +tf_efficientnetv2_s,35.77,27.849,1,300,21.46 +resnet50_gn,35.65,27.945,1,224,25.56 +vit_base_resnet26d_224,35.65,27.939,1,224,101.4 +resnetv2_50d_evos,35.51,28.052,1,224,25.59 +resnetv2_50d_gn,35.48,28.077,1,224,25.57 +convnext_tiny,35.4,28.138,1,384,28.59 +levit_conv_512d,35.36,28.174,1,224,92.5 +efficientnetv2_rw_s,35.35,28.184,1,288,23.94 +seresnext101_32x4d,35.28,28.24,1,224,48.96 +regnetx_160,35.27,28.238,1,224,54.28 +crossvit_18_dagger_240,35.24,28.275,1,240,44.27 +resmlp_36_224,35.24,28.273,1,224,44.69 +levit_512d,35.22,28.283,1,224,92.5 +legacy_seresnext101_32x4d,35.18,28.313,1,224,48.96 +resnetblur101d,35.12,28.366,1,224,44.57 +maxvit_nano_rw_256,35.11,28.365,1,256,15.45 +nf_regnet_b4,35.02,28.45,1,320,30.21 +hgnetv2_b5,34.92,28.527,1,224,39.57 +darknet53,34.91,28.539,1,288,41.61 +vit_relpos_base_patch32_plus_rpn_256,34.86,28.579,1,256,119.42 +resnet101d,34.76,28.665,1,256,44.57 +mobilevitv2_175,34.66,28.741,1,384,14.25 +regnetv_064,34.6,28.79,1,288,30.58 +vit_medium_patch16_reg4_256,34.54,28.846,1,256,38.87 +vit_medium_patch16_reg4_gap_256,34.51,28.871,1,256,38.87 +efficientvit_b3,34.48,28.893,1,256,48.65 +vit_small_r26_s32_224,34.26,29.081,1,224,36.43 +hrnet_w18_small_v2,34.09,29.227,1,224,15.6 +gcvit_xtiny,34.04,29.274,1,224,19.98 +repvgg_b1g4,33.94,29.354,1,224,39.97 +nextvit_base,33.92,29.369,1,224,44.82 +convformer_s18,33.87,29.421,1,224,26.77 +twins_pcpvt_base,33.77,29.509,1,224,43.83 +regnetz_040,33.51,29.736,1,320,27.12 +res2net50_26w_6s,33.51,29.737,1,224,37.05 +regnety_040_sgn,33.5,29.739,1,288,20.65 +dpn92,33.38,29.853,1,224,37.67 +regnetz_040_h,33.37,29.86,1,320,28.94 +efficientvit_l2,33.35,29.874,1,256,63.71 +ecaresnet101d,33.18,30.033,1,224,44.57 +resnetrs101,33.16,30.049,1,192,63.62 +vit_small_resnet50d_s16_224,33.15,30.054,1,224,57.53 +selecsls42,32.94,30.251,1,224,30.35 +vit_small_patch16_384,32.89,30.299,1,384,22.2 +efficientvit_b3,32.71,30.465,1,288,48.65 +seresnet101,32.53,30.638,1,224,49.33 +legacy_seresnet101,32.5,30.668,1,224,49.33 +resnet101,32.39,30.768,1,288,44.55 +resnet152,32.27,30.879,1,160,60.19 +inception_next_small,32.25,30.901,1,224,49.37 +efficientvit_b3,32.15,30.999,1,224,48.65 +coat_lite_small,32.05,31.097,1,224,19.84 +cs3sedarknet_x,32.04,31.1,1,256,35.4 +efficientnet_b3_gn,32.01,31.134,1,288,11.73 +poolformerv2_m36,32.0,31.141,1,224,56.08 +vit_base_patch32_384,31.93,31.21,1,384,88.3 +nf_regnet_b4,31.88,31.263,1,384,30.21 +fastvit_sa36,31.82,31.317,1,256,31.53 +efficientnet_b4,31.76,31.379,1,384,19.34 +densenetblur121d,31.68,31.462,1,288,8.0 +cs3edgenet_x,31.66,31.478,1,256,47.82 +resnet152,31.62,31.519,1,176,60.19 +repvit_m2_3,31.61,31.53,1,224,23.69 +poolformer_m36,31.49,31.647,1,224,56.17 +selecsls60b,31.42,31.719,1,224,32.77 +vit_base_patch16_rpn_224,31.41,31.734,1,224,86.54 +deit3_small_patch16_384,31.29,31.856,1,384,22.21 +focalnet_tiny_lrf,31.28,31.863,1,224,28.65 +tf_efficientnet_b4,31.26,31.884,1,380,19.34 +res2net50_14w_8s,31.21,31.935,1,224,25.06 +convnext_small,31.19,31.95,1,224,50.22 +hgnetv2_b5,31.15,31.995,1,288,39.57 +maxvit_rmlp_nano_rw_256,31.14,31.993,1,256,15.5 +xcit_tiny_24_p16_384,31.09,32.069,1,384,12.12 +efficientvit_l2,31.04,32.103,1,224,63.71 +resnet50_gn,30.96,32.194,1,288,25.56 +efficientvit_l2,30.9,32.252,1,288,63.71 +selecsls42b,30.8,32.368,1,224,32.46 +vit_base_patch32_clip_384,30.63,32.537,1,384,88.3 +resnetv2_50x1_bit,30.6,32.565,1,224,25.55 +nest_small,30.32,32.867,1,224,38.35 +vit_base_patch16_gap_224,30.08,33.14,1,224,86.57 +deit3_base_patch16_224,30.04,33.181,1,224,86.59 +vit_base_patch16_clip_quickgelu_224,30.04,33.179,1,224,86.19 +mixnet_xl,30.03,33.189,1,224,11.9 +efficientnetv2_s,29.98,33.254,1,384,21.46 +vit_base_patch16_224_miil,29.95,33.282,1,224,94.4 +efficientformer_l7,29.93,33.307,1,224,82.23 +nfnet_f0,29.92,33.319,1,192,71.49 +vit_base_patch16_224,29.92,33.31,1,224,86.57 +dm_nfnet_f0,29.79,33.469,1,192,71.49 +deit_base_patch16_224,29.77,33.481,1,224,86.57 +gc_efficientnetv2_rw_t,29.75,33.501,1,288,13.68 +eva02_small_patch14_336,29.68,33.591,1,336,22.13 +resnetaa101d,29.64,33.631,1,288,44.57 +nest_small_jx,29.62,33.655,1,224,38.35 +cs3sedarknet_x,29.53,33.76,1,288,35.4 +convnextv2_tiny,29.51,33.772,1,384,28.64 +tf_efficientnetv2_s,29.51,33.78,1,384,21.46 +mvitv2_small,29.48,33.811,1,224,34.87 +resnext101_32x8d,29.47,33.824,1,176,88.79 +pit_b_224,29.44,33.855,1,224,73.76 +repvgg_b1,29.33,33.984,1,224,57.42 +maxxvit_rmlp_nano_rw_256,29.32,33.998,1,256,16.78 +efficientnet_b3_g8_gn,29.31,34.011,1,288,14.25 +pit_b_distilled_224,29.26,34.069,1,224,74.79 +deit_base_distilled_patch16_224,29.13,34.224,1,224,87.34 +xcit_small_12_p16_384,29.13,34.224,1,384,26.25 +convnextv2_small,29.09,34.269,1,224,50.32 +tresnet_v2_l,29.06,34.305,1,224,46.17 +resnetblur101d,29.04,34.324,1,288,44.57 +regnetz_d8,29.03,34.34,1,320,23.37 +vit_base_patch16_clip_224,29.03,34.345,1,224,86.57 +nextvit_small,28.84,34.564,1,384,31.76 +vit_base_patch16_siglip_224,28.82,34.585,1,224,92.88 +swinv2_tiny_window8_256,28.77,34.644,1,256,28.35 +levit_conv_384_s8,28.66,34.782,1,224,39.12 +tresnet_l,28.61,34.843,1,224,55.99 +efficientformerv2_l,28.56,34.904,1,224,26.32 +xception65,28.52,34.957,1,299,39.92 +resnetv2_50d_evos,28.46,35.027,1,288,25.59 +resnetv2_50d_gn,28.46,35.032,1,288,25.57 +resnetv2_101,28.42,35.084,1,288,44.54 +resnet152c,28.31,35.214,1,224,60.21 +resnet101d,28.25,35.287,1,320,44.57 +regnetz_d32,28.23,35.315,1,320,27.58 +fastvit_ma36,28.2,35.351,1,256,44.07 +vit_base_resnet50d_224,28.2,35.357,1,224,110.97 +xcit_tiny_24_p8_224,28.17,35.389,1,224,12.11 +densenet169,28.15,35.419,1,224,14.15 +efficientnet_b3_gn,28.07,35.515,1,320,11.73 +vit_base_patch16_xp_224,28.0,35.611,1,224,86.51 +ecaresnet101d,27.96,35.651,1,288,44.57 +vit_base_patch32_clip_448,27.84,35.815,1,448,88.34 +efficientnetv2_rw_s,27.78,35.893,1,384,23.94 +resnet152s,27.77,35.899,1,224,60.32 +cs3edgenet_x,27.67,36.025,1,288,47.82 +xception65p,27.58,36.151,1,299,39.82 +resnest101e,27.47,36.301,1,256,48.28 +densenet121,27.43,36.356,1,288,7.98 +convnextv2_nano,27.42,36.364,1,384,15.62 +regnety_120,27.36,36.442,1,288,51.82 +regnetz_d8_evos,27.33,36.481,1,320,23.46 +cait_xxs36_224,27.32,36.503,1,224,17.3 +tnt_b_patch16_224,27.31,36.501,1,224,65.41 +dla169,27.28,36.548,1,224,53.39 +coatnet_1_rw_224,27.21,36.649,1,224,41.72 +flexivit_base,27.18,36.682,1,240,86.59 +ese_vovnet99b,27.13,36.754,1,224,63.2 +resnet152,26.95,36.992,1,224,60.19 +resnet152d,26.94,37.016,1,224,60.21 +seresnet101,26.8,37.206,1,288,49.33 +beitv2_base_patch16_224,26.72,37.316,1,224,86.53 +nf_resnet101,26.64,37.432,1,224,44.55 +beit_base_patch16_224,26.63,37.447,1,224,86.53 +twins_svt_base,26.61,37.465,1,224,56.07 +maxvit_tiny_rw_224,26.58,37.51,1,224,29.06 +resnetv2_152,26.55,37.553,1,224,60.19 +regnety_160,26.51,37.61,1,224,83.59 +vit_relpos_base_patch16_rpn_224,26.45,37.7,1,224,86.41 +resnetv2_152d,26.15,38.137,1,224,60.2 +coatnet_rmlp_1_rw_224,26.13,38.166,1,224,41.69 +cs3se_edgenet_x,26.07,38.25,1,256,50.72 +regnetz_e8,26.06,38.268,1,256,57.7 +nextvit_large,26.03,38.313,1,224,57.87 +samvit_base_patch16_224,25.96,38.411,1,224,86.46 +resnext101_64x4d,25.92,38.47,1,224,83.46 +vit_relpos_base_patch16_224,25.89,38.52,1,224,86.43 +nf_ecaresnet101,25.87,38.542,1,224,44.55 +resnetrs101,25.82,38.619,1,288,63.62 +pvt_v2_b2,25.81,38.645,1,224,25.36 +res2net50_26w_8s,25.76,38.713,1,224,48.4 +eca_nfnet_l1,25.7,38.803,1,256,41.41 +caformer_s36,25.66,38.862,1,224,39.3 +res2net101d,25.65,38.872,1,224,45.23 +efficientnet_b3_g8_gn,25.52,39.078,1,320,14.25 +res2net101_26w_4s,25.5,39.108,1,224,45.21 +inception_v4,25.41,39.25,1,299,42.68 +coatnet_1_224,25.35,39.33,1,224,42.23 +convmixer_768_32,25.35,39.337,1,224,21.11 +xcit_small_24_p16_224,25.29,39.432,1,224,47.67 +coatnet_rmlp_1_rw2_224,25.19,39.588,1,224,41.72 +resnet152d,25.12,39.699,1,256,60.21 +nfnet_f0,25.1,39.741,1,256,71.49 +poolformerv2_m48,25.05,39.803,1,224,73.35 +vit_base_r26_s32_224,25.05,39.807,1,224,101.38 +maxvit_tiny_rw_256,25.04,39.834,1,256,29.07 +tresnet_m,24.96,39.959,1,448,31.39 +vit_relpos_base_patch16_cls_224,24.94,39.997,1,224,86.43 +resnext101_32x8d,24.91,40.03,1,224,88.79 +vit_relpos_base_patch16_clsgap_224,24.86,40.115,1,224,86.43 +xcit_tiny_12_p8_384,24.76,40.28,1,384,6.71 +dm_nfnet_f0,24.74,40.318,1,256,71.49 +vit_base_patch16_reg4_gap_256,24.62,40.507,1,256,86.62 +dpn98,24.48,40.74,1,224,61.57 +wide_resnet50_2,24.46,40.774,1,176,68.88 +gmlp_b16_224,24.07,41.431,1,224,73.08 +vit_base_patch16_siglip_256,24.05,41.476,1,256,92.93 +gcvit_tiny,23.98,41.603,1,224,28.22 +levit_384_s8,23.87,41.794,1,224,39.12 +davit_small,23.68,42.124,1,224,49.75 +mixnet_xxl,23.56,42.341,1,224,23.96 +poolformer_m48,23.53,42.396,1,224,73.47 +twins_pcpvt_large,23.52,42.407,1,224,60.99 +eva02_base_patch16_clip_224,23.47,42.505,1,224,86.26 +resnet200,23.46,42.51,1,224,64.67 +nf_seresnet101,23.38,42.658,1,224,49.33 +maxvit_tiny_pm_256,23.24,42.933,1,256,30.09 +efficientvit_l2,23.17,43.043,1,384,63.71 +xcit_small_12_p8_224,23.13,43.134,1,224,26.21 +resnet152,23.07,43.239,1,288,60.19 +seresnext101_64x4d,23.04,43.291,1,224,88.23 +convformer_s36,22.99,43.386,1,224,40.01 +inception_next_base,22.95,43.455,1,224,86.67 +repvgg_b2g4,22.93,43.506,1,224,61.76 +volo_d2_224,22.88,43.599,1,224,58.68 +tresnet_xl,22.85,43.645,1,224,78.44 +maxxvit_rmlp_tiny_rw_256,22.79,43.777,1,256,29.64 +hgnet_base,22.77,43.808,1,224,71.58 +focalnet_small_srf,22.7,43.939,1,224,49.89 +seresnet152,22.65,44.045,1,224,66.82 +convnext_base,22.56,44.224,1,224,88.59 +convnext_small,22.45,44.442,1,288,50.22 +maxvit_tiny_tf_224,22.41,44.515,1,224,30.92 +xception71,22.39,44.561,1,299,42.34 +mobilevitv2_200,22.37,44.598,1,384,18.45 +mvitv2_small_cls,22.22,44.891,1,224,34.87 +cait_s24_224,22.13,45.067,1,224,46.92 +legacy_seresnet152,22.1,45.142,1,224,66.82 +swinv2_cr_small_224,22.07,45.196,1,224,49.7 +selecsls84,22.06,45.23,1,224,50.95 +cs3se_edgenet_x,21.96,45.423,1,320,50.72 +seresnext101_32x8d,21.85,45.669,1,224,93.57 +seresnext101d_32x8d,21.84,45.683,1,224,93.59 +swinv2_cr_small_ns_224,21.8,45.762,1,224,49.7 +vit_small_patch8_224,21.78,45.8,1,224,21.67 +caformer_s18,21.72,45.93,1,384,26.34 +nf_regnet_b5,21.71,45.963,1,384,49.74 +vit_small_r26_s32_384,21.54,46.309,1,384,36.47 +crossvit_base_240,21.5,46.397,1,240,105.03 +inception_resnet_v2,21.5,46.408,1,299,55.84 +swin_small_patch4_window7_224,21.43,46.55,1,224,49.61 +densenet201,21.3,46.854,1,224,20.01 +swinv2_tiny_window16_256,21.3,46.85,1,256,28.35 +wide_resnet50_2,21.3,46.84,1,224,68.88 +volo_d1_384,21.29,46.867,1,384,26.78 +vit_medium_patch16_gap_384,21.25,46.957,1,384,39.03 +convit_base,21.22,47.014,1,224,86.54 +pvt_v2_b3,21.2,47.06,1,224,45.24 +seresnextaa101d_32x8d,21.16,47.142,1,224,93.59 +hgnetv2_b6,21.15,47.171,1,224,75.26 +vit_small_patch16_18x2_224,21.11,47.258,1,224,64.67 +nest_base_jx,21.09,47.301,1,224,67.72 +resnet200d,21.09,47.302,1,256,64.69 +eca_nfnet_l1,21.05,47.388,1,320,41.41 +densenet161,21.02,47.463,1,224,28.68 +nest_base,20.8,47.968,1,224,67.72 +focalnet_small_lrf,20.74,48.098,1,224,50.34 +gcvit_small,20.74,48.118,1,224,51.09 +maxvit_rmlp_tiny_rw_256,20.72,48.148,1,256,29.15 +seresnet152d,20.72,48.153,1,256,66.84 +resnet152d,20.71,48.179,1,320,60.21 +nextvit_base,20.66,48.299,1,384,44.82 +vit_small_patch16_36x1_224,20.65,48.32,1,224,64.67 +efficientnet_b5,20.64,48.335,1,416,30.39 +maxvit_rmlp_small_rw_224,20.49,48.705,1,224,64.9 +resnetrs152,20.48,48.724,1,256,86.62 +resnext101_64x4d,20.46,48.756,1,288,83.46 +mvitv2_base,20.41,48.893,1,224,51.47 +convnextv2_base,20.33,49.071,1,224,88.72 +regnety_160,20.32,49.106,1,288,83.59 +hrnet_w18_ssld,20.05,49.77,1,224,21.3 +hrnet_w18,19.54,51.074,1,224,21.3 +crossvit_15_dagger_408,19.4,51.437,1,408,28.5 +mixer_l32_224,19.37,51.507,1,224,206.94 +maxvit_small_tf_224,19.35,51.563,1,224,68.93 +regnetz_e8,19.29,51.743,1,320,57.7 +efficientnetv2_m,19.18,52.035,1,320,54.14 +tiny_vit_21m_384,19.1,52.268,1,384,21.23 +convformer_s18,19.06,52.354,1,384,26.77 +resnet200,19.04,52.414,1,288,64.67 +eva02_base_patch14_224,18.97,52.61,1,224,85.76 +convnext_base,18.94,52.694,1,256,88.59 +caformer_m36,18.93,52.724,1,224,56.2 +halonet_h1,18.91,52.787,1,256,8.1 +efficientnet_b5,18.89,52.829,1,448,30.39 +seresnet152,18.79,53.123,1,288,66.82 +twins_svt_large,18.76,53.204,1,224,99.27 +wide_resnet50_2,18.74,53.249,1,288,68.88 +dpn131,18.71,53.352,1,224,79.25 +swin_s3_small_224,18.71,53.329,1,224,49.74 +vit_base_patch16_plus_240,18.67,53.459,1,240,117.56 +convnext_small,18.55,53.795,1,384,50.22 +nf_regnet_b5,18.54,53.841,1,456,49.74 +ecaresnet200d,18.27,54.62,1,256,64.69 +xcit_medium_24_p16_224,18.21,54.821,1,224,84.4 +hrnet_w32,18.13,55.048,1,224,41.23 +regnetx_320,18.12,55.073,1,224,107.81 +repvgg_b2,17.92,55.687,1,224,89.02 +efficientnetv2_rw_m,17.89,55.798,1,320,53.24 +focalnet_base_srf,17.67,56.484,1,224,88.15 +swinv2_cr_small_ns_256,17.64,56.598,1,256,49.7 +coat_tiny,17.45,57.214,1,224,5.5 +levit_conv_512_s8,17.38,57.446,1,224,74.05 +levit_512_s8,17.36,57.485,1,224,74.05 +dpn107,17.34,57.575,1,224,86.92 +seresnext101_32x8d,17.3,57.703,1,288,93.57 +coatnet_2_rw_224,17.27,57.778,1,224,73.87 +convformer_m36,17.23,57.93,1,224,57.05 +swin_s3_base_224,17.18,58.095,1,224,71.13 +davit_base,17.13,58.254,1,224,87.95 +swinv2_cr_base_224,16.91,59.034,1,224,87.88 +seresnet152d,16.86,59.213,1,320,66.84 +hgnetv2_b6,16.82,59.354,1,288,75.26 +seresnext101d_32x8d,16.8,59.399,1,288,93.59 +resnet200d,16.63,60.036,1,320,64.69 +swinv2_cr_base_ns_224,16.59,60.178,1,224,87.88 +coat_lite_medium,16.58,60.196,1,224,44.57 +resnetrs200,16.57,60.229,1,256,93.21 +hgnet_base,16.53,60.378,1,288,71.58 +cait_xxs24_384,16.5,60.52,1,384,12.03 +seresnextaa101d_32x8d,16.44,60.712,1,288,93.59 +focalnet_base_lrf,16.42,60.799,1,224,88.75 +volo_d3_224,16.39,60.905,1,224,86.33 +vit_relpos_base_patch16_plus_240,16.38,60.949,1,240,117.38 +wide_resnet101_2,16.37,60.994,1,176,126.89 +repvgg_b3g4,16.31,61.206,1,224,83.83 +hrnet_w40,16.28,61.301,1,224,57.56 +tf_efficientnet_b5,16.28,61.33,1,456,30.39 +ecaresnet200d,16.21,61.594,1,288,64.69 +vit_base_r50_s16_224,16.2,61.605,1,224,97.89 +hrnet_w18_ssld,16.19,61.65,1,288,21.3 +seresnet200d,16.19,61.664,1,256,71.86 +dm_nfnet_f1,16.17,61.746,1,224,132.63 +maxvit_rmlp_small_rw_256,16.12,61.934,1,256,64.9 +coatnet_2_224,16.05,62.19,1,224,74.68 +coatnet_rmlp_2_rw_224,16.05,62.216,1,224,73.88 +hrnet_w30,15.98,62.476,1,224,37.71 +swin_base_patch4_window7_224,15.92,62.688,1,224,87.77 +hrnet_w48,15.8,63.193,1,224,77.47 +xcit_small_24_p16_384,15.73,63.481,1,384,47.67 +mvitv2_base_cls,15.67,63.721,1,224,65.44 +hrnet_w48_ssld,15.62,63.92,1,224,77.47 +resnetv2_101x1_bit,15.5,64.422,1,224,44.54 +vit_large_patch32_224,15.38,64.898,1,224,305.51 +nfnet_f1,15.31,65.221,1,224,132.63 +tf_efficientnetv2_m,15.27,65.378,1,384,54.14 +pvt_v2_b4,15.22,65.611,1,224,62.56 +eca_nfnet_l2,15.2,65.695,1,320,56.72 +crossvit_18_dagger_408,15.18,65.761,1,408,44.61 +resnetrs152,15.16,65.876,1,320,86.62 +sequencer2d_s,15.13,65.982,1,224,27.65 +swinv2_small_window8_256,15.12,66.049,1,256,49.73 +caformer_b36,14.81,67.42,1,224,98.75 +vit_so150m_patch16_reg4_gap_256,14.79,67.533,1,256,134.13 +hrnet_w44,14.76,67.633,1,224,67.06 +pvt_v2_b5,14.76,67.666,1,224,81.96 +swinv2_base_window12_192,14.64,68.185,1,192,109.28 +seresnet200d,14.49,68.9,1,288,71.86 +nextvit_large,14.45,69.101,1,384,57.87 +seresnextaa101d_32x8d,14.44,69.13,1,320,93.59 +coat_mini,14.39,69.372,1,224,10.34 +efficientnetv2_m,14.33,69.663,1,416,54.14 +wide_resnet101_2,14.31,69.771,1,224,126.89 +tresnet_l,14.3,69.826,1,448,55.99 +regnety_160,14.2,70.322,1,384,83.59 +vit_so150m_patch16_reg4_map_256,14.19,70.354,1,256,141.48 +convnext_base,14.1,70.818,1,288,88.59 +resnetv2_50x1_bit,13.95,71.57,1,448,25.55 +convnext_large,13.92,71.749,1,224,197.77 +regnety_320,13.91,71.795,1,224,145.05 +resnetrs200,13.77,72.49,1,320,93.21 +coat_small,13.71,72.826,1,224,21.69 +maxxvit_rmlp_small_rw_256,13.61,73.351,1,256,66.01 +xcit_tiny_24_p8_384,13.54,73.776,1,384,12.11 +gcvit_base,13.34,74.838,1,224,90.32 +hrnet_w48_ssld,13.24,75.442,1,288,77.47 +swinv2_base_window8_256,13.0,76.791,1,256,87.92 +inception_next_base,12.99,76.866,1,384,86.67 +xcit_small_24_p8_224,12.99,76.888,1,224,47.63 +convformer_s36,12.84,77.75,1,384,40.01 +convnextv2_base,12.83,77.812,1,288,88.72 +efficientnetv2_rw_m,12.83,77.808,1,416,53.24 +legacy_senet154,12.83,77.84,1,224,115.09 +convnextv2_large,12.71,78.563,1,224,197.96 +convformer_b36,12.64,79.028,1,224,99.88 +resnetrs270,12.54,79.637,1,256,129.86 +senet154,12.54,79.615,1,224,115.09 +resnest200e,12.5,79.914,1,320,70.2 +repvgg_b3,12.24,81.603,1,224,123.09 +swinv2_small_window16_256,12.22,81.758,1,256,49.73 +dm_nfnet_f1,12.19,81.897,1,320,132.63 +vit_small_patch14_reg4_dinov2,12.18,81.993,1,518,22.06 +eca_nfnet_l2,12.14,82.304,1,384,56.72 +vit_small_patch14_dinov2,12.0,83.198,1,518,22.06 +xcit_large_24_p16_224,12.0,83.236,1,224,189.1 +efficientvit_l3,11.82,84.485,1,224,246.04 +caformer_s36,11.81,84.544,1,384,39.3 +tf_efficientnetv2_m,11.71,85.301,1,480,54.14 +convnext_base,11.68,85.493,1,320,88.59 +seresnet269d,11.66,85.618,1,256,113.67 +nfnet_f1,11.65,85.748,1,320,132.63 +convnext_large_mlp,11.64,85.831,1,256,200.13 +hrnet_w64,11.56,86.429,1,224,128.06 +convnext_base,11.52,86.708,1,384,88.59 +convmixer_1536_20,11.44,87.282,1,224,51.63 +sequencer2d_m,11.44,87.314,1,224,38.31 +cait_xxs36_384,11.3,88.39,1,384,17.37 +vit_base_patch16_clip_384,11.28,88.553,1,384,86.86 +deit_base_patch16_384,11.24,88.868,1,384,86.86 +deit3_base_patch16_384,11.21,89.062,1,384,86.88 +deit_base_distilled_patch16_384,11.2,89.193,1,384,87.63 +davit_large,11.12,89.816,1,224,196.81 +vit_base_patch16_siglip_384,11.11,89.91,1,384,93.18 +vit_base_patch16_384,11.1,89.979,1,384,86.86 +tresnet_xl,10.86,91.955,1,448,78.44 +densenet264d,10.85,92.074,1,224,72.74 +eca_nfnet_l3,10.82,92.336,1,352,72.04 +maxxvitv2_rmlp_base_rw_224,10.75,92.904,1,224,116.09 +pnasnet5large,10.75,92.913,1,331,86.06 +nasnetalarge,10.68,93.528,1,331,88.75 +efficientvit_l3,10.66,93.703,1,256,246.04 +swinv2_cr_tiny_384,10.62,94.018,1,384,28.33 +swinv2_cr_large_224,10.6,94.24,1,224,196.68 +ecaresnet269d,10.55,94.713,1,320,102.09 +swin_large_patch4_window7_224,10.5,95.17,1,224,196.53 +cait_xs24_384,10.48,95.27,1,384,26.67 +resnext101_32x16d,10.42,95.861,1,224,194.03 +volo_d4_224,10.3,96.948,1,224,192.96 +vit_large_r50_s32_224,10.24,97.519,1,224,328.99 +mixer_l16_224,10.19,98.039,1,224,208.2 +seresnet269d,10.18,98.097,1,288,113.67 +swinv2_large_window12_192,10.15,98.416,1,192,228.77 +tf_efficientnet_b6,10.13,98.587,1,528,43.04 +dm_nfnet_f2,10.06,99.26,1,256,193.78 +maxvit_tiny_tf_384,10.04,99.454,1,384,30.98 +nfnet_f2,10.03,99.603,1,256,193.78 +volo_d2_384,10.03,99.551,1,384,58.87 +convnextv2_base,10.01,99.781,1,384,88.72 +convnext_large,9.99,99.979,1,288,197.77 +xcit_small_12_p8_384,9.95,100.392,1,384,26.21 +xcit_medium_24_p16_384,9.8,101.966,1,384,84.4 +maxvit_base_tf_224,9.72,102.786,1,224,119.47 +ecaresnet269d,9.69,103.106,1,352,102.09 +vgg11,9.64,103.628,1,224,132.86 +vit_base_patch16_18x2_224,9.64,103.632,1,224,256.73 +vgg11_bn,9.5,105.194,1,224,132.87 +efficientnetv2_l,9.39,106.364,1,384,118.52 +tf_efficientnetv2_l,9.31,107.306,1,384,118.52 +swinv2_base_window12to16_192to256,9.3,107.471,1,256,87.92 +convformer_m36,9.27,107.717,1,384,57.05 +vgg13_bn,9.26,107.879,1,224,133.05 +convnextv2_large,9.22,108.292,1,288,197.96 +maxvit_rmlp_base_rw_224,9.22,108.408,1,224,116.14 +swinv2_base_window16_256,9.21,108.436,1,256,87.92 +efficientnet_b6,9.2,108.582,1,528,43.04 +resnetrs270,9.19,108.694,1,352,129.86 +vgg13,9.01,110.918,1,224,133.05 +caformer_m36,8.96,111.546,1,384,56.2 +resnetrs350,8.94,111.782,1,288,163.96 +coatnet_rmlp_3_rw_224,8.77,113.984,1,224,165.15 +coatnet_3_rw_224,8.74,114.329,1,224,181.81 +coatnet_3_224,8.71,114.748,1,224,166.97 +beit_base_patch16_384,8.65,115.469,1,384,86.74 +vgg16,8.61,116.0,1,224,138.36 +efficientvit_l3,8.58,116.47,1,320,246.04 +coat_lite_medium_384,8.54,116.926,1,384,44.57 +vgg19_bn,8.53,117.153,1,224,143.68 +vgg16_bn,8.52,117.29,1,224,138.37 +resnetv2_101x1_bit,8.46,118.13,1,448,44.54 +vgg19,8.39,119.033,1,224,143.67 +vit_base_patch8_224,8.39,119.029,1,224,86.58 +eca_nfnet_l3,7.98,125.225,1,448,72.04 +xcit_medium_24_p8_224,7.98,125.271,1,224,84.32 +maxxvitv2_rmlp_large_rw_224,7.72,129.399,1,224,215.42 +convnext_large_mlp,7.67,130.275,1,320,200.13 +nfnet_f2,7.66,130.475,1,352,193.78 +dm_nfnet_f2,7.6,131.419,1,352,193.78 +vit_large_patch32_384,7.57,131.935,1,384,306.63 +repvgg_d2se,7.54,132.439,1,320,133.33 +seresnextaa201d_32x8d,7.52,132.81,1,320,149.39 +vit_base_r50_s16_384,7.46,133.951,1,384,98.95 +sequencer2d_l,7.43,134.481,1,224,54.3 +maxvit_small_tf_384,7.23,138.115,1,384,69.02 +resmlp_big_24_224,7.21,138.531,1,224,129.14 +efficientvit_l3,7.1,140.815,1,384,246.04 +efficientnetv2_l,7.02,142.385,1,480,118.52 +cait_s24_384,6.98,143.242,1,384,47.06 +vit_large_patch16_224,6.96,143.598,1,224,304.33 +deit3_large_patch16_224,6.92,144.377,1,224,304.37 +mvitv2_large,6.92,144.482,1,224,217.99 +resnest269e,6.89,145.117,1,416,110.93 +volo_d5_224,6.88,145.294,1,224,295.46 +convnext_xlarge,6.87,145.419,1,224,350.2 +eva_large_patch14_196,6.87,145.357,1,196,304.14 +resnetrs420,6.84,146.047,1,320,191.89 +tf_efficientnetv2_l,6.79,147.238,1,480,118.52 +regnety_640,6.77,147.685,1,224,281.38 +efficientnetv2_xl,6.76,147.902,1,384,208.12 +tf_efficientnetv2_xl,6.73,148.547,1,384,208.12 +resnetrs350,6.65,150.31,1,384,163.96 +flexivit_large,6.63,150.711,1,240,304.36 +nfnet_f3,6.59,151.709,1,320,254.92 +tiny_vit_21m_512,6.55,152.682,1,512,21.27 +regnety_320,6.4,156.191,1,384,145.05 +dm_nfnet_f3,6.39,156.391,1,320,254.92 +vit_base_patch16_siglip_512,6.25,159.877,1,512,93.52 +beit_large_patch16_224,6.22,160.576,1,224,304.43 +beitv2_large_patch16_224,6.22,160.717,1,224,304.43 +maxvit_large_tf_224,6.2,161.197,1,224,211.79 +swinv2_large_window12to16_192to256,6.12,163.37,1,256,196.74 +convnext_large,6.04,165.574,1,384,197.77 +convnext_large_mlp,6.01,166.316,1,384,200.13 +vit_so400m_patch14_siglip_224,5.97,167.337,1,224,427.68 +tf_efficientnet_b7,5.96,167.661,1,600,66.35 +swinv2_cr_small_384,5.93,168.406,1,384,49.7 +vit_large_patch16_siglip_256,5.86,170.441,1,256,315.96 +vit_large_patch14_224,5.84,171.067,1,224,304.2 +vit_large_patch14_clip_quickgelu_224,5.84,171.235,1,224,303.97 +vit_large_patch14_clip_224,5.82,171.768,1,224,304.2 +coatnet_rmlp_2_rw_384,5.75,173.867,1,384,73.88 +mvitv2_large_cls,5.73,174.501,1,224,234.58 +convformer_b36,5.71,174.868,1,384,99.88 +seresnextaa201d_32x8d,5.71,174.871,1,384,149.39 +eva02_base_patch14_448,5.68,175.869,1,448,87.12 +davit_huge,5.66,176.53,1,224,348.92 +vit_large_patch14_xp_224,5.58,179.001,1,224,304.06 +maxvit_tiny_tf_512,5.54,180.385,1,512,31.05 +efficientnet_b7,5.48,182.298,1,600,66.35 +xcit_large_24_p16_384,5.46,183.021,1,384,189.1 +coatnet_4_224,5.41,184.721,1,224,275.43 +volo_d3_448,5.38,185.659,1,448,86.63 +caformer_b36,5.37,186.081,1,384,98.75 +convnextv2_large,5.36,186.287,1,384,197.96 +eva02_large_patch14_clip_224,5.35,186.928,1,224,304.11 +eva02_large_patch14_224,5.34,187.155,1,224,303.27 +convnext_xlarge,5.27,189.818,1,288,350.2 +xcit_small_24_p8_384,5.25,190.508,1,384,47.63 +focalnet_large_fl3,5.2,192.302,1,384,239.13 +resnetrs420,5.16,193.791,1,416,191.89 +resnetv2_152x2_bit,5.06,197.516,1,224,236.34 +vit_large_r50_s32_384,4.97,201.254,1,384,329.09 +focalnet_large_fl4,4.95,201.96,1,384,239.32 +nfnet_f3,4.77,209.555,1,416,254.92 +cait_s36_384,4.73,211.462,1,384,68.37 +dm_nfnet_f3,4.73,211.21,1,416,254.92 +vit_base_patch14_dinov2,4.67,214.237,1,518,86.58 +swinv2_cr_base_384,4.62,216.189,1,384,87.88 +vit_base_patch14_reg4_dinov2,4.61,216.991,1,518,86.58 +efficientnetv2_xl,4.49,222.467,1,512,208.12 +tf_efficientnetv2_xl,4.48,223.158,1,512,208.12 +resnetv2_50x3_bit,4.45,224.393,1,224,217.32 +xcit_large_24_p8_224,4.41,226.888,1,224,188.93 +dm_nfnet_f4,4.4,227.093,1,384,316.07 +nfnet_f4,4.32,231.615,1,384,316.07 +maxxvitv2_rmlp_base_rw_384,4.28,233.473,1,384,116.09 +swin_base_patch4_window12_384,4.25,235.251,1,384,87.9 +maxvit_base_tf_384,4.01,249.254,1,384,119.65 +maxvit_rmlp_base_rw_384,3.97,252.089,1,384,116.14 +maxvit_xlarge_tf_224,3.84,260.169,1,224,506.99 +swinv2_cr_huge_224,3.73,267.645,1,224,657.83 +resnext101_32x32d,3.7,269.902,1,224,468.53 +convnextv2_huge,3.65,274.084,1,224,660.29 +regnety_640,3.47,288.16,1,384,281.38 +efficientnet_b8,3.46,289.292,1,672,87.41 +nfnet_f5,3.32,300.922,1,416,377.21 +maxvit_small_tf_512,3.3,303.006,1,512,69.13 +tf_efficientnet_b8,3.3,302.574,1,672,87.41 +xcit_medium_24_p8_384,3.3,303.237,1,384,84.32 +convnext_xlarge,3.28,305.125,1,384,350.2 +dm_nfnet_f5,3.28,304.623,1,416,377.21 +vit_large_patch16_siglip_384,3.09,323.863,1,384,316.28 +volo_d4_448,3.08,324.261,1,448,193.41 +vit_large_patch14_clip_quickgelu_336,3.06,326.699,1,336,304.29 +deit3_large_patch16_384,3.05,327.468,1,384,304.76 +eva_large_patch14_336,3.03,330.35,1,336,304.53 +swin_large_patch4_window12_384,3.02,330.733,1,384,196.74 +vit_large_patch14_clip_336,3.02,330.571,1,336,304.53 +vit_large_patch16_384,3.02,330.83,1,384,304.72 +swinv2_cr_large_384,3.0,333.347,1,384,196.68 +resnetv2_152x2_bit,2.88,346.891,1,384,236.34 +vit_huge_patch14_gap_224,2.88,347.103,1,224,630.76 +resnetv2_101x3_bit,2.86,349.835,1,224,387.93 +nfnet_f4,2.83,353.869,1,512,316.07 +convnextv2_huge,2.8,357.503,1,288,660.29 +eva02_large_patch14_clip_336,2.8,357.279,1,336,304.43 +vit_huge_patch14_224,2.8,356.629,1,224,630.76 +vit_huge_patch14_clip_224,2.8,356.916,1,224,632.05 +deit3_huge_patch14_224,2.79,358.708,1,224,632.13 +vit_huge_patch14_xp_224,2.79,358.77,1,224,631.8 +vit_huge_patch14_clip_quickgelu_224,2.78,360.058,1,224,632.08 +swinv2_base_window12to24_192to384,2.77,361.199,1,384,87.92 +mvitv2_huge_cls,2.76,362.599,1,224,694.8 +dm_nfnet_f4,2.75,363.412,1,512,316.07 +focalnet_huge_fl3,2.74,365.417,1,224,745.28 +focalnet_xlarge_fl3,2.68,372.855,1,384,408.79 +maxvit_large_tf_384,2.66,375.595,1,384,212.03 +regnety_1280,2.66,376.44,1,224,644.81 +focalnet_huge_fl4,2.65,376.997,1,224,686.46 +focalnet_xlarge_fl4,2.63,379.808,1,384,409.03 +nfnet_f6,2.53,394.377,1,448,438.36 +vit_giant_patch16_gap_224,2.52,397.009,1,224,1011.37 +dm_nfnet_f6,2.5,399.61,1,448,438.36 +convnext_xxlarge,2.49,402.224,1,256,846.47 +coatnet_5_224,2.46,406.549,1,224,687.47 +beit_large_patch16_384,2.43,411.343,1,384,305.0 +resnetv2_152x2_bit,2.36,422.825,1,448,236.34 +resnetv2_50x3_bit,2.34,426.363,1,448,217.32 +dm_nfnet_f5,2.14,466.965,1,544,377.21 +eva_giant_patch14_224,2.12,471.488,1,224,1012.56 +nfnet_f5,2.11,474.766,1,544,377.21 +eva_giant_patch14_clip_224,2.1,476.74,1,224,1012.59 +vit_giant_patch14_224,2.1,476.373,1,224,1012.61 +vit_giant_patch14_clip_224,2.07,482.288,1,224,1012.65 +nfnet_f7,2.01,496.295,1,480,499.5 +vit_so400m_patch14_siglip_384,2.01,496.268,1,384,428.23 +volo_d5_448,1.97,507.454,1,448,295.91 +maxvit_base_tf_512,1.88,531.931,1,512,119.88 +davit_giant,1.86,536.86,1,224,1406.47 +convnextv2_huge,1.75,572.127,1,384,660.29 +eva02_large_patch14_448,1.71,586.058,1,448,305.08 +tf_efficientnet_l2,1.71,584.697,1,475,480.31 +cait_m36_384,1.7,588.838,1,384,271.22 +dm_nfnet_f6,1.7,587.555,1,576,438.36 +nfnet_f6,1.7,589.297,1,576,438.36 +swinv2_large_window12to24_192to384,1.7,587.644,1,384,196.74 +xcit_large_24_p8_384,1.68,596.178,1,384,188.93 +vit_huge_patch14_clip_336,1.64,609.779,1,336,632.46 +regnety_1280,1.59,628.931,1,384,644.81 +maxvit_xlarge_tf_384,1.53,652.708,1,384,475.32 +vit_large_patch14_dinov2,1.5,664.399,1,518,304.37 +vit_large_patch14_reg4_dinov2,1.49,673.215,1,518,304.37 +volo_d5_512,1.47,680.639,1,512,296.09 +resnetv2_101x3_bit,1.45,690.923,1,448,387.93 +vit_huge_patch14_clip_378,1.34,746.759,1,378,632.68 +vit_huge_patch14_clip_quickgelu_378,1.33,751.148,1,378,632.68 +nfnet_f7,1.32,756.231,1,608,499.5 +maxvit_large_tf_512,1.28,779.969,1,512,212.33 +swinv2_cr_huge_384,1.26,792.156,1,384,657.94 +vit_huge_patch16_gap_448,1.24,806.627,1,448,631.67 +swinv2_cr_giant_224,1.22,817.72,1,224,2598.76 +vit_gigantic_patch14_224,1.2,831.652,1,224,1844.44 +vit_gigantic_patch14_clip_224,1.2,830.217,1,224,1844.91 +beit_large_patch16_512,1.15,869.779,1,512,305.67 +convnextv2_huge,1.11,904.605,1,512,660.29 +resnetv2_152x4_bit,1.07,931.333,1,224,936.53 +eva_giant_patch14_336,1.06,945.007,1,336,1013.01 +maxvit_xlarge_tf_512,0.74,1353.032,1,512,475.77 +regnety_2560,0.71,1404.762,1,384,1282.6 +cait_m48_448,0.64,1574.651,1,448,356.46 +samvit_base_patch16,0.61,1639.865,1,1024,89.67 +tf_efficientnet_l2,0.6,1664.515,1,800,480.31 +efficientnet_l2,0.58,1732.697,1,800,480.31 +resnetv2_152x4_bit,0.54,1835.495,1,480,936.53 +eva_giant_patch14_560,0.46,2166.883,1,560,1014.45 +vit_giant_patch14_dinov2,0.46,2154.504,1,518,1136.48 +vit_giant_patch14_reg4_dinov2,0.46,2171.619,1,518,1136.48 +eva02_enormous_patch14_clip_224,0.44,2252.224,1,224,4350.56 +swinv2_cr_giant_384,0.37,2697.442,1,384,2598.76 +samvit_large_patch16,0.28,3628.054,1,1024,308.28 +samvit_huge_patch16,0.19,5153.132,1,1024,637.03 diff --git a/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt240-cpu-i7_12700h-dynamo.csv b/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt240-cpu-i7_12700h-dynamo.csv new file mode 100644 index 0000000000000000000000000000000000000000..f8f52c7bcb0a29b91110ee5ec8de2c6282ce4147 --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt240-cpu-i7_12700h-dynamo.csv @@ -0,0 +1,1441 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_byobnet,160,930.48,1.062,1,0.46,0.03,0.43 +test_vit,160,752.11,1.315,1,0.37,0.04,0.48 +test_efficientnet,160,647.07,1.53,1,0.36,0.06,0.55 +mobilenetv3_small_075,224,393.5,2.524,1,2.04,0.05,1.3 +mobilenetv3_small_050,224,384.04,2.586,1,1.59,0.03,0.92 +tf_mobilenetv3_small_minimal_100,224,379.32,2.616,1,2.04,0.06,1.41 +lcnet_035,224,378.45,2.618,1,1.64,0.03,1.04 +tinynet_e,106,376.63,2.636,1,2.04,0.03,0.69 +mobilenetv3_small_100,224,374.07,2.654,1,2.54,0.06,1.42 +lcnet_075,224,373.7,2.656,1,2.36,0.1,1.99 +lcnet_050,224,364.66,2.725,1,1.88,0.05,1.26 +mobilenetv2_035,224,323.01,3.076,1,1.68,0.07,2.86 +tf_mobilenetv3_small_100,224,322.73,3.079,1,2.54,0.06,1.42 +tf_mobilenetv3_small_075,224,298.55,3.327,1,2.04,0.05,1.3 +lcnet_100,224,291.87,3.406,1,2.95,0.16,2.52 +mnasnet_small,224,265.95,3.739,1,2.03,0.07,2.16 +mnasnet_050,224,252.54,3.939,1,2.22,0.11,3.07 +mobilenetv4_conv_small,224,248.97,3.996,1,3.77,0.19,1.97 +regnetx_002,224,237.37,4.191,1,2.68,0.2,2.16 +tinynet_d,152,232.83,4.272,1,2.34,0.05,1.42 +semnasnet_050,224,224.05,4.443,1,2.08,0.11,3.44 +mobilenetv2_050,224,221.33,4.499,1,1.97,0.1,3.64 +efficientvit_b0,224,210.09,4.735,1,3.41,0.1,2.87 +regnety_002,224,209.64,4.749,1,3.16,0.2,2.17 +mobilenetv4_conv_small,256,199.91,4.979,1,3.77,0.25,2.57 +mobilenetv2_075,224,196.78,5.061,1,2.64,0.22,5.86 +lcnet_150,224,194.96,5.108,1,4.5,0.34,3.79 +ghostnet_050,224,185.37,5.373,1,2.59,0.05,1.77 +efficientvit_m0,224,184.92,5.39,1,2.35,0.08,0.91 +repghostnet_050,224,182.79,5.45,1,2.31,0.05,2.02 +repghostnet_058,224,180.93,5.504,1,2.55,0.07,2.59 +tinynet_c,184,177.3,5.621,1,2.46,0.11,2.87 +levit_128s,224,173.25,5.748,1,7.78,0.31,1.88 +mobilenetv3_large_075,224,172.84,5.763,1,3.99,0.16,4.0 +mnasnet_075,224,169.28,5.884,1,3.17,0.23,4.77 +hardcorenas_a,224,159.47,6.247,1,5.26,0.23,4.38 +resnet10t,176,158.58,6.28,1,5.44,0.7,1.51 +efficientvit_m2,224,156.16,6.382,1,4.19,0.2,1.47 +mobilenetv1_100,224,155.99,6.388,1,4.23,0.58,5.04 +hgnetv2_b0,224,154.02,6.468,1,6.0,0.33,2.12 +semnasnet_075,224,153.91,6.473,1,2.91,0.23,5.54 +ese_vovnet19b_slim_dw,224,153.26,6.504,1,1.9,0.4,5.28 +levit_conv_128s,224,151.31,6.583,1,7.78,0.31,1.88 +mobilenetv2_100,224,151.16,6.591,1,3.5,0.31,6.68 +repghostnet_080,224,150.68,6.615,1,3.28,0.1,3.22 +efficientvit_m1,224,149.13,6.685,1,2.98,0.17,1.33 +mobilenetv3_rw,224,148.89,6.692,1,5.48,0.23,4.41 +semnasnet_100,224,147.79,6.744,1,3.89,0.32,6.23 +mnasnet_100,224,147.07,6.774,1,4.38,0.33,5.46 +edgenext_xx_small,288,145.77,6.838,1,1.33,0.33,4.21 +edgenext_xx_small,256,142.76,6.983,1,1.33,0.26,3.33 +mobilenetv3_large_100,256,141.04,7.067,1,5.48,0.29,5.75 +hardcorenas_b,224,139.46,7.146,1,5.18,0.26,5.09 +mobilenetv1_100h,224,137.02,7.269,1,5.28,0.63,5.09 +repghostnet_100,224,136.7,7.291,1,4.07,0.15,3.98 +tf_mobilenetv3_large_minimal_100,224,135.13,7.374,1,3.92,0.22,4.4 +mobilenetv3_large_100,224,135.11,7.377,1,5.48,0.23,4.41 +tf_mobilenetv3_large_075,224,133.04,7.493,1,3.99,0.16,4.0 +levit_128,224,128.82,7.736,1,9.21,0.41,2.71 +tinynet_b,188,128.04,7.786,1,3.73,0.21,4.44 +regnetx_004,224,128.0,7.786,1,5.16,0.4,3.14 +hardcorenas_c,224,126.13,7.902,1,5.52,0.28,5.01 +efficientnet_lite0,224,124.77,7.99,1,4.65,0.4,6.74 +regnetx_006,224,124.69,7.995,1,6.2,0.61,3.98 +regnetx_004_tv,224,123.65,8.061,1,5.5,0.42,3.17 +spnasnet_100,224,121.97,8.171,1,4.42,0.35,6.03 +mobilenet_edgetpu_v2_xs,224,119.74,8.329,1,4.46,0.7,4.8 +tf_mobilenetv3_large_100,224,118.57,8.408,1,5.48,0.23,4.41 +hardcorenas_d,224,117.16,8.511,1,7.5,0.3,4.93 +mobilenetv1_125,224,116.05,8.591,1,6.27,0.89,6.3 +fbnetc_100,224,115.63,8.626,1,5.57,0.4,6.51 +hardcorenas_f,224,115.25,8.654,1,8.2,0.35,5.57 +mobilenetv2_110d,224,114.18,8.735,1,4.52,0.45,8.71 +levit_conv_128,224,113.39,8.793,1,9.21,0.41,2.71 +dla46_c,224,113.23,8.806,1,1.3,0.58,4.5 +regnety_004,224,111.71,8.931,1,4.34,0.41,3.89 +vit_tiny_r_s16_p8_224,224,110.52,9.023,1,6.34,0.44,2.06 +mobilevitv2_050,256,110.49,9.03,1,1.37,0.48,8.04 +mobilenetv2_140,224,109.44,9.115,1,6.11,0.6,9.57 +repghostnet_111,224,109.01,9.149,1,4.54,0.18,4.38 +mobilenetv1_100,256,108.9,9.161,1,4.23,0.76,6.59 +dla46x_c,224,108.69,9.178,1,1.07,0.54,5.66 +efficientvit_m4,224,108.18,9.222,1,8.8,0.3,1.7 +hardcorenas_e,224,108.15,9.221,1,8.07,0.35,5.65 +convnext_atto,224,107.95,9.243,1,3.7,0.55,3.81 +cs3darknet_s,256,107.58,9.272,1,3.28,0.72,2.97 +efficientvit_m3,224,107.18,9.305,1,6.9,0.27,1.62 +edgenext_x_small,256,107.01,9.325,1,2.34,0.54,5.93 +regnety_006,224,105.83,9.425,1,6.06,0.61,4.33 +gernet_s,224,104.32,9.558,1,8.17,0.75,2.65 +mnasnet_140,224,103.97,9.594,1,7.12,0.6,7.71 +resnet18,160,103.24,9.66,1,11.69,0.93,1.27 +mobilenetv1_100h,256,103.01,9.684,1,5.28,0.82,6.65 +tf_efficientnetv2_b0,192,102.23,9.758,1,7.14,0.54,3.51 +hgnetv2_b1,224,101.5,9.823,1,6.34,0.49,2.73 +ghostnet_100,224,101.36,9.84,1,5.18,0.15,3.55 +resnet10t,224,101.08,9.869,1,5.44,1.1,2.43 +levit_conv_192,224,100.88,9.888,1,10.95,0.66,3.2 +levit_192,224,100.87,9.887,1,10.95,0.66,3.2 +ghostnet_130,224,100.75,9.898,1,7.36,0.24,4.6 +regnetx_008,224,100.17,9.958,1,7.26,0.81,5.15 +cs3darknet_focus_s,256,99.25,10.051,1,3.27,0.69,2.7 +tinynet_a,192,99.06,10.071,1,6.19,0.35,5.41 +repghostnet_130,224,99.05,10.068,1,5.48,0.25,5.24 +pit_ti_distilled_224,224,98.66,10.111,1,5.1,0.71,6.23 +rexnetr_100,224,98.26,10.151,1,4.88,0.43,7.72 +tf_efficientnet_lite0,224,97.3,10.249,1,4.65,0.4,6.74 +pit_ti_224,224,97.19,10.264,1,4.85,0.7,6.19 +regnety_008,224,97.13,10.269,1,6.26,0.81,5.25 +convnext_atto_ols,224,96.79,10.31,1,3.7,0.58,4.11 +hgnetv2_b0,288,96.75,10.308,1,6.0,0.54,3.51 +mobilenet_edgetpu_100,224,95.97,10.397,1,4.09,1.0,5.75 +regnety_008_tv,224,94.42,10.567,1,6.43,0.84,5.42 +repghostnet_150,224,93.96,10.617,1,6.58,0.32,6.0 +efficientnet_lite1,240,93.82,10.637,1,5.42,0.62,10.14 +efficientnet_b0,224,93.58,10.662,1,5.29,0.4,6.75 +efficientvit_b1,224,91.69,10.879,1,9.1,0.53,7.25 +semnasnet_140,224,91.48,10.907,1,6.11,0.6,8.87 +rexnet_100,224,91.29,10.929,1,4.8,0.41,7.44 +mobilevit_xxs,256,90.65,11.011,1,1.27,0.42,8.34 +edgenext_x_small,288,90.43,11.036,1,2.34,0.68,7.5 +convnext_femto_ols,224,89.2,11.187,1,5.23,0.82,4.87 +mobilenetv1_125,256,88.85,11.231,1,6.27,1.16,8.23 +mixer_s32_224,224,88.28,11.299,1,19.1,1.0,2.28 +xcit_nano_12_p16_224,224,87.78,11.371,1,3.05,0.56,4.17 +ese_vovnet19b_slim,224,87.17,11.445,1,3.17,1.69,3.52 +efficientformerv2_s0,224,86.81,11.497,1,3.6,0.41,5.3 +convnextv2_atto,224,86.78,11.501,1,3.71,0.55,3.81 +mobilenet_edgetpu_v2_s,224,85.54,11.666,1,5.99,1.21,6.6 +mobilenetv4_conv_medium,224,84.5,11.808,1,9.72,0.84,5.8 +dla60x_c,224,84.24,11.846,1,1.32,0.59,6.01 +tf_efficientnetv2_b1,192,83.74,11.917,1,8.14,0.76,4.59 +efficientnet_b0,256,82.19,12.142,1,5.29,0.52,8.81 +resnet14t,176,81.88,12.186,1,10.08,1.07,3.61 +hgnetv2_b1,288,81.46,12.251,1,6.34,0.82,4.51 +mobileone_s1,224,81.13,12.302,1,4.83,0.86,9.67 +efficientvit_b1,256,80.92,12.336,1,9.1,0.69,9.46 +mobilenetv2_120d,224,80.63,12.378,1,5.83,0.69,11.97 +ese_vovnet19b_dw,224,80.55,12.39,1,6.54,1.34,8.25 +fbnetv3_b,224,80.54,12.391,1,8.6,0.42,6.97 +repghostnet_200,224,80.43,12.406,1,9.8,0.54,7.96 +deit_tiny_distilled_patch16_224,224,80.18,12.448,1,5.91,1.27,6.01 +mobilenetv4_hybrid_medium_075,224,80.03,12.47,1,7.31,0.66,5.65 +tf_efficientnetv2_b0,224,79.79,12.509,1,7.14,0.73,4.77 +nf_regnet_b0,192,79.28,12.59,1,8.76,0.37,3.15 +levit_256,224,77.98,12.799,1,18.89,1.13,4.23 +ghostnetv2_100,224,77.73,12.839,1,6.16,0.18,4.55 +vit_small_patch32_224,224,77.57,12.865,1,22.88,1.15,2.5 +vit_tiny_patch16_224,224,77.57,12.868,1,5.72,1.26,5.97 +deit_tiny_patch16_224,224,77.2,12.931,1,5.72,1.26,5.97 +efficientnet_b1_pruned,240,77.19,12.932,1,6.33,0.4,6.21 +tf_efficientnet_b0,224,77.17,12.933,1,5.29,0.4,6.75 +convnext_femto,224,77.08,12.948,1,5.22,0.79,4.57 +fbnetv3_b,256,77.03,12.958,1,8.6,0.55,9.1 +fbnetv3_d,224,76.95,12.97,1,10.31,0.52,8.5 +rexnetr_130,224,76.89,12.979,1,7.61,0.68,9.81 +convnext_atto_ols,288,75.39,13.242,1,3.7,0.96,6.8 +tf_efficientnet_lite1,240,74.97,13.315,1,5.42,0.62,10.14 +efficientnet_cc_b0_4e,224,74.51,13.396,1,13.31,0.41,9.42 +efficientvit_b1,288,73.91,13.508,1,9.1,0.87,11.96 +efficientnet_lite2,260,73.69,13.546,1,6.09,0.89,12.9 +efficientvit_m5,224,72.44,13.781,1,12.47,0.53,2.41 +efficientnet_b0_gn,224,72.25,13.817,1,5.29,0.42,6.75 +hgnetv2_b2,224,72.08,13.849,1,11.22,1.15,4.12 +mobilenetv4_conv_medium,256,71.94,13.876,1,9.72,1.1,7.58 +convnextv2_femto,224,71.93,13.879,1,5.23,0.79,4.57 +efficientnet_b0_g16_evos,224,71.28,14.004,1,8.11,1.01,7.42 +rexnet_130,224,71.2,14.022,1,7.56,0.68,9.71 +gmlp_ti16_224,224,70.31,14.2,1,5.87,1.34,7.55 +efficientnet_blur_b0,224,68.95,14.477,1,5.29,0.43,8.72 +fbnetv3_d,256,68.8,14.512,1,10.31,0.68,11.1 +mobilevitv2_075,256,68.69,14.534,1,2.87,1.05,12.06 +tf_mixnet_s,224,68.57,14.56,1,4.13,0.25,6.25 +pit_xs_224,224,68.23,14.63,1,10.62,1.4,7.71 +levit_conv_256,224,67.71,14.745,1,18.89,1.13,4.23 +edgenext_small,256,67.66,14.754,1,5.59,1.26,9.07 +ghostnetv2_130,224,67.59,14.768,1,8.96,0.28,5.9 +visformer_tiny,224,67.48,14.795,1,10.32,1.27,5.72 +rexnetr_150,224,67.41,14.81,1,9.78,0.89,11.13 +repvit_m1,224,67.3,14.835,1,5.49,0.83,7.45 +pit_xs_distilled_224,224,66.69,14.963,1,11.0,1.41,7.76 +efficientnet_es_pruned,224,66.45,15.026,1,5.44,1.81,8.73 +tf_efficientnet_es,224,66.43,15.029,1,5.44,1.81,8.73 +efficientnet_cc_b0_8e,224,66.29,15.061,1,24.01,0.42,9.42 +convnext_atto,288,66.24,15.073,1,3.7,0.91,6.3 +efficientnet_b0_g8_gn,224,66.06,15.111,1,6.56,0.66,6.75 +efficientnet_es,224,65.44,15.255,1,5.44,1.81,8.73 +mobilenet_edgetpu_v2_m,224,65.39,15.267,1,8.46,1.85,8.15 +vit_tiny_r_s16_p8_384,384,65.32,15.284,1,6.36,1.34,6.49 +repvit_m0_9,224,64.74,15.423,1,5.49,0.83,7.45 +tf_efficientnet_cc_b0_4e,224,64.52,15.451,1,13.31,0.41,9.42 +regnetz_005,224,64.47,15.487,1,7.12,0.52,5.86 +fastvit_t8,256,64.37,15.515,1,4.03,0.7,8.63 +seresnet18,224,64.3,15.526,1,11.78,1.82,2.49 +rexnet_150,224,64.23,15.544,1,9.73,0.9,11.21 +tf_efficientnetv2_b2,208,64.06,15.584,1,10.1,1.06,6.0 +mobilenetv3_large_150d,256,63.96,15.61,1,14.62,1.03,12.35 +pvt_v2_b0,224,63.86,15.634,1,3.67,0.57,7.99 +regnetx_016,224,63.8,15.648,1,9.19,1.62,7.93 +efficientnet_b1,224,63.79,15.651,1,7.79,0.59,9.36 +mixnet_s,224,63.75,15.663,1,4.13,0.25,6.25 +efficientformerv2_s1,224,63.46,15.736,1,6.19,0.67,7.66 +mobileone_s2,224,63.39,15.751,1,7.88,1.34,11.55 +mobilenetv4_hybrid_medium,224,62.29,16.028,1,11.07,0.98,6.84 +resnet18,224,62.22,16.046,1,11.69,1.82,2.48 +resnet14t,224,62.13,16.072,1,10.08,1.69,5.8 +vit_xsmall_patch16_clip_224,224,62.07,16.087,1,8.28,1.79,6.65 +repvgg_a0,224,62.04,16.092,1,9.11,1.52,3.59 +tf_efficientnet_cc_b0_8e,224,61.37,16.269,1,24.01,0.42,9.42 +nf_regnet_b0,256,61.3,16.286,1,8.76,0.64,5.58 +efficientformer_l1,224,61.29,16.291,1,12.29,1.3,5.53 +repvit_m1_0,224,60.89,16.399,1,7.3,1.13,8.69 +legacy_seresnet18,224,60.78,16.425,1,11.78,1.82,2.49 +convnextv2_atto,288,60.59,16.476,1,3.71,0.91,6.3 +poolformerv2_s12,224,60.47,16.512,1,11.89,1.83,5.53 +tf_efficientnet_lite2,260,59.99,16.647,1,6.09,0.89,12.9 +resnet34,160,59.58,16.758,1,21.8,1.87,1.91 +efficientnet_b1,240,59.08,16.902,1,7.79,0.71,10.88 +eva02_tiny_patch14_224,224,58.77,16.992,1,5.5,1.7,9.14 +xcit_tiny_12_p16_224,224,58.69,17.015,1,6.72,1.24,6.29 +convnext_pico,224,58.47,17.082,1,9.05,1.37,6.1 +convnext_femto_ols,288,58.37,17.107,1,5.23,1.35,8.06 +skresnet18,224,57.26,17.438,1,11.96,1.82,3.24 +resnetblur18,224,56.99,17.521,1,11.69,2.34,3.39 +repvit_m2,224,56.93,17.54,1,8.8,1.36,9.43 +convnext_femto,288,56.87,17.561,1,5.22,1.3,7.56 +edgenext_small_rw,256,56.39,17.709,1,7.83,1.58,9.51 +mobilenetv4_conv_blur_medium,224,56.36,17.718,1,9.72,1.22,8.58 +levit_256d,224,56.23,17.762,1,26.21,1.4,4.93 +repvit_m1_1,224,56.14,17.789,1,8.8,1.36,9.43 +efficientnet_b2_pruned,260,56.09,17.802,1,8.31,0.73,9.13 +resnet18d,224,55.94,17.85,1,11.71,2.06,3.29 +mobilenetv4_conv_medium,320,55.63,17.951,1,9.72,1.71,11.84 +ese_vovnet19b_dw,288,55.61,17.955,1,6.54,2.22,13.63 +tf_efficientnetv2_b1,240,55.54,17.979,1,8.14,1.21,7.34 +mobilenetv4_hybrid_medium,256,55.47,18.002,1,11.07,1.29,9.01 +crossvit_tiny_240,240,55.35,18.041,1,7.01,1.57,9.08 +hgnetv2_b2,288,55.22,18.084,1,11.22,1.89,6.8 +poolformer_s12,224,55.05,18.138,1,11.92,1.82,5.53 +mobilenetv4_conv_aa_medium,256,54.57,18.299,1,9.72,1.58,10.3 +convnext_pico_ols,224,54.47,18.335,1,9.06,1.43,6.5 +tf_efficientnet_b1,240,54.39,18.361,1,7.79,0.71,10.88 +efficientnet_b1,256,53.72,18.59,1,7.79,0.77,12.22 +mobilevit_xs,256,53.72,18.59,1,2.32,1.05,16.33 +xcit_nano_12_p16_384,384,53.61,18.629,1,3.05,1.64,12.15 +ghostnetv2_160,224,53.14,18.791,1,12.39,0.42,7.23 +resnet26,224,52.94,18.865,1,16.0,2.36,7.35 +hgnetv2_b3,224,52.84,18.9,1,16.29,1.78,5.07 +crossvit_9_240,240,52.78,18.922,1,8.55,1.85,9.52 +levit_conv_256d,224,52.46,19.035,1,26.21,1.4,4.93 +cs3darknet_m,256,51.91,19.236,1,9.31,2.08,5.28 +cs3darknet_focus_m,256,51.44,19.417,1,9.3,1.98,4.89 +mixnet_m,224,51.43,19.421,1,5.01,0.36,8.19 +regnetz_b16,224,51.32,19.464,1,9.72,1.45,9.95 +crossvit_9_dagger_240,240,51.29,19.474,1,8.78,1.99,9.97 +dpn48b,224,51.25,19.489,1,9.13,1.69,8.92 +mobilenet_edgetpu_v2_m,256,51.21,19.504,1,8.46,2.42,10.65 +regnety_016,224,51.17,19.52,1,11.2,1.63,8.04 +convnextv2_pico,224,50.82,19.657,1,9.07,1.37,6.1 +efficientnet_b2,256,50.61,19.735,1,9.11,0.89,12.81 +mobilenetv3_large_150d,320,50.08,19.941,1,14.62,1.61,19.29 +nf_regnet_b1,256,49.7,20.096,1,10.22,0.82,7.27 +legacy_seresnext26_32x4d,224,49.5,20.177,1,16.79,2.49,9.39 +mobilenetv4_conv_blur_medium,256,49.26,20.275,1,9.72,1.59,11.2 +mobileone_s3,224,49.26,20.277,1,10.17,1.94,13.85 +mobileone_s0,224,49.2,20.304,1,5.29,1.09,15.48 +tf_mixnet_m,224,49.19,20.305,1,5.01,0.36,8.19 +vit_medium_patch32_clip_224,224,49.16,20.313,1,39.69,2.0,3.34 +convnextv2_femto,288,48.87,20.44,1,5.23,1.3,7.56 +mobilevitv2_100,256,48.56,20.567,1,4.9,1.84,16.08 +repvgg_a1,224,48.34,20.655,1,14.09,2.64,4.74 +gmixer_12_224,224,48.05,20.786,1,12.7,2.67,7.26 +regnetz_005,288,47.79,20.902,1,7.12,0.86,9.68 +edgenext_small,320,47.75,20.917,1,5.59,1.97,14.16 +efficientnet_lite3,300,47.75,20.918,1,8.2,1.65,21.85 +efficientnet_b1,288,47.52,21.022,1,7.79,0.97,15.46 +tiny_vit_5m_224,224,47.52,21.012,1,12.08,1.28,11.25 +regnetz_b16_evos,224,47.13,21.193,1,9.74,1.43,9.95 +ecaresnext50t_32x4d,224,46.88,21.304,1,15.41,2.7,10.09 +resnext50_32x4d,160,46.79,21.347,1,25.03,2.17,7.35 +coat_lite_tiny,224,46.76,21.36,1,5.72,1.6,11.65 +mobilenet_edgetpu_v2_l,224,46.43,21.512,1,10.92,2.55,9.05 +tf_efficientnetv2_b2,260,46.31,21.567,1,10.1,1.72,9.84 +cs3darknet_focus_m,288,46.19,21.624,1,9.3,2.51,6.19 +fbnetv3_g,240,46.17,21.632,1,16.62,1.28,14.87 +xcit_nano_12_p8_224,224,45.93,21.749,1,3.05,2.16,15.71 +ecaresnext26t_32x4d,224,45.83,21.795,1,15.41,2.7,10.09 +efficientnet_b3_pruned,300,45.83,21.793,1,9.86,1.04,11.86 +seresnext26t_32x4d,224,45.82,21.799,1,16.81,2.7,10.09 +resnest14d,224,45.77,21.821,1,10.61,2.76,7.33 +dla34,224,45.69,21.857,1,15.74,3.07,5.02 +rexnet_200,224,45.02,22.186,1,16.37,1.56,14.91 +resnet18,288,44.98,22.209,1,11.69,3.01,4.11 +seresnext26d_32x4d,224,44.67,22.357,1,16.81,2.73,10.19 +eca_resnext26ts,256,44.62,22.384,1,10.3,2.43,10.52 +seresnext26ts,256,44.62,22.387,1,10.39,2.43,10.52 +nf_regnet_b1,288,44.25,22.573,1,10.22,1.02,9.2 +cs3darknet_m,288,44.05,22.676,1,9.31,2.63,6.69 +resnext26ts,256,43.89,22.755,1,10.3,2.43,10.52 +hrnet_w18_small,224,43.65,22.885,1,13.19,1.61,5.72 +resnet50,160,43.63,22.892,1,25.56,2.1,5.67 +gernet_m,224,43.54,22.945,1,21.14,3.02,5.24 +resmlp_12_224,224,43.38,23.027,1,15.35,3.01,5.5 +mobilenetv4_hybrid_medium,320,43.17,23.141,1,11.07,2.05,14.36 +efficientvit_b2,224,43.16,23.139,1,24.33,1.6,14.62 +resnext50_32x4d,176,42.92,23.271,1,25.03,2.71,8.97 +efficientnet_b2,288,42.86,23.307,1,9.11,1.12,16.2 +lambda_resnet26rpt_256,256,42.84,23.317,1,10.99,3.16,11.87 +lambda_resnet26t,256,42.48,23.514,1,10.96,3.02,11.87 +gcresnext26ts,256,42.38,23.57,1,10.48,2.43,10.53 +levit_384,224,42.25,23.643,1,39.13,2.36,6.26 +rexnetr_200,224,42.22,23.653,1,16.52,1.59,15.11 +seresnet18,288,42.19,23.675,1,11.78,3.01,4.11 +efficientnet_cc_b1_8e,240,42.18,23.68,1,39.72,0.75,15.44 +coat_lite_mini,224,42.12,23.714,1,11.01,2.0,12.25 +dpn68,224,42.01,23.778,1,12.61,2.35,10.47 +tf_mixnet_l,224,41.91,23.833,1,7.33,0.58,10.84 +efficientformerv2_s2,224,41.63,23.997,1,12.71,1.27,11.77 +tf_efficientnet_b2,260,41.61,24.005,1,9.11,1.02,13.83 +fastvit_t12,256,41.56,24.039,1,7.55,1.42,12.42 +resnet26d,224,41.27,24.203,1,16.01,2.6,8.15 +nf_regnet_b2,240,41.2,24.245,1,14.31,0.97,7.23 +resnet50d,160,40.99,24.37,1,25.58,2.22,6.08 +eca_botnext26ts_256,256,40.97,24.379,1,10.59,2.46,11.6 +tf_efficientnet_em,240,40.71,24.537,1,6.9,3.04,14.34 +resnet18d,288,40.62,24.592,1,11.71,3.41,5.43 +tf_efficientnetv2_b3,240,40.22,24.835,1,14.36,1.93,9.95 +efficientnetv2_rw_t,224,40.18,24.866,1,13.65,1.93,9.94 +efficientnet_em,240,40.1,24.91,1,6.9,3.04,14.34 +tf_efficientnet_cc_b1_8e,240,40.1,24.913,1,39.72,0.75,15.44 +mixnet_l,224,39.9,25.04,1,7.33,0.58,10.84 +seresnet50,160,39.88,25.05,1,28.09,2.1,5.69 +resnet50,176,39.81,25.095,1,25.56,2.62,6.92 +tf_efficientnet_lite3,300,39.76,25.126,1,8.2,1.65,21.85 +repvgg_b0,224,39.75,25.127,1,15.82,3.41,6.15 +botnet26t_256,256,39.7,25.167,1,12.49,3.32,11.98 +eca_halonext26ts,256,39.59,25.233,1,10.76,2.44,11.46 +levit_conv_384,224,39.52,25.276,1,39.13,2.36,6.26 +mobilevit_s,256,39.42,25.343,1,5.58,2.03,19.94 +hgnetv2_b3,288,39.28,25.432,1,16.29,2.94,8.38 +resnet26t,256,39.22,25.471,1,16.01,3.35,10.52 +dpn68b,224,39.08,25.566,1,12.61,2.35,10.47 +regnetx_032,224,39.08,25.564,1,15.3,3.2,11.37 +ecaresnet50t,160,39.07,25.569,1,25.57,2.21,6.04 +haloregnetz_b,224,38.92,25.668,1,11.68,1.97,11.94 +repvit_m3,224,38.91,25.671,1,10.68,1.89,13.94 +halonet26t,256,38.81,25.741,1,12.48,3.19,11.69 +ecaresnet50d_pruned,224,38.75,25.779,1,19.94,2.53,6.43 +edgenext_small_rw,320,38.68,25.829,1,7.83,2.46,14.85 +fbnetv3_g,288,38.42,26.003,1,16.62,1.77,21.09 +hgnetv2_b4,224,38.42,26.001,1,19.8,2.75,6.7 +coatnet_pico_rw_224,224,38.15,26.193,1,10.85,2.05,14.62 +convit_tiny,224,38.05,26.261,1,5.71,1.26,7.94 +efficientvit_b2,256,38.01,26.282,1,24.33,2.09,19.03 +nf_regnet_b2,272,37.81,26.422,1,14.31,1.22,9.27 +pit_s_224,224,37.65,26.533,1,23.46,2.88,11.56 +seresnext26ts,288,37.55,26.602,1,10.39,3.07,13.32 +resnext26ts,288,37.5,26.641,1,10.3,3.07,13.31 +mobilevitv2_125,256,37.19,26.863,1,7.48,2.86,20.1 +resnet34,224,37.02,26.985,1,21.8,3.67,3.74 +vit_small_patch32_384,384,36.96,27.025,1,22.92,3.45,8.25 +convnext_pico_ols,288,36.86,27.111,1,9.06,2.37,10.74 +pit_s_distilled_224,224,36.78,27.158,1,24.04,2.9,11.64 +tiny_vit_11m_224,224,36.53,27.347,1,20.35,2.04,13.49 +resnetblur18,288,36.4,27.445,1,11.69,3.87,5.6 +resnetrs50,160,36.37,27.467,1,35.69,2.29,6.2 +regnetz_b16,288,36.11,27.668,1,9.72,2.39,16.43 +darknet17,256,36.08,27.687,1,14.3,3.26,7.18 +regnety_032,224,36.05,27.711,1,19.44,3.2,11.26 +gc_efficientnetv2_rw_t,224,35.95,27.794,1,13.68,1.94,9.97 +fastvit_s12,256,35.89,27.845,1,9.47,1.82,13.67 +maxvit_pico_rw_256,256,35.82,27.894,1,7.46,1.83,22.3 +ecaresnet26t,256,35.72,27.972,1,16.01,3.35,10.53 +legacy_seresnet34,224,35.62,28.047,1,21.96,3.67,3.74 +mixer_s16_224,224,35.4,28.223,1,18.53,3.79,5.97 +dla60x,224,35.39,28.232,1,17.35,3.54,13.8 +eca_resnext26ts,288,35.16,28.416,1,10.3,3.07,13.32 +mobileone_s4,224,34.97,28.571,1,14.95,3.04,17.74 +resnet34d,224,34.93,28.602,1,21.82,3.91,4.54 +maxvit_rmlp_pico_rw_256,256,34.8,28.705,1,7.52,1.85,24.86 +convnext_nano,224,34.79,28.717,1,15.59,2.46,8.37 +seresnet34,224,34.75,28.749,1,21.96,3.67,3.74 +regnetz_c16,256,34.57,28.903,1,13.46,2.51,16.57 +resnest26d,224,34.46,28.994,1,17.07,3.64,9.97 +vit_betwixt_patch32_clip_224,224,34.42,29.026,1,61.41,3.09,4.17 +convnext_nano_ols,224,34.3,29.131,1,15.65,2.65,9.38 +gcresnext26ts,288,34.18,29.228,1,10.48,3.07,13.33 +convnextv2_nano,224,33.96,29.419,1,15.62,2.46,8.37 +vit_wee_patch16_reg1_gap_256,256,33.86,29.51,1,13.42,3.83,13.9 +repvit_m1_5,224,33.68,29.667,1,14.64,2.31,15.7 +fastvit_sa12,256,33.64,29.7,1,11.58,1.96,14.03 +regnetz_b16_evos,288,33.34,29.968,1,9.74,2.36,16.43 +xcit_tiny_12_p16_384,384,33.27,30.033,1,6.72,3.64,18.26 +poolformerv2_s24,224,33.25,30.052,1,21.34,3.42,10.68 +hgnet_tiny,224,33.19,30.106,1,14.74,4.54,6.36 +twins_svt_small,224,33.19,30.101,1,24.06,2.94,13.75 +mobilenetv4_hybrid_large_075,256,33.17,30.117,1,22.75,2.06,11.64 +xcit_tiny_24_p16_224,224,33.16,30.134,1,12.12,2.34,11.82 +efficientnet_b3,288,33.12,30.163,1,12.23,1.63,21.49 +resnet26,288,32.71,30.542,1,16.0,3.9,12.15 +nf_regnet_b3,288,32.68,30.576,1,18.59,1.67,11.84 +efficientvit_b2,288,32.57,30.68,1,24.33,2.64,24.03 +gcvit_xxtiny,224,32.5,30.743,1,12.0,2.14,15.36 +cait_xxs24_224,224,32.44,30.799,1,11.96,2.53,20.29 +convnextv2_pico,288,32.09,31.137,1,9.07,2.27,10.08 +skresnet34,224,31.94,31.278,1,22.28,3.67,5.13 +coatnet_nano_cc_224,224,31.9,31.328,1,13.76,2.24,15.02 +mobilenetv4_hybrid_medium,384,31.84,31.377,1,11.07,3.01,21.18 +mixer_b32_224,224,31.83,31.392,1,60.29,3.24,6.29 +hrnet_w18_small_v2,224,31.76,31.451,1,15.6,2.62,9.65 +regnetz_c16_evos,256,31.48,31.744,1,13.49,2.48,16.57 +nf_seresnet26,224,31.29,31.929,1,17.4,2.41,7.36 +rexnetr_200,288,31.29,31.935,1,16.52,2.62,24.96 +regnetx_040,224,31.24,31.982,1,22.12,3.99,12.2 +coatnet_nano_rw_224,224,31.2,32.033,1,15.14,2.41,15.41 +cspresnext50,256,31.19,32.036,1,20.57,4.05,15.86 +dla60_res2next,224,31.19,32.04,1,17.03,3.49,13.17 +nf_resnet26,224,31.16,32.071,1,16.0,2.41,7.35 +coatnext_nano_rw_224,224,31.15,32.082,1,14.7,2.47,12.8 +resnext50_32x4d,224,30.91,32.332,1,25.03,4.26,14.4 +convnext_pico,288,30.83,32.41,1,9.05,2.27,10.08 +vit_pwee_patch16_reg1_gap_256,256,30.75,32.497,1,15.25,4.37,15.87 +regnety_040,224,30.56,32.694,1,20.65,4.0,12.29 +mixnet_xl,224,30.5,32.766,1,11.9,0.93,14.57 +resnet50,224,30.48,32.78,1,25.56,4.11,11.11 +poolformer_s24,224,30.46,32.804,1,21.39,3.41,10.68 +dla60,224,30.28,32.993,1,22.04,4.26,10.16 +seresnext26t_32x4d,288,30.14,33.149,1,16.81,4.46,16.68 +resnetaa34d,224,29.99,33.316,1,21.82,4.43,5.07 +darknet21,256,29.98,33.327,1,20.86,3.93,7.47 +gernet_l,256,29.97,33.343,1,31.08,4.57,8.0 +tf_efficientnetv2_b3,300,29.89,33.434,1,14.36,3.04,15.74 +resnet50t,224,29.81,33.516,1,25.57,4.32,11.82 +resnet26d,288,29.49,33.889,1,16.01,4.29,13.48 +nf_ecaresnet26,224,29.45,33.93,1,16.0,2.41,7.36 +tf_efficientnet_b3,300,29.39,33.995,1,12.23,1.87,23.83 +deit_small_patch16_224,224,29.36,34.032,1,22.05,4.61,11.95 +resnext50d_32x4d,224,29.31,34.096,1,25.05,4.5,15.2 +resnet50d,224,29.3,34.102,1,25.58,4.35,11.92 +resnet50c,224,28.94,34.531,1,25.58,4.35,11.92 +edgenext_base,256,28.92,34.545,1,18.51,3.85,15.58 +skresnet50,224,28.92,34.553,1,25.8,4.11,12.5 +deit_small_distilled_patch16_224,224,28.75,34.757,1,22.44,4.63,12.02 +densenet121,224,28.75,34.76,1,7.98,2.87,6.9 +ecaresnetlight,224,28.74,34.769,1,30.16,4.11,8.42 +nf_regnet_b3,320,28.72,34.794,1,18.59,2.05,14.61 +seresnext50_32x4d,224,28.7,34.812,1,27.56,4.26,14.42 +ecaresnet50d_pruned,288,28.61,34.922,1,19.94,4.19,10.61 +twins_pcpvt_small,224,28.57,34.969,1,24.11,3.83,18.08 +efficientnetv2_rw_t,288,28.56,34.992,1,13.65,3.19,16.42 +legacy_seresnet50,224,28.54,35.008,1,28.09,3.88,10.6 +coatnet_rmlp_nano_rw_224,224,28.35,35.248,1,15.15,2.62,20.34 +deit3_small_patch16_224,224,28.3,35.317,1,22.06,4.61,11.95 +resnet32ts,256,28.3,35.316,1,17.96,4.63,11.58 +seresnext26d_32x4d,288,28.24,35.385,1,16.81,4.51,16.85 +seresnet50,224,28.23,35.388,1,28.09,4.11,11.13 +mobilevitv2_150,256,28.2,35.435,1,10.59,4.09,24.11 +legacy_seresnext50_32x4d,224,28.16,35.481,1,27.56,4.26,14.42 +cs3darknet_l,256,28.08,35.587,1,21.16,4.86,8.55 +resnet50_gn,224,27.93,35.772,1,25.56,4.14,11.11 +ecaresnet101d_pruned,224,27.91,35.796,1,24.88,3.48,7.69 +gcresnext50ts,256,27.84,35.895,1,15.67,3.75,15.46 +hgnetv2_b4,288,27.83,35.909,1,19.8,4.54,11.08 +efficientnet_b3,320,27.78,35.977,1,12.23,2.01,26.52 +caformer_s18,224,27.71,36.057,1,26.34,4.13,19.39 +pvt_v2_b1,224,27.63,36.165,1,14.01,2.12,15.39 +cs3darknet_focus_l,256,27.57,36.242,1,21.15,4.66,8.03 +cspresnet50,256,27.54,36.281,1,21.62,4.54,11.5 +resnet26t,320,27.51,36.326,1,16.01,5.24,16.44 +densenetblur121d,224,27.46,36.385,1,8.0,3.11,7.9 +skresnet50d,224,27.46,36.393,1,25.82,4.36,13.31 +resnetv2_50,224,27.39,36.486,1,25.55,4.11,11.11 +cspresnet50d,256,27.36,36.525,1,21.64,4.86,12.55 +ecaresnet50t,224,27.34,36.544,1,25.57,4.32,11.83 +dpn68b,288,27.22,36.717,1,12.61,3.89,17.3 +efficientnet_b3_gn,288,27.22,36.708,1,11.73,1.74,23.35 +resnetv2_50d,224,27.21,36.721,1,25.57,4.35,11.92 +efficientformer_l3,224,27.2,36.738,1,31.41,3.93,12.01 +gc_efficientnetv2_rw_t,288,27.05,36.95,1,13.68,3.2,16.45 +res2next50,224,27.0,37.006,1,24.67,4.2,13.71 +ecaresnet50d,224,26.97,37.048,1,25.58,4.35,11.93 +fastvit_mci0,256,26.96,37.067,1,11.41,2.42,18.29 +gmlp_s16_224,224,26.92,37.128,1,19.42,4.42,15.1 +res2net50_26w_4s,224,26.87,37.194,1,25.7,4.28,12.61 +vit_tiny_patch16_384,384,26.83,37.249,1,5.79,4.7,25.39 +vit_small_patch16_224,224,26.71,37.412,1,22.05,4.61,11.95 +convnext_tiny,224,26.62,37.534,1,28.59,4.47,13.44 +eca_resnet33ts,256,26.56,37.626,1,19.68,4.76,11.66 +regnetv_040,224,26.53,37.665,1,20.64,4.0,12.29 +skresnext50_32x4d,224,26.51,37.688,1,27.48,4.5,17.18 +vit_relpos_small_patch16_rpn_224,224,26.5,37.708,1,21.97,4.59,13.05 +seresnet50t,224,26.45,37.776,1,28.1,4.32,11.83 +eva02_tiny_patch14_336,336,26.39,37.866,1,5.76,4.68,27.16 +mobilenetv4_hybrid_medium,448,26.36,37.913,1,11.07,4.2,29.64 +vit_small_r26_s32_224,224,26.29,38.004,1,36.43,3.56,9.85 +gcvit_xtiny,224,26.28,38.018,1,19.98,2.93,20.26 +vit_relpos_small_patch16_224,224,26.23,38.099,1,21.98,4.59,13.05 +rexnetr_300,224,26.17,38.18,1,34.81,3.39,22.16 +regnety_040_sgn,224,26.08,38.315,1,20.65,4.03,12.29 +mobilenetv4_conv_large,256,26.05,38.363,1,32.59,2.86,12.14 +res2net50_48w_2s,224,26.05,38.36,1,25.29,4.18,11.72 +resnet33ts,256,26.05,38.358,1,19.68,4.76,11.66 +inception_next_tiny,224,25.94,38.523,1,28.06,4.19,11.98 +resnetblur50,224,25.93,38.542,1,25.56,5.16,12.02 +dla60_res2net,224,25.89,38.592,1,20.85,4.15,12.34 +sedarknet21,256,25.87,38.621,1,20.95,3.93,7.47 +xcit_tiny_12_p8_224,224,25.82,38.703,1,6.71,4.81,23.6 +gcresnet33ts,256,25.8,38.728,1,19.88,4.76,11.68 +resnetv2_50t,224,25.78,38.759,1,25.57,4.32,11.82 +mobilenetv4_conv_large,320,25.68,38.912,1,32.59,4.47,18.97 +resnetrs50,224,25.6,39.031,1,35.69,4.48,12.14 +resnetaa50,224,25.59,39.051,1,25.56,5.15,11.64 +cs3sedarknet_l,256,25.57,39.083,1,21.91,4.86,8.56 +seresnet33ts,256,25.55,39.113,1,19.78,4.76,11.66 +vit_base_patch32_clip_quickgelu_224,224,25.52,39.162,1,87.85,4.41,5.01 +res2net50d,224,25.41,39.319,1,25.72,4.52,13.41 +resnet50_clip_gap,224,25.33,39.444,1,23.53,5.39,12.44 +gmixer_24_224,224,25.32,39.467,1,24.72,5.28,14.45 +resnet101,160,25.18,39.69,1,44.55,4.0,8.28 +rexnet_300,224,25.16,39.723,1,34.71,3.44,22.4 +resnetaa50d,224,25.13,39.772,1,25.58,5.39,12.44 +resnetblur50d,224,25.03,39.922,1,25.58,5.4,12.82 +efficientformerv2_l,224,24.99,39.986,1,26.32,2.59,18.54 +resnet34,288,24.98,40.004,1,21.8,6.07,6.18 +vit_srelpos_small_patch16_224,224,24.97,40.028,1,21.97,4.59,12.16 +hiera_tiny_224,224,24.89,40.14,1,27.91,4.91,17.13 +res2net50_14w_8s,224,24.78,40.33,1,25.06,4.21,13.28 +regnety_032,288,24.68,40.5,1,19.44,5.29,18.61 +resnetv2_50d_frn,224,24.63,40.576,1,25.59,4.33,11.92 +densenet169,224,24.61,40.608,1,14.15,3.4,7.3 +resnet33ts,288,24.57,40.672,1,19.68,6.02,14.75 +efficientnet_b3_g8_gn,288,24.55,40.716,1,14.25,2.59,23.35 +convnextv2_tiny,224,24.54,40.715,1,28.64,4.47,13.44 +sehalonet33ts,256,24.53,40.735,1,13.69,3.55,14.7 +resnetv2_50d_gn,224,24.52,40.755,1,25.57,4.38,11.92 +vit_base_patch32_clip_224,224,24.37,41.007,1,88.22,4.41,5.01 +davit_tiny,224,24.36,41.026,1,28.36,4.54,18.89 +convnext_nano,288,24.35,41.038,1,15.59,4.06,13.84 +coat_lite_small,224,24.34,41.057,1,19.84,3.96,22.09 +convnextv2_nano,288,24.34,41.052,1,15.62,4.06,13.84 +ecaresnet26t,320,24.25,41.216,1,16.01,5.24,16.44 +pvt_v2_b2_li,224,24.06,41.533,1,22.55,3.91,27.6 +regnetz_c16,320,24.0,41.635,1,13.46,3.92,25.88 +resnest50d_4s2x40d,224,23.98,41.674,1,30.42,4.4,17.94 +resnet34d,288,23.92,41.785,1,21.82,6.47,7.51 +vit_base_patch32_224,224,23.74,42.092,1,88.22,4.41,5.01 +gcresnext50ts,288,23.72,42.132,1,15.67,4.75,19.57 +efficientnet_b3_gn,320,23.7,42.166,1,11.73,2.14,28.83 +seresnet34,288,23.69,42.184,1,21.96,6.07,6.18 +resnet50s,224,23.56,42.42,1,25.68,5.47,13.52 +repvgg_a2,224,23.52,42.486,1,28.21,5.7,6.26 +seresnetaa50d,224,23.38,42.748,1,28.11,5.4,12.46 +convformer_s18,224,23.35,42.802,1,26.77,3.96,15.82 +vit_small_resnet26d_224,224,23.28,42.926,1,63.61,5.07,11.12 +sam2_hiera_tiny,224,23.24,42.996,1,26.85,4.91,17.12 +flexivit_small,240,23.15,43.162,1,22.06,5.35,14.18 +poolformerv2_s36,224,23.1,43.266,1,30.79,5.01,15.82 +resnetv2_50d_evos,224,23.02,43.404,1,25.59,4.33,11.92 +xcit_small_12_p16_224,224,23.02,43.419,1,26.25,4.82,12.58 +cait_xxs36_224,224,22.89,43.667,1,17.3,3.77,30.34 +convnext_tiny_hnf,224,22.84,43.758,1,28.59,4.47,13.44 +tiny_vit_21m_224,224,22.73,43.971,1,33.22,4.29,20.08 +resnet101,176,22.71,44.012,1,44.55,4.92,10.08 +tf_efficientnet_lite4,380,22.62,44.185,1,13.01,4.04,45.66 +coatnet_bn_0_rw_224,224,22.61,44.203,1,27.44,4.67,22.04 +resnet32ts,288,22.61,44.196,1,17.96,5.86,14.65 +maxvit_nano_rw_256,256,22.59,44.245,1,15.45,4.46,30.28 +convnext_nano_ols,288,22.45,44.517,1,15.65,4.38,15.5 +resnest50d,224,22.45,44.512,1,27.48,5.4,14.36 +regnetz_c16_evos,320,22.44,44.548,1,13.49,3.86,25.88 +visformer_small,224,22.38,44.663,1,40.22,4.88,11.43 +ese_vovnet39b,224,22.3,44.813,1,24.57,7.09,6.74 +maxvit_rmlp_nano_rw_256,256,22.28,44.85,1,15.5,4.47,31.92 +cs3darknet_focus_l,288,22.27,44.874,1,21.15,5.9,10.16 +resmlp_24_224,224,22.22,44.978,1,30.02,5.96,10.91 +regnetx_064,224,22.15,45.129,1,26.21,6.49,16.37 +convmixer_1024_20_ks9_p14,224,22.09,45.242,1,24.38,5.55,5.51 +selecsls60,224,22.09,45.246,1,30.67,3.59,5.52 +tresnet_m,224,22.09,45.236,1,31.39,5.75,7.31 +nextvit_small,224,22.04,45.35,1,31.76,5.81,18.44 +ecaresnet50t,256,21.83,45.776,1,25.57,5.64,15.45 +lambda_resnet50ts,256,21.8,45.839,1,21.54,5.07,17.48 +resnet50_clip,224,21.77,45.907,1,38.32,6.14,12.98 +mobilevitv2_175,256,21.73,45.985,1,14.25,5.54,28.13 +seresnet33ts,288,21.65,46.163,1,19.78,6.02,14.76 +vit_little_patch16_reg1_gap_256,256,21.63,46.198,1,22.52,6.27,18.06 +cs3darknet_l,288,21.62,46.225,1,21.16,6.16,10.83 +poolformer_s36,224,21.62,46.237,1,30.86,5.0,15.82 +selecsls42,224,21.54,46.391,1,30.35,2.94,4.62 +gcresnet50t,256,21.51,46.467,1,25.9,5.42,14.67 +vit_little_patch16_reg4_gap_256,256,21.51,46.462,1,22.52,6.35,18.33 +inception_v3,299,21.48,46.52,1,23.83,5.73,8.97 +efficientnetv2_s,288,21.43,46.639,1,21.46,4.75,20.13 +dla102x,224,21.31,46.899,1,26.31,5.89,19.42 +hgnet_tiny,288,21.24,47.063,1,14.74,7.51,10.51 +regnetz_040_h,256,21.23,47.076,1,28.94,4.12,24.29 +selecsls60b,224,21.23,47.084,1,32.77,3.63,5.52 +sebotnet33ts_256,256,21.22,47.09,1,13.7,3.89,17.46 +coatnet_0_rw_224,224,21.11,47.346,1,27.44,4.43,18.73 +gcresnet33ts,288,21.11,47.353,1,19.88,6.02,14.78 +edgenext_base,320,21.02,47.535,1,18.51,6.01,24.32 +resnetv2_50x1_bit,224,20.97,47.659,1,25.55,4.23,11.11 +resnest50d_1s4x24d,224,20.96,47.676,1,25.68,4.43,13.57 +densenet121,288,20.94,47.724,1,7.98,4.74,11.41 +mvitv2_tiny,224,20.94,47.716,1,24.17,4.7,21.16 +levit_512,224,20.93,47.75,1,95.17,5.64,10.22 +vit_base_patch32_clip_256,256,20.88,47.863,1,87.86,5.76,6.65 +eca_resnet33ts,288,20.85,47.947,1,19.68,6.02,14.76 +efficientvit_b3,224,20.85,47.936,1,48.65,3.99,26.9 +regnetz_040,256,20.81,48.025,1,27.12,4.06,24.19 +regnetz_d8,256,20.72,48.241,1,23.37,3.97,23.74 +resnetaa34d,288,20.68,48.319,1,21.82,7.33,8.38 +regnety_064,224,20.66,48.367,1,30.58,6.39,16.41 +eca_vovnet39b,224,20.63,48.456,1,22.6,7.09,6.74 +fastvit_sa24,256,20.6,48.531,1,21.55,3.8,24.32 +regnety_040,288,20.57,48.586,1,20.65,6.61,20.3 +eva02_small_patch14_224,224,20.51,48.731,1,21.62,6.14,18.28 +coatnet_rmlp_0_rw_224,224,20.41,48.965,1,27.45,4.72,24.89 +regnetz_d8_evos,256,20.41,48.979,1,23.46,4.5,24.92 +resnet50_mlp,256,20.4,48.996,1,26.65,7.05,16.25 +cspresnet50w,256,20.38,49.046,1,28.12,5.04,12.19 +cs3sedarknet_l,288,20.36,49.078,1,21.91,6.16,10.83 +resnet50,288,20.27,49.302,1,25.56,6.8,18.37 +tnt_s_patch16_224,224,20.22,49.434,1,23.76,5.24,24.37 +selecsls42b,224,20.21,49.453,1,32.46,2.98,4.62 +crossvit_15_240,240,20.18,49.54,1,27.53,5.81,19.77 +efficientnetv2_rw_s,288,20.17,49.545,1,23.94,4.91,21.41 +nf_regnet_b4,320,20.15,49.614,1,30.21,3.29,19.88 +levit_512d,224,20.1,49.719,1,92.5,5.85,11.3 +densenetblur121d,288,20.05,49.842,1,8.0,5.14,13.06 +coatnet_0_224,224,20.03,49.905,1,25.04,4.58,24.01 +maxxvit_rmlp_nano_rw_256,256,20.03,49.896,1,16.78,4.37,26.05 +efficientnet_b3_g8_gn,320,19.99,49.99,1,14.25,3.2,28.83 +lamhalobotnet50ts_256,256,19.98,50.023,1,22.57,5.02,18.44 +nest_tiny,224,19.98,50.035,1,17.06,5.83,25.48 +repvit_m2_3,224,19.97,50.049,1,23.69,4.57,26.21 +mobilenetv4_conv_large,384,19.92,50.188,1,32.59,6.43,27.31 +mixnet_xxl,224,19.9,50.236,1,23.96,2.04,23.43 +efficientnet_b4,320,19.87,50.313,1,19.34,3.13,34.76 +cs3sedarknet_xdw,256,19.74,50.637,1,21.6,5.97,17.18 +focalnet_tiny_srf,224,19.63,50.908,1,28.43,4.42,16.32 +vovnet39a,224,19.45,51.376,1,22.6,7.09,6.73 +nest_tiny_jx,224,19.42,51.473,1,17.06,5.83,25.48 +tf_efficientnetv2_s,300,19.41,51.505,1,21.46,5.35,22.73 +cspdarknet53,256,19.38,51.573,1,27.64,6.57,16.81 +crossvit_15_dagger_240,240,19.31,51.749,1,28.21,6.13,20.43 +crossvit_small_240,240,19.31,51.758,1,26.86,5.63,18.17 +ese_vovnet39b_evos,224,19.31,51.751,1,24.58,7.07,6.74 +halonet50ts,256,19.29,51.811,1,22.73,5.3,19.2 +resnet50t,288,19.24,51.951,1,25.57,7.14,19.53 +efficientvit_l1,224,19.21,52.041,1,52.65,5.27,15.85 +resnext50_32x4d,288,19.14,52.223,1,25.03,7.04,23.81 +ecaresnet101d_pruned,288,19.12,52.28,1,24.88,5.75,12.71 +resnet50d,288,19.02,52.56,1,25.58,7.19,19.7 +dla102,224,19.0,52.604,1,33.27,7.19,14.18 +hrnet_w18,224,18.91,52.842,1,21.3,4.32,16.31 +eca_nfnet_l0,224,18.9,52.886,1,24.14,4.35,10.47 +deit3_medium_patch16_224,224,18.81,53.125,1,38.85,8.0,15.93 +regnetv_064,224,18.81,53.15,1,30.58,6.39,16.41 +resnet51q,256,18.74,53.325,1,35.7,6.38,16.55 +levit_conv_512,224,18.66,53.577,1,95.17,5.64,10.22 +seresnet50,288,18.63,53.661,1,28.09,6.8,18.39 +nf_ecaresnet50,224,18.58,53.793,1,25.56,4.21,11.13 +resnext50d_32x4d,288,18.58,53.804,1,25.05,7.44,25.13 +regnetx_080,224,18.54,53.896,1,39.57,8.02,14.06 +xcit_tiny_24_p16_384,384,18.53,53.945,1,12.12,6.87,34.29 +ecaresnetlight,288,18.49,54.067,1,30.16,6.79,13.91 +efficientnet_lite4,380,18.46,54.139,1,13.01,4.04,45.66 +res2net50_26w_6s,224,18.45,54.179,1,37.05,6.33,15.28 +hiera_small_224,224,18.4,54.311,1,35.01,6.42,20.75 +nf_seresnet50,224,18.35,54.466,1,28.09,4.21,11.13 +efficientnet_el_pruned,300,18.29,54.65,1,10.59,8.0,30.7 +mobilenetv4_conv_aa_large,384,18.24,54.793,1,32.59,7.07,32.29 +hgnet_small,224,18.18,54.971,1,24.36,8.53,8.79 +seresnext50_32x4d,288,18.17,55.012,1,27.56,7.04,23.82 +regnetv_040,288,18.16,55.041,1,20.64,6.6,20.3 +nfnet_l0,224,18.13,55.121,1,35.07,4.36,10.47 +resnet152,160,18.13,55.135,1,60.19,5.9,11.51 +tf_efficientnet_el,300,18.09,55.246,1,10.59,8.0,30.7 +maxvit_tiny_rw_224,224,18.08,55.285,1,29.06,5.11,33.11 +levit_conv_512d,224,18.04,55.4,1,92.5,5.85,11.3 +regnetz_d32,256,18.03,55.442,1,27.58,5.98,23.74 +efficientnet_el,300,18.01,55.489,1,10.59,8.0,30.7 +botnet50ts_256,256,17.92,55.786,1,22.74,5.54,22.23 +coat_tiny,224,17.9,55.83,1,5.5,4.35,27.2 +convit_small,224,17.82,56.107,1,27.78,5.76,17.87 +hgnetv2_b5,224,17.81,56.119,1,39.57,6.56,11.19 +focalnet_tiny_lrf,224,17.77,56.251,1,28.65,4.49,17.76 +densenet201,224,17.74,56.353,1,20.01,4.34,7.85 +gcresnet50t,288,17.53,57.015,1,25.9,6.86,18.57 +ecaresnet50d,288,17.4,57.445,1,25.58,7.19,19.72 +ecaresnet50t,288,17.4,57.452,1,25.57,7.14,19.55 +rexnetr_300,288,17.39,57.482,1,34.81,5.59,36.61 +seresnet50t,288,17.39,57.487,1,28.1,7.14,19.55 +maxxvitv2_nano_rw_256,256,17.38,57.518,1,23.7,6.26,23.05 +rdnet_tiny,224,17.34,57.657,1,23.86,5.06,15.98 +resnet101,224,17.34,57.657,1,44.55,7.83,16.23 +vit_relpos_medium_patch16_rpn_224,224,17.33,57.668,1,38.73,7.97,17.02 +resnet50_gn,288,17.29,57.794,1,25.56,6.85,18.37 +resnetrs101,192,17.26,57.911,1,63.62,6.04,12.7 +mobilevitv2_200,256,17.22,58.054,1,18.45,7.22,32.15 +dpn92,224,17.19,58.161,1,37.67,6.54,18.21 +regnety_040_sgn,288,17.19,58.137,1,20.65,6.67,20.3 +regnety_080,224,17.19,58.131,1,39.18,8.0,17.97 +vit_relpos_medium_patch16_224,224,17.17,58.219,1,38.75,7.97,17.02 +vit_medium_patch16_clip_224,224,17.15,58.278,1,38.59,8.0,15.93 +resnetv2_101,224,17.14,58.329,1,44.54,7.83,16.23 +resnetv2_50,288,17.09,58.478,1,25.55,6.79,18.37 +cs3darknet_focus_x,256,17.05,58.614,1,35.02,8.03,10.69 +xcit_nano_12_p8_384,384,16.99,58.831,1,3.05,6.34,46.08 +fastvit_mci1,256,16.98,58.858,1,21.54,4.72,32.84 +convnext_tiny,288,16.97,58.884,1,28.59,7.39,22.21 +resnet101c,224,16.95,58.961,1,44.57,8.08,17.04 +vit_relpos_medium_patch16_cls_224,224,16.95,58.979,1,38.76,8.03,18.24 +resnetv2_101d,224,16.89,59.16,1,44.56,8.07,17.04 +darknetaa53,256,16.8,59.481,1,36.02,7.97,12.39 +halo2botnet50ts_256,256,16.78,59.557,1,22.64,5.02,21.78 +volo_d1_224,224,16.72,59.796,1,26.63,6.94,24.43 +resnetaa50,288,16.69,59.883,1,25.56,8.52,19.24 +efficientvit_b3,256,16.66,60.01,1,48.65,5.2,35.01 +twins_pcpvt_base,224,16.65,60.03,1,43.83,6.68,25.25 +resnet61q,256,16.62,60.14,1,36.85,7.8,17.01 +resnetblur50,288,16.61,60.191,1,25.56,8.52,19.87 +vit_base_resnet26d_224,224,16.58,60.287,1,101.4,6.97,13.16 +gcvit_tiny,224,16.52,60.518,1,28.22,4.79,29.82 +resnet101d,224,16.51,60.551,1,44.57,8.08,17.04 +pvt_v2_b2,224,16.5,60.588,1,25.36,4.05,27.53 +cs3darknet_x,256,16.43,60.827,1,35.05,8.38,11.35 +ese_vovnet57b,224,16.42,60.873,1,38.61,8.95,7.52 +legacy_xception,299,16.38,61.034,1,22.86,8.4,35.83 +caformer_s36,224,16.37,61.082,1,39.3,8.0,37.53 +resnet152,176,16.36,61.094,1,60.19,7.22,13.99 +vit_base_patch32_plus_256,256,16.32,61.244,1,119.48,7.79,7.76 +regnety_080_tv,224,16.31,61.303,1,39.38,8.51,19.73 +nf_regnet_b4,384,16.3,61.318,1,30.21,4.7,28.61 +resnetblur50d,288,16.16,61.857,1,25.58,8.92,21.19 +nf_resnet50,256,16.12,62.022,1,25.56,5.46,14.52 +resnetaa50d,288,16.12,62.011,1,25.58,8.92,20.57 +legacy_seresnet101,224,16.09,62.122,1,49.33,7.61,15.74 +vit_relpos_base_patch32_plus_rpn_256,256,16.08,62.159,1,119.42,7.68,8.01 +vitamin_small_224,224,16.02,62.397,1,22.03,5.92,26.38 +convnextv2_tiny,288,16.0,62.486,1,28.64,7.39,22.21 +swin_s3_tiny_224,224,15.94,62.698,1,28.33,4.64,19.13 +resnet101_clip_gap,224,15.8,63.277,1,42.52,9.11,17.56 +resnext101_32x4d,224,15.77,63.375,1,44.18,8.01,21.23 +legacy_seresnext101_32x4d,224,15.62,63.985,1,48.96,8.02,21.26 +ecaresnet50t,320,15.61,64.017,1,25.57,8.82,24.13 +seresnext101_32x4d,224,15.61,64.046,1,48.96,8.02,21.26 +cs3sedarknet_x,256,15.58,64.167,1,35.4,8.38,11.35 +seresnetaa50d,288,15.52,64.403,1,28.11,8.92,20.59 +resnet51q,288,15.5,64.477,1,35.7,8.07,20.94 +nextvit_base,224,15.45,64.681,1,44.82,8.29,23.71 +vit_srelpos_medium_patch16_224,224,15.43,64.774,1,38.74,7.96,16.21 +vovnet57a,224,15.4,64.912,1,36.64,8.95,7.52 +resnet101s,224,15.39,64.967,1,44.67,9.19,18.64 +seresnet101,224,15.38,64.992,1,49.33,7.84,16.27 +resnetaa101d,224,15.21,65.73,1,44.57,9.12,17.56 +ecaresnet101d,224,15.14,66.034,1,44.57,8.08,17.07 +mvitv2_small,224,15.14,66.027,1,34.87,7.0,28.08 +repvgg_b1g4,224,15.08,66.265,1,39.97,8.15,10.64 +resnetblur101d,224,15.07,66.319,1,44.57,9.12,17.94 +resmlp_36_224,224,15.04,66.459,1,44.69,8.91,16.33 +efficientvit_l2,224,15.01,66.578,1,63.71,6.97,19.58 +hrnet_w18_ssld,224,15.01,66.595,1,21.3,4.32,16.31 +hieradet_small,256,15.0,66.639,1,34.72,8.51,27.76 +regnetz_040_h,320,15.0,66.65,1,28.94,6.43,37.94 +hiera_small_abswin_256,256,14.93,66.937,1,34.36,8.29,26.38 +regnetz_d8,320,14.89,67.145,1,23.37,6.19,37.08 +vit_medium_patch16_gap_240,240,14.88,67.192,1,44.4,9.22,18.81 +maxvit_tiny_tf_224,224,14.78,67.612,1,30.92,5.6,35.78 +tresnet_v2_l,224,14.74,67.805,1,46.17,8.85,16.34 +vit_base_r26_s32_224,224,14.74,67.808,1,101.38,6.81,12.36 +fastvit_sa36,256,14.73,67.874,1,31.53,5.64,34.61 +res2net101_26w_4s,224,14.73,67.84,1,45.21,8.1,18.45 +ese_vovnet39b,288,14.65,68.251,1,24.57,11.71,11.13 +tf_efficientnet_b4,380,14.63,68.312,1,19.34,4.49,49.49 +efficientnet_b4,384,14.42,69.335,1,19.34,4.51,50.04 +res2net101d,224,14.35,69.65,1,45.23,8.35,19.25 +dla102x2,224,14.29,69.96,1,41.28,9.34,29.91 +regnetz_040,320,14.25,70.144,1,27.12,6.35,37.78 +darknet53,256,14.22,70.293,1,41.61,9.31,12.39 +resnet101_clip,224,14.2,70.412,1,56.26,9.81,18.08 +xcit_tiny_24_p8_224,224,14.19,70.43,1,12.11,9.21,45.39 +regnetz_d8_evos,320,14.17,70.558,1,23.46,7.03,38.92 +resnetv2_50d_gn,288,14.15,70.641,1,25.57,7.24,19.7 +mobilenetv4_hybrid_large,384,14.13,70.77,1,37.76,7.77,34.52 +efficientvit_b3,288,14.11,70.869,1,48.65,6.58,44.2 +convnextv2_nano,384,14.05,71.163,1,15.62,7.22,24.61 +res2net50_26w_8s,224,14.04,71.2,1,48.4,8.37,17.95 +darknetaa53,288,14.0,71.413,1,36.02,10.08,15.68 +xception41p,299,14.0,71.38,1,26.91,9.25,39.86 +selecsls84,224,13.98,71.492,1,50.95,5.9,7.57 +crossvit_18_240,240,13.96,71.63,1,43.27,9.05,26.26 +nf_resnet50,288,13.95,71.667,1,25.56,6.88,18.37 +hrnet_w18_ssld,288,13.94,71.685,1,21.3,7.14,26.96 +vit_base_resnet50d_224,224,13.9,71.926,1,110.97,8.73,16.92 +resnet101d,256,13.83,72.262,1,44.57,10.55,22.25 +convformer_s36,224,13.8,72.438,1,40.01,7.67,30.5 +eca_nfnet_l0,288,13.77,72.613,1,24.14,7.12,17.29 +crossvit_18_dagger_240,240,13.74,72.779,1,44.27,9.5,27.03 +regnety_064,288,13.72,72.855,1,30.58,10.56,27.11 +convnext_small,224,13.67,73.118,1,50.22,8.71,21.56 +inception_next_small,224,13.67,73.105,1,49.37,8.36,19.27 +mvitv2_small_cls,224,13.66,73.202,1,34.87,7.04,28.17 +poolformerv2_m36,224,13.66,73.166,1,56.08,8.81,22.02 +densenet161,224,13.58,73.622,1,28.68,7.79,11.06 +efficientnetv2_s,384,13.56,73.714,1,21.46,8.44,35.77 +tf_efficientnetv2_s,384,13.52,73.932,1,21.46,8.44,35.77 +nfnet_l0,288,13.49,74.084,1,35.07,7.13,17.29 +mobilevitv2_150,384,13.48,74.141,1,10.59,9.2,54.25 +coat_mini,224,13.45,74.346,1,10.34,6.82,33.68 +convnext_tiny_hnf,288,13.42,74.494,1,28.59,7.39,22.21 +vit_medium_patch16_gap_256,256,13.41,74.536,1,38.86,10.59,22.15 +swinv2_cr_tiny_224,224,13.37,74.759,1,28.33,4.66,28.45 +resnet61q,288,13.36,74.793,1,36.85,9.87,21.52 +resnetv2_50d_evos,288,13.35,74.854,1,25.59,7.15,19.7 +vit_medium_patch16_reg1_gap_256,256,13.34,74.917,1,38.88,10.63,22.26 +hiera_base_224,224,13.32,75.074,1,51.52,9.4,30.42 +maxxvit_rmlp_tiny_rw_256,256,13.31,75.078,1,29.64,6.66,39.76 +vit_medium_patch16_rope_reg1_gap_256,256,13.29,75.192,1,38.74,10.63,22.26 +xception41,299,13.25,75.445,1,26.97,9.28,39.86 +efficientnetv2_rw_s,384,13.21,75.671,1,23.94,8.72,38.03 +swinv2_cr_tiny_ns_224,224,13.19,75.808,1,28.33,4.66,28.45 +cs3darknet_x,288,13.16,75.972,1,35.05,10.6,14.36 +vit_medium_patch16_reg4_gap_256,256,13.11,76.222,1,38.88,10.76,22.6 +resnetv2_101x1_bit,224,13.02,76.755,1,44.54,8.04,16.23 +twins_svt_base,224,12.99,76.97,1,56.07,8.59,26.33 +coatnet_rmlp_1_rw_224,224,12.93,77.316,1,41.69,7.85,35.47 +convnextv2_small,224,12.84,77.862,1,50.32,8.71,21.56 +mobilenetv4_conv_large,448,12.81,78.014,1,32.59,8.75,37.17 +cs3sedarknet_x,288,12.68,78.865,1,35.4,10.6,14.37 +regnetx_120,224,12.63,79.151,1,46.11,12.13,21.37 +xcit_small_24_p16_224,224,12.59,79.418,1,47.67,9.1,23.64 +davit_small,224,12.56,79.582,1,49.75,8.8,30.49 +poolformer_m36,224,12.5,79.957,1,56.17,8.8,22.02 +coatnet_1_rw_224,224,12.42,80.466,1,41.72,8.04,34.6 +swin_tiny_patch4_window7_224,224,12.42,80.508,1,28.29,4.51,17.06 +coatnet_rmlp_1_rw2_224,224,12.39,80.67,1,41.72,8.11,40.13 +hrnet_w30,224,12.38,80.735,1,37.71,8.15,21.21 +hrnet_w32,224,12.34,81.003,1,41.23,8.97,22.02 +tresnet_l,224,12.28,81.411,1,55.99,10.9,11.9 +wide_resnet50_2,176,12.22,81.798,1,68.88,7.29,8.97 +resnet152,224,12.12,82.493,1,60.19,11.56,22.56 +resnext101_32x8d,176,12.11,82.522,1,88.79,10.33,19.37 +regnetv_064,288,12.09,82.675,1,30.58,10.55,27.11 +resnet152d,224,12.09,82.714,1,60.21,11.8,23.36 +resnet152c,224,12.08,82.742,1,60.21,11.8,23.36 +regnety_120,224,12.07,82.847,1,51.82,12.14,21.38 +pvt_v2_b3,224,12.06,82.917,1,45.24,6.92,37.7 +vit_small_r26_s32_384,384,12.05,82.953,1,36.47,10.43,29.85 +darknet53,288,12.01,83.223,1,41.61,11.78,15.68 +dla169,224,12.0,83.33,1,53.39,11.6,20.2 +nextvit_large,224,11.87,84.205,1,57.87,10.78,28.99 +cait_s24_224,224,11.85,84.345,1,46.92,9.35,40.58 +efficientvit_l2,256,11.85,84.387,1,63.71,9.09,25.49 +hgnet_small,288,11.83,84.498,1,24.36,14.09,14.53 +cs3edgenet_x,256,11.82,84.601,1,47.82,11.53,12.92 +fastvit_mci2,256,11.77,84.931,1,35.82,7.91,43.34 +twins_pcpvt_large,224,11.75,85.04,1,60.99,9.84,35.82 +mobilenetv4_conv_aa_large,448,11.69,85.545,1,32.59,9.63,43.94 +vit_small_resnet50d_s16_224,224,11.61,86.102,1,57.53,13.48,24.82 +fastvit_ma36,256,11.6,86.188,1,44.07,7.88,41.09 +efficientformer_l7,224,11.59,86.272,1,82.23,10.17,24.45 +regnetz_d32,320,11.54,86.648,1,27.58,9.33,37.08 +rdnet_small,224,11.51,86.884,1,50.44,8.74,22.55 +legacy_seresnet152,224,11.4,87.709,1,66.82,11.33,22.08 +hgnetv2_b5,288,11.33,88.251,1,39.57,10.84,18.5 +coatnet_1_224,224,11.32,88.289,1,42.23,8.7,39.0 +resnetv2_152d,224,11.31,88.39,1,60.2,11.8,23.36 +regnety_080,288,11.26,88.798,1,39.18,13.22,29.69 +nest_small_jx,224,11.24,88.941,1,38.35,10.35,40.04 +mixer_b16_224,224,11.22,89.103,1,59.88,12.62,14.53 +levit_384_s8,224,11.2,89.297,1,39.12,9.98,35.86 +resnetv2_152,224,11.2,89.225,1,60.19,11.55,22.56 +nf_resnet101,224,11.16,89.569,1,44.55,8.01,16.23 +inception_v4,299,11.15,89.619,1,42.68,12.28,15.09 +resnet152s,224,11.13,89.808,1,60.32,12.92,24.96 +nest_small,224,11.12,89.891,1,38.35,10.35,40.04 +resnet101,288,11.12,89.884,1,44.55,12.95,26.83 +cs3se_edgenet_x,256,11.11,89.994,1,50.72,11.53,12.94 +dpn98,224,11.06,90.407,1,61.57,11.73,25.2 +coat_lite_medium,224,11.05,90.497,1,44.57,9.81,40.06 +deit3_small_patch16_384,384,11.04,90.541,1,22.21,15.52,50.78 +nf_ecaresnet101,224,10.88,91.856,1,44.55,8.01,16.27 +nf_seresnet101,224,10.88,91.896,1,49.33,8.02,16.27 +seresnet152,224,10.79,92.672,1,66.82,11.57,22.61 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+maxvit_rmlp_small_rw_224,224,10.24,97.618,1,64.9,10.75,49.3 +seresnext101_32x4d,288,10.23,97.701,1,48.96,13.25,35.12 +gcvit_small,224,10.19,98.115,1,51.09,8.57,41.61 +vit_base_patch32_384,384,10.18,98.198,1,88.3,13.06,16.5 +mvitv2_base,224,10.16,98.419,1,51.47,10.16,40.5 +mobilenetv4_conv_aa_large,480,10.13,98.667,1,32.59,11.05,50.45 +hiera_base_plus_224,224,10.1,99.002,1,69.9,12.67,37.98 +hiera_base_abswin_256,256,10.09,99.038,1,51.27,12.46,40.7 +seresnet101,288,10.09,99.045,1,49.33,12.95,26.87 +tnt_b_patch16_224,224,10.09,99.034,1,65.41,14.09,39.01 +cs3edgenet_x,288,10.08,99.208,1,47.82,14.59,16.36 +vit_base_patch32_clip_384,384,10.07,99.249,1,88.3,13.06,16.5 +focalnet_small_lrf,224,9.99,100.025,1,50.34,8.74,28.61 +vit_small_patch16_384,384,9.98,100.186,1,22.2,15.52,50.78 +mixer_l32_224,224,9.95,100.463,1,206.94,11.27,19.86 +convformer_s18,384,9.85,101.499,1,26.77,11.63,46.49 +ese_vovnet99b,224,9.85,101.454,1,63.2,16.51,11.27 +resnetrs101,288,9.84,101.582,1,63.62,13.56,28.53 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+beit_base_patch16_224,224,9.41,106.266,1,86.53,17.58,23.9 +eca_nfnet_l1,256,9.35,106.973,1,41.41,9.62,22.04 +wide_resnet50_2,224,9.35,106.889,1,68.88,11.43,14.4 +deit3_base_patch16_224,224,9.3,107.552,1,86.59,17.58,23.9 +repvgg_b2g4,224,9.25,108.051,1,61.76,12.63,12.9 +beitv2_base_patch16_224,224,9.24,108.256,1,86.53,17.58,23.9 +deit_base_distilled_patch16_224,224,9.21,108.526,1,87.34,17.68,24.05 +xception65,299,9.21,108.536,1,39.92,13.96,52.48 +eva02_small_patch14_336,336,9.11,109.777,1,22.13,15.48,54.33 +xcit_tiny_12_p8_384,384,9.1,109.816,1,6.71,14.13,69.14 +convnext_base,224,9.09,110.021,1,88.59,15.38,28.75 +vit_small_patch16_36x1_224,224,9.09,110.009,1,64.67,13.71,35.69 +resnext101_64x4d,224,9.01,110.983,1,83.46,15.52,31.21 +vit_small_patch16_18x2_224,224,9.0,111.03,1,64.67,13.71,35.69 +coat_small,224,8.98,111.393,1,21.69,12.61,44.25 +seresnext101_64x4d,224,8.97,111.422,1,88.23,15.53,31.25 +convformer_m36,224,8.95,111.669,1,57.05,12.89,42.05 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+maxvit_xlarge_tf_384,384,0.44,2276.68,1,475.32,292.78,668.76 +resnetv2_152x4_bit,224,0.44,2264.656,1,936.53,186.9,90.22 +eva02_large_patch14_448,448,0.42,2395.984,1,305.08,362.33,689.95 +regnety_1280,384,0.41,2419.045,1,644.81,374.99,210.2 +vit_huge_patch14_clip_336,336,0.41,2463.126,1,632.46,390.97,407.54 +xcit_large_24_p8_384,384,0.38,2625.871,1,188.93,415.0,531.82 +nfnet_f6,576,0.37,2705.399,1,438.36,378.69,452.2 +dm_nfnet_f6,576,0.36,2764.673,1,438.36,378.69,452.2 +volo_d5_512,512,0.35,2888.125,1,296.09,425.09,1105.37 +vit_gigantic_patch14_224,224,0.33,3043.914,1,1844.44,483.95,275.37 +vit_gigantic_patch14_clip_224,224,0.33,3054.145,1,1844.91,483.96,275.37 +vit_so400m_patch14_siglip_gap_448,448,0.33,3055.238,1,413.33,487.18,764.26 +swinv2_cr_huge_384,384,0.32,3112.935,1,657.94,352.04,583.18 +vit_huge_patch14_clip_quickgelu_378,378,0.32,3153.715,1,632.68,503.79,572.79 +cait_m48_448,448,0.31,3237.951,1,356.46,329.41,1708.23 +convnextv2_huge,512,0.31,3235.039,1,660.29,600.81,413.07 +vit_huge_patch14_clip_378,378,0.31,3192.651,1,632.68,503.79,572.79 +vit_large_patch14_dinov2,518,0.3,3283.515,1,304.37,507.15,1058.82 +vit_large_patch14_reg4_dinov2,518,0.3,3302.339,1,304.37,508.9,1064.02 +nfnet_f7,608,0.29,3432.385,1,499.5,480.39,570.85 +vit_huge_patch16_gap_448,448,0.29,3444.702,1,631.67,544.7,636.83 +swinv2_cr_giant_224,224,0.28,3558.238,1,2598.76,483.85,309.15 +eva_giant_patch14_336,336,0.25,3940.881,1,1013.01,620.64,550.67 +samvit_base_patch16,1024,0.24,4123.097,1,89.67,486.43,1343.27 +davit_huge_fl,768,0.23,4324.634,1,360.64,744.84,1060.3 +maxvit_xlarge_tf_512,512,0.22,4520.247,1,475.77,534.14,1413.22 +efficientnet_l2,800,0.21,4705.895,1,480.31,479.12,1707.39 +regnety_2560,384,0.21,4775.125,1,1282.6,747.83,296.49 +tf_efficientnet_l2,800,0.21,4750.597,1,480.31,479.12,1707.39 +resnetv2_152x4_bit,480,0.17,5875.601,1,936.53,844.84,414.26 +sam2_hiera_large,1024,0.16,6214.186,1,212.15,907.48,2190.34 +eva02_enormous_patch14_clip_224,224,0.14,6939.876,1,4350.56,1132.46,497.58 +vit_giant_patch14_dinov2,518,0.1,10207.402,1,1136.48,1784.2,2757.89 +vit_giant_patch14_reg4_dinov2,518,0.1,10361.576,1,1136.48,1790.08,2771.21 +eva_giant_patch14_560,560,0.09,10775.568,1,1014.45,1906.76,2577.17 +samvit_large_patch16,1024,0.09,11652.888,1,308.28,1493.86,2553.78 +swinv2_cr_giant_384,384,0.09,11052.423,1,2598.76,1450.71,1394.86 +vit_so400m_patch14_siglip_gap_896,896,0.06,16814.851,1,416.87,2731.49,8492.88 +samvit_huge_patch16,1024,0.05,21270.331,1,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt240-cpu-i9_10940x-dynamo.csv b/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt240-cpu-i9_10940x-dynamo.csv new file mode 100644 index 0000000000000000000000000000000000000000..08cb260231b8cdb87b3cb3a261b4e0cde15ae59d --- /dev/null +++ b/pytorch-image-models/results/benchmark-infer-fp32-nchw-pt240-cpu-i9_10940x-dynamo.csv @@ -0,0 +1,1436 @@ +model,infer_img_size,infer_samples_per_sec,infer_step_time,infer_batch_size,param_count,infer_gmacs,infer_macts +test_vit,160,707.29,1.405,1,0.37,0.04,0.48 +test_byobnet,160,682.63,1.456,1,0.46,0.03,0.43 +test_efficientnet,160,642.73,1.547,1,0.36,0.06,0.55 +lcnet_035,224,382.51,2.604,1,1.64,0.03,1.04 +resnet10t,176,353.93,2.811,1,5.44,0.7,1.51 +tf_mobilenetv3_small_minimal_100,224,353.74,2.817,1,2.04,0.06,1.41 +lcnet_050,224,342.17,2.912,1,1.88,0.05,1.26 +mobilenetv3_small_050,224,310.68,3.207,1,1.59,0.03,0.92 +tinynet_e,106,298.2,3.341,1,2.04,0.03,0.69 +lcnet_075,224,288.24,3.457,1,2.36,0.1,1.99 +lcnet_150,224,281.14,3.544,1,4.5,0.34,3.79 +lcnet_100,224,263.86,3.777,1,2.95,0.16,2.52 +mobilenetv2_035,224,263.63,3.783,1,1.68,0.07,2.86 +mobilenetv3_small_075,224,253.25,3.937,1,2.04,0.05,1.3 +levit_128s,224,252.75,3.943,1,7.78,0.31,1.88 +mobilenetv3_small_100,224,249.8,3.991,1,2.54,0.06,1.42 +mobilenetv1_100,224,248.33,4.013,1,4.23,0.58,5.04 +mobilenetv4_conv_small,224,246.85,4.038,1,3.77,0.19,1.97 +regnetx_002,224,246.41,4.047,1,2.68,0.2,2.16 +tf_mobilenetv3_small_100,224,242.79,4.106,1,2.54,0.06,1.42 +tf_mobilenetv3_small_075,224,239.83,4.157,1,2.04,0.05,1.3 +mobilenetv2_050,224,237.22,4.205,1,1.97,0.1,3.64 +mobilenetv1_125,224,236.41,4.215,1,6.27,0.89,6.3 +mobilenetv1_100h,224,232.23,4.292,1,5.28,0.63,5.09 +mnasnet_050,224,220.94,4.515,1,2.22,0.11,3.07 +resnet18,160,218.15,4.568,1,11.69,0.93,1.27 +ese_vovnet19b_slim,224,217.47,4.584,1,3.17,1.69,3.52 +mobilenetv4_conv_small,256,215.85,4.62,1,3.77,0.25,2.57 +efficientvit_b0,224,210.08,4.748,1,3.41,0.1,2.87 +mobilenetv1_125,256,206.54,4.827,1,6.27,1.16,8.23 +mnasnet_small,224,204.46,4.879,1,2.03,0.07,2.16 +tinynet_d,152,200.46,4.976,1,2.34,0.05,1.42 +mobilenetv2_075,224,198.9,5.015,1,2.64,0.22,5.86 +mobilenetv1_100h,256,196.41,5.077,1,5.28,0.82,6.65 +mnasnet_075,224,196.4,5.078,1,3.17,0.23,4.77 +levit_conv_128s,224,195.14,5.111,1,7.78,0.31,1.88 +mobilenetv1_100,256,193.77,5.146,1,4.23,0.76,6.59 +hardcorenas_a,224,192.83,5.172,1,5.26,0.23,4.38 +mobilenetv3_large_075,224,190.56,5.234,1,3.99,0.16,4.0 +levit_192,224,190.44,5.237,1,10.95,0.66,3.2 +ese_vovnet19b_slim_dw,224,190.32,5.24,1,1.9,0.4,5.28 +levit_128,224,188.71,5.285,1,9.21,0.41,2.71 +mobilenetv2_100,224,188.22,5.299,1,3.5,0.31,6.68 +mnasnet_100,224,184.21,5.415,1,4.38,0.33,5.46 +pit_ti_distilled_224,224,182.36,5.47,1,5.1,0.71,6.23 +deit_tiny_patch16_224,224,181.9,5.484,1,5.72,1.26,5.97 +vit_tiny_patch16_224,224,181.68,5.49,1,5.72,1.26,5.97 +semnasnet_050,224,181.4,5.5,1,2.08,0.11,3.44 +efficientnet_lite0,224,181.39,5.499,1,4.65,0.4,6.74 +gernet_s,224,180.68,5.52,1,8.17,0.75,2.65 +vit_tiny_r_s16_p8_224,224,180.34,5.53,1,6.34,0.44,2.06 +resnet10t,224,179.52,5.555,1,5.44,1.1,2.43 +deit_tiny_distilled_patch16_224,224,178.78,5.579,1,5.91,1.27,6.01 +hgnetv2_b0,224,178.19,5.598,1,6.0,0.33,2.12 +mobilenet_edgetpu_v2_xs,224,176.26,5.66,1,4.46,0.7,4.8 +mobilenetv3_large_100,224,176.25,5.659,1,5.48,0.23,4.41 +mobilenetv3_rw,224,175.76,5.676,1,5.48,0.23,4.41 +tf_mobilenetv3_large_minimal_100,224,174.07,5.73,1,3.92,0.22,4.4 +mixer_s32_224,224,170.9,5.836,1,19.1,1.0,2.28 +cs3darknet_focus_s,256,167.84,5.944,1,3.27,0.69,2.7 +pit_ti_224,224,167.47,5.957,1,4.85,0.7,6.19 +mobilenetv2_140,224,163.23,6.112,1,6.11,0.6,9.57 +mobilenet_edgetpu_100,224,160.55,6.215,1,4.09,1.0,5.75 +tf_mobilenetv3_large_075,224,159.49,6.256,1,3.99,0.16,4.0 +cs3darknet_s,256,158.87,6.28,1,3.28,0.72,2.97 +hardcorenas_b,224,158.23,6.306,1,5.18,0.26,5.09 +vit_small_patch32_224,224,158.12,6.31,1,22.88,1.15,2.5 +ese_vovnet19b_dw,224,157.95,6.316,1,6.54,1.34,8.25 +regnety_002,224,157.24,6.345,1,3.16,0.2,2.17 +edgenext_xx_small,256,156.86,6.364,1,1.33,0.26,3.33 +dla46_c,224,155.31,6.425,1,1.3,0.58,4.5 +resnet14t,176,155.29,6.425,1,10.08,1.07,3.61 +hardcorenas_c,224,153.28,6.51,1,5.52,0.28,5.01 +regnetx_004_tv,224,150.43,6.634,1,5.5,0.42,3.17 +dla46x_c,224,150.23,6.643,1,1.07,0.54,5.66 +spnasnet_100,224,150.13,6.647,1,4.42,0.35,6.03 +tf_mobilenetv3_large_100,224,149.59,6.67,1,5.48,0.23,4.41 +mobilenet_edgetpu_v2_s,224,147.34,6.773,1,5.99,1.21,6.6 +fbnetc_100,224,147.19,6.78,1,5.57,0.4,6.51 +semnasnet_075,224,147.11,6.784,1,2.91,0.23,5.54 +mnasnet_140,224,146.87,6.794,1,7.12,0.6,7.71 +semnasnet_100,224,146.31,6.821,1,3.89,0.32,6.23 +gmixer_12_224,224,146.18,6.826,1,12.7,2.67,7.26 +gmlp_ti16_224,224,146.06,6.832,1,5.87,1.34,7.55 +efficientvit_m1,224,144.48,6.91,1,2.98,0.17,1.33 +tf_efficientnet_lite0,224,143.01,6.978,1,4.65,0.4,6.74 +efficientnet_es,224,143.0,6.979,1,5.44,1.81,8.73 +pit_xs_224,224,141.88,7.033,1,10.62,1.4,7.71 +tf_efficientnet_es,224,141.88,7.034,1,5.44,1.81,8.73 +efficientnet_es_pruned,224,141.73,7.041,1,5.44,1.81,8.73 +resnet14t,224,141.53,7.051,1,10.08,1.69,5.8 +mobilenetv2_110d,224,141.41,7.057,1,4.52,0.45,8.71 +levit_conv_128,224,141.12,7.072,1,9.21,0.41,2.71 +pit_xs_distilled_224,224,141.01,7.077,1,11.0,1.41,7.76 +mobilevitv2_050,256,140.89,7.084,1,1.37,0.48,8.04 +efficientnet_lite1,240,140.13,7.122,1,5.42,0.62,10.14 +regnetx_004,224,139.23,7.169,1,5.16,0.4,3.14 +levit_conv_192,224,138.83,7.189,1,10.95,0.66,3.2 +levit_256,224,137.56,7.255,1,18.89,1.13,4.23 +efficientvit_m2,224,137.23,7.274,1,4.19,0.2,1.47 +regnetx_006,224,135.97,7.341,1,6.2,0.61,3.98 +repghostnet_058,224,135.13,7.387,1,2.55,0.07,2.59 +efficientvit_m0,224,135.04,7.393,1,2.35,0.08,0.91 +semnasnet_140,224,134.8,7.404,1,6.11,0.6,8.87 +ghostnet_050,224,134.36,7.43,1,2.59,0.05,1.77 +regnetx_008,224,133.61,7.47,1,7.26,0.81,5.15 +tinynet_c,184,131.97,7.564,1,2.46,0.11,2.87 +ese_vovnet19b_dw,288,131.11,7.612,1,6.54,2.22,13.63 +hgnetv2_b1,224,130.75,7.633,1,6.34,0.49,2.73 +edgenext_xx_small,288,130.47,7.652,1,1.33,0.33,4.21 +repghostnet_050,224,130.45,7.653,1,2.31,0.05,2.02 +mobilenet_edgetpu_v2_m,224,129.27,7.721,1,8.46,1.85,8.15 +convnext_atto,224,129.03,7.736,1,3.7,0.55,3.81 +repghostnet_080,224,128.98,7.74,1,3.28,0.1,3.22 +mobileone_s1,224,128.61,7.761,1,4.83,0.86,9.67 +edgenext_x_small,256,126.77,7.875,1,2.34,0.54,5.93 +resnet18,224,126.23,7.907,1,11.69,1.82,2.48 +resnext26ts,256,126.06,7.918,1,10.3,2.43,10.52 +dla60x_c,224,123.16,8.107,1,1.32,0.59,6.01 +ghostnet_100,224,122.98,8.117,1,5.18,0.15,3.55 +convnextv2_atto,224,122.08,8.178,1,3.71,0.55,3.81 +repghostnet_100,224,122.06,8.18,1,4.07,0.15,3.98 +hgnetv2_b0,288,121.94,8.186,1,6.0,0.54,3.51 +efficientnet_lite2,260,120.94,8.254,1,6.09,0.89,12.9 +resnet34,160,120.62,8.275,1,21.8,1.87,1.91 +hardcorenas_f,224,120.04,8.317,1,8.2,0.35,5.57 +convnext_atto_ols,224,119.07,8.384,1,3.7,0.58,4.11 +convnext_femto_ols,224,118.75,8.407,1,5.23,0.82,4.87 +resnet18d,224,118.43,8.429,1,11.71,2.06,3.29 +edgenext_x_small,288,118.24,8.444,1,2.34,0.68,7.5 +repghostnet_111,224,118.2,8.447,1,4.54,0.18,4.38 +resnetblur18,224,117.55,8.492,1,11.69,2.34,3.39 +mobilenet_edgetpu_v2_m,256,117.43,8.501,1,8.46,2.42,10.65 +hardcorenas_e,224,117.2,8.518,1,8.07,0.35,5.65 +vit_xsmall_patch16_clip_224,224,116.75,8.551,1,8.28,1.79,6.65 +eca_resnext26ts,256,115.44,8.647,1,10.3,2.43,10.52 +mobilenet_edgetpu_v2_l,224,115.23,8.663,1,10.92,2.55,9.05 +regnety_008,224,115.15,8.67,1,6.26,0.81,5.25 +mobilenetv2_120d,224,115.14,8.67,1,5.83,0.69,11.97 +mobilenetv4_conv_medium,224,115.13,8.671,1,9.72,0.84,5.8 +visformer_tiny,224,115.0,8.681,1,10.32,1.27,5.72 +efficientvit_m3,224,114.86,8.693,1,6.9,0.27,1.62 +mobileone_s2,224,113.76,8.776,1,7.88,1.34,11.55 +tf_efficientnet_lite1,240,112.67,8.86,1,5.42,0.62,10.14 +convnext_femto,224,112.57,8.869,1,5.22,0.79,4.57 +mobilevitv2_075,256,112.53,8.872,1,2.87,1.05,12.06 +ecaresnext50t_32x4d,224,112.38,8.883,1,15.41,2.7,10.09 +resmlp_12_224,224,112.17,8.9,1,15.35,3.01,5.5 +repghostnet_130,224,111.65,8.942,1,5.48,0.25,5.24 +lambda_resnet26t,256,111.48,8.955,1,10.96,3.02,11.87 +ecaresnext26t_32x4d,224,111.45,8.957,1,15.41,2.7,10.09 +regnety_008_tv,224,111.42,8.961,1,6.43,0.84,5.42 +resnext26ts,288,111.36,8.965,1,10.3,3.07,13.31 +resnet18,288,111.26,8.973,1,11.69,3.01,4.11 +rexnetr_100,224,110.23,9.058,1,4.88,0.43,7.72 +poolformerv2_s12,224,110.08,9.069,1,11.89,1.83,5.53 +hardcorenas_d,224,109.61,9.11,1,7.5,0.3,4.93 +mobilenetv4_conv_medium,256,109.52,9.116,1,9.72,1.1,7.58 +edgenext_small,256,109.41,9.125,1,5.59,1.26,9.07 +levit_conv_256,224,109.21,9.143,1,18.89,1.13,4.23 +seresnet18,224,109.21,9.142,1,11.78,1.82,2.49 +tinynet_b,188,108.98,9.162,1,3.73,0.21,4.44 +regnety_004,224,108.81,9.177,1,4.34,0.41,3.89 +seresnext26ts,256,108.64,9.19,1,10.39,2.43,10.52 +poolformer_s12,224,108.39,9.211,1,11.92,1.82,5.53 +rexnet_100,224,108.28,9.221,1,4.8,0.41,7.44 +resnest14d,224,107.97,9.247,1,10.61,2.76,7.33 +botnet26t_256,256,107.88,9.254,1,12.49,3.32,11.98 +legacy_seresnet18,224,107.78,9.264,1,11.78,1.82,2.49 +ghostnet_130,224,107.61,9.278,1,7.36,0.24,4.6 +eva02_tiny_patch14_224,224,107.45,9.293,1,5.5,1.7,9.14 +legacy_seresnext26_32x4d,224,107.29,9.305,1,16.79,2.49,9.39 +gernet_m,224,106.38,9.385,1,21.14,3.02,5.24 +efficientvit_b1,256,106.12,9.409,1,9.1,0.69,9.46 +convnext_atto,288,106.05,9.416,1,3.7,0.91,6.3 +eca_botnext26ts_256,256,105.99,9.42,1,10.59,2.46,11.6 +efficientvit_m4,224,105.66,9.45,1,8.8,0.3,1.7 +rexnetr_130,224,105.52,9.462,1,7.61,0.68,9.81 +hgnetv2_b2,224,105.37,9.475,1,11.22,1.15,4.12 +efficientvit_b1,224,104.82,9.526,1,9.1,0.53,7.25 +repghostnet_150,224,104.8,9.528,1,6.58,0.32,6.0 +mixer_s16_224,224,104.38,9.565,1,18.53,3.79,5.97 +fastvit_t8,256,104.04,9.598,1,4.03,0.7,8.63 +resnet18d,288,104.01,9.6,1,11.71,3.41,5.43 +efficientformer_l1,224,103.55,9.642,1,12.29,1.3,5.53 +mobileone_s3,224,103.38,9.659,1,10.17,1.94,13.85 +regnetx_016,224,103.31,9.664,1,9.19,1.62,7.93 +mobilenetv4_conv_aa_medium,256,102.95,9.698,1,9.72,1.58,10.3 +regnety_006,224,102.78,9.716,1,6.06,0.61,4.33 +seresnext26d_32x4d,224,102.22,9.767,1,16.81,2.73,10.19 +mobilenetv4_conv_blur_medium,224,101.88,9.801,1,9.72,1.22,8.58 +eca_resnext26ts,288,101.54,9.834,1,10.3,3.07,13.32 +efficientnet_b0,256,101.51,9.837,1,5.29,0.52,8.81 +hgnet_tiny,224,101.32,9.854,1,14.74,4.54,6.36 +seresnext26t_32x4d,224,100.84,9.901,1,16.81,2.7,10.09 +pit_s_224,224,100.54,9.931,1,23.46,2.88,11.56 +resnet26,224,100.47,9.938,1,16.0,2.36,7.35 +tf_efficientnet_lite2,260,100.42,9.943,1,6.09,0.89,12.9 +efficientnet_b0,224,100.41,9.945,1,5.29,0.4,6.75 +halonet26t,256,100.03,9.982,1,12.48,3.19,11.69 +resnetblur18,288,99.96,9.989,1,11.69,3.87,5.6 +repvgg_a0,224,99.9,9.995,1,9.11,1.52,3.59 +vit_medium_patch32_clip_224,224,99.8,10.005,1,39.69,2.0,3.34 +mobilevit_xxs,256,99.69,10.017,1,1.27,0.42,8.34 +convnext_femto,288,99.64,10.021,1,5.22,1.3,7.56 +convnext_femto_ols,288,99.29,10.057,1,5.23,1.35,8.06 +efficientnet_em,240,99.13,10.073,1,6.9,3.04,14.34 +rexnetr_150,224,98.92,10.095,1,9.78,0.89,11.13 +mobilenetv4_conv_medium,320,98.53,10.135,1,9.72,1.71,11.84 +seresnet18,288,98.53,10.135,1,11.78,3.01,4.11 +hgnetv2_b1,288,98.34,10.154,1,6.34,0.82,4.51 +edgenext_small_rw,256,98.33,10.155,1,7.83,1.58,9.51 +eca_halonext26ts,256,98.17,10.172,1,10.76,2.44,11.46 +tf_efficientnetv2_b0,192,97.93,10.197,1,7.14,0.54,3.51 +rexnet_130,224,97.92,10.198,1,7.56,0.68,9.71 +lambda_resnet26rpt_256,256,97.62,10.229,1,10.99,3.16,11.87 +levit_256d,224,97.59,10.232,1,26.21,1.4,4.93 +tf_efficientnet_em,240,97.41,10.252,1,6.9,3.04,14.34 +vit_tiny_r_s16_p8_384,384,96.81,10.314,1,6.36,1.34,6.49 +efficientvit_b1,288,96.74,10.323,1,9.1,0.87,11.96 +rexnet_150,224,96.5,10.348,1,9.73,0.9,11.21 +gcresnext26ts,256,96.09,10.392,1,10.48,2.43,10.53 +resnet26d,224,96.02,10.399,1,16.01,2.6,8.15 +convnext_pico,224,95.9,10.413,1,9.05,1.37,6.1 +mobilenetv4_conv_blur_medium,256,95.82,10.421,1,9.72,1.59,11.2 +repghostnet_200,224,95.57,10.449,1,9.8,0.54,7.96 +vit_wee_patch16_reg1_gap_256,256,95.45,10.462,1,13.42,3.83,13.9 +convit_tiny,224,95.05,10.506,1,5.71,1.26,7.94 +xcit_nano_12_p16_224,224,94.66,10.551,1,3.05,0.56,4.17 +seresnext26ts,288,94.65,10.55,1,10.39,3.07,13.32 +tinynet_a,192,94.03,10.62,1,6.19,0.35,5.41 +mobilevitv2_100,256,93.64,10.665,1,4.9,1.84,16.08 +pit_s_distilled_224,224,93.54,10.676,1,24.04,2.9,11.64 +pvt_v2_b0,224,93.24,10.71,1,3.67,0.57,7.99 +mobilenetv3_large_150d,224,92.84,10.756,1,14.62,, +convnextv2_atto,288,92.51,10.795,1,3.71,0.91,6.3 +efficientnet_lite3,300,92.29,10.82,1,8.2,1.65,21.85 +convnext_pico_ols,224,91.85,10.873,1,9.06,1.43,6.5 +efficientnet_b0_g16_evos,224,91.32,10.937,1,8.11,1.01,7.42 +cs3darknet_focus_m,288,90.7,11.01,1,9.3,2.51,6.19 +resnext50_32x4d,160,90.34,11.055,1,25.03,2.17,7.35 +convnext_atto_ols,288,89.86,11.114,1,3.7,0.96,6.8 +cs3darknet_m,256,89.72,11.13,1,9.31,2.08,5.28 +levit_384,224,89.69,11.134,1,39.13,2.36,6.26 +mobilenetv4_hybrid_medium_075,224,89.67,11.138,1,7.31,0.66,5.65 +cs3darknet_focus_m,256,89.19,11.197,1,9.3,1.98,4.89 +efficientformerv2_s0,224,89.05,11.216,1,3.6,0.41,5.3 +convnextv2_femto,224,88.91,11.232,1,5.23,0.79,4.57 +resnext50_32x4d,176,88.9,11.233,1,25.03,2.71,8.97 +tf_efficientnetv2_b0,224,88.9,11.234,1,7.14,0.73,4.77 +efficientnet_cc_b0_4e,224,88.85,11.24,1,13.31,0.41,9.42 +resnet26t,256,88.38,11.3,1,16.01,3.35,10.52 +tf_efficientnet_b0,224,88.14,11.33,1,5.29,0.4,6.75 +fastvit_t12,256,88.12,11.333,1,7.55,1.42,12.42 +edgenext_small,320,88.04,11.343,1,5.59,1.97,14.16 +dla34,224,87.87,11.366,1,15.74,3.07,5.02 +cs3darknet_m,288,87.77,11.378,1,9.31,2.63,6.69 +resnet26,288,87.72,11.384,1,16.0,3.9,12.15 +efficientvit_m5,224,87.3,11.441,1,12.47,0.53,2.41 +vit_tiny_patch16_384,384,87.07,11.47,1,5.79,4.7,25.39 +skresnet18,224,87.06,11.471,1,11.96,1.82,3.24 +gcresnext26ts,288,86.9,11.493,1,10.48,3.07,13.33 +hgnetv2_b2,288,86.5,11.546,1,11.22,1.89,6.8 +mobilevitv2_125,256,85.3,11.709,1,7.48,2.86,20.1 +repvgg_a1,224,85.15,11.728,1,14.09,2.64,4.74 +rexnetr_200,224,84.71,11.79,1,16.52,1.59,15.11 +hgnetv2_b3,224,84.48,11.822,1,16.29,1.78,5.07 +darknet17,256,84.06,11.881,1,14.3,3.26,7.18 +vit_betwixt_patch32_clip_224,224,83.79,11.919,1,61.41,3.09,4.17 +efficientnet_blur_b0,224,83.71,11.932,1,5.29,0.43,8.72 +resnet33ts,256,83.15,12.01,1,19.68,4.76,11.66 +resnet26d,288,82.94,12.042,1,16.01,4.29,13.48 +efficientnet_cc_b0_8e,224,82.88,12.051,1,24.01,0.42,9.42 +rexnet_200,224,82.7,12.077,1,16.37,1.56,14.91 +crossvit_tiny_240,240,82.5,12.107,1,7.01,1.57,9.08 +seresnext26t_32x4d,288,82.5,12.105,1,16.81,4.46,16.68 +vit_pwee_patch16_reg1_gap_256,256,82.09,12.167,1,15.25,4.37,15.87 +fastvit_s12,256,81.63,12.236,1,9.47,1.82,13.67 +vit_small_patch32_384,384,81.63,12.236,1,22.92,3.45,8.25 +edgenext_small_rw,320,81.46,12.261,1,7.83,2.46,14.85 +ecaresnet26t,256,81.44,12.263,1,16.01,3.35,10.53 +crossvit_9_240,240,81.43,12.267,1,8.55,1.85,9.52 +mobilevit_xs,256,80.96,12.337,1,2.32,1.05,16.33 +mobilenetv4_hybrid_medium,224,80.75,12.369,1,11.07,0.98,6.84 +resnet34,224,80.61,12.391,1,21.8,3.67,3.74 +regnetx_032,224,80.6,12.392,1,15.3,3.2,11.37 +xcit_tiny_12_p16_224,224,80.35,12.432,1,6.72,1.24,6.29 +mobilenetv4_hybrid_medium,256,79.87,12.506,1,11.07,1.29,9.01 +tf_efficientnetv2_b2,208,79.66,12.539,1,10.1,1.06,6.0 +tf_efficientnet_cc_b0_4e,224,79.65,12.541,1,13.31,0.41,9.42 +gmixer_24_224,224,79.39,12.581,1,24.72,5.28,14.45 +eca_resnet33ts,256,79.24,12.604,1,19.68,4.76,11.66 +xcit_nano_12_p16_384,384,79.2,12.612,1,3.05,1.64,12.15 +mobileone_s4,224,79.16,12.618,1,14.95,3.04,17.74 +levit_conv_256d,224,79.12,12.625,1,26.21,1.4,4.93 +dla60x,224,78.71,12.691,1,17.35,3.54,13.8 +resnet32ts,288,78.66,12.697,1,17.96,5.86,14.65 +fastvit_sa12,256,78.18,12.776,1,11.58,1.96,14.03 +resnet34d,224,78.17,12.777,1,21.82,3.91,4.54 +regnetz_005,224,78.07,12.795,1,7.12,0.52,5.86 +tf_efficientnetv2_b1,192,77.79,12.842,1,8.14,0.76,4.59 +crossvit_9_dagger_240,240,77.65,12.864,1,8.78,1.99,9.97 +resnest26d,224,77.61,12.869,1,17.07,3.64,9.97 +vit_srelpos_small_patch16_224,224,77.26,12.928,1,21.97,4.59,12.16 +resnet50,160,77.2,12.938,1,25.56,2.1,5.67 +seresnext26d_32x4d,288,77.0,12.973,1,16.81,4.51,16.85 +resnext50_32x4d,224,76.91,12.987,1,25.03,4.26,14.4 +ecaresnet50d_pruned,224,76.62,13.036,1,19.94,2.53,6.43 +vit_small_patch16_224,224,76.49,13.058,1,22.05,4.61,11.95 +deit3_small_patch16_224,224,76.48,13.06,1,22.06,4.61,11.95 +deit_small_patch16_224,224,76.41,13.072,1,22.05,4.61,11.95 +tf_efficientnetv2_b1,240,76.41,13.073,1,8.14,1.21,7.34 +deit_small_distilled_patch16_224,224,76.14,13.119,1,22.44,4.63,12.02 +convnextv2_pico,224,76.02,13.14,1,9.07,1.37,6.1 +resnet26t,320,75.79,13.179,1,16.01,5.24,16.44 +coat_lite_mini,224,75.62,13.208,1,11.01,2.0,12.25 +nf_regnet_b0,192,75.34,13.258,1,8.76,0.37,3.15 +resnetaa34d,224,75.18,13.287,1,21.82,4.43,5.07 +xcit_nano_12_p8_224,224,75.15,13.293,1,3.05,2.16,15.71 +resnet50d,160,75.14,13.293,1,25.58,2.22,6.08 +tf_efficientnet_cc_b0_8e,224,74.92,13.333,1,24.01,0.42,9.42 +resnext50d_32x4d,224,74.78,13.358,1,25.05,4.5,15.2 +resnet50,176,74.75,13.362,1,25.56,2.62,6.92 +fbnetv3_b,224,74.72,13.368,1,8.6,0.42,6.97 +resnet33ts,288,74.42,13.421,1,19.68,6.02,14.75 +tiny_vit_5m_224,224,74.28,13.448,1,12.08,1.28,11.25 +mixer_b32_224,224,74.14,13.472,1,60.29,3.24,6.29 +vit_little_patch16_reg1_gap_256,256,74.1,13.481,1,22.52,6.27,18.06 +gernet_l,256,74.0,13.498,1,31.08,4.57,8.0 +dpn48b,224,73.68,13.557,1,9.13,1.69,8.92 +hgnet_tiny,288,73.65,13.563,1,14.74,7.51,10.51 +efficientnet_b1_pruned,240,73.55,13.583,1,6.33,0.4,6.21 +vit_little_patch16_reg4_gap_256,256,73.51,13.589,1,22.52,6.35,18.33 +efficientnet_b1,240,73.32,13.625,1,7.79,0.71,10.88 +cspresnext50,256,73.13,13.66,1,20.57,4.05,15.86 +vovnet39a,224,73.1,13.665,1,22.6,7.09,6.73 +gmlp_s16_224,224,73.08,13.668,1,19.42,4.42,15.1 +mobilevitv2_150,256,72.89,13.705,1,10.59,4.09,24.11 +regnetz_b16,224,72.69,13.743,1,9.72,1.45,9.95 +edgenext_base,256,72.61,13.756,1,18.51,3.85,15.58 +resnet32ts,256,72.54,13.77,1,17.96,4.63,11.58 +coat_lite_tiny,224,72.5,13.777,1,5.72,1.6,11.65 +regnetz_005,288,72.4,13.799,1,7.12,0.86,9.68 +repvgg_b0,224,72.16,13.843,1,15.82,3.41,6.15 +hgnetv2_b4,224,71.76,13.92,1,19.8,2.75,6.7 +dla60,224,71.69,13.933,1,22.04,4.26,10.16 +efficientnet_b1,224,71.49,13.974,1,7.79,0.59,9.36 +levit_conv_384,224,70.82,14.105,1,39.13,2.36,6.26 +efficientnet_b1,256,70.76,14.119,1,7.79,0.77,12.22 +regnetx_040,224,70.59,14.151,1,22.12,3.99,12.2 +tf_efficientnet_lite3,300,70.56,14.156,1,8.2,1.65,21.85 +eca_vovnet39b,224,70.47,14.174,1,22.6,7.09,6.74 +efficientnet_b2_pruned,260,70.35,14.199,1,8.31,0.73,9.13 +tf_efficientnetv2_b2,260,70.25,14.221,1,10.1,1.72,9.84 +hgnetv2_b3,288,70.06,14.259,1,16.29,2.94,8.38 +fbnetv3_d,224,70.0,14.271,1,10.31,0.52,8.5 +convnext_nano_ols,224,69.98,14.274,1,15.65,2.65,9.38 +fbnetv3_b,256,69.83,14.307,1,8.6,0.55,9.1 +repvit_m0_9,224,69.8,14.313,1,5.49,0.83,7.45 +efficientformerv2_s1,224,69.57,14.36,1,6.19,0.67,7.66 +darknet21,256,69.5,14.374,1,20.86,3.93,7.47 +ese_vovnet39b,224,69.4,14.394,1,24.57,7.09,6.74 +eca_resnet33ts,288,69.39,14.395,1,19.68,6.02,14.76 +nf_regnet_b0,256,69.34,14.407,1,8.76,0.64,5.58 +ecaresnet50t,160,69.28,14.419,1,25.57,2.21,6.04 +repvit_m1,224,69.18,14.442,1,5.49,0.83,7.45 +rexnetr_200,288,69.08,14.46,1,16.52,2.62,24.96 +res2net50_48w_2s,224,69.0,14.478,1,25.29,4.18,11.72 +resnet34,288,68.86,14.507,1,21.8,6.07,6.18 +flexivit_small,240,68.81,14.517,1,22.06,5.35,14.18 +legacy_seresnet34,224,68.78,14.524,1,21.96,3.67,3.74 +convnext_tiny,224,68.68,14.544,1,28.59,4.47,13.44 +ecaresnet26t,320,68.52,14.579,1,16.01,5.24,16.44 +regnetz_b16_evos,224,68.42,14.6,1,9.74,1.43,9.95 +repvit_m1_1,224,68.38,14.61,1,8.8,1.36,9.43 +efficientnet_b2,256,68.17,14.655,1,9.11,0.89,12.81 +efficientvit_b2,224,68.17,14.655,1,24.33,1.6,14.62 +repvit_m2,224,67.69,14.76,1,8.8,1.36,9.43 +seresnet34,224,67.32,14.839,1,21.96,3.67,3.74 +resnet50,224,67.2,14.865,1,25.56,4.11,11.11 +vit_relpos_small_patch16_rpn_224,224,67.18,14.869,1,21.97,4.59,13.05 +convnextv2_femto,288,66.86,14.941,1,5.23,1.3,7.56 +resnet34d,288,66.79,14.958,1,21.82,6.47,7.51 +gcresnet33ts,256,66.39,15.047,1,19.88,4.76,11.68 +tf_efficientnet_b1,240,66.1,15.114,1,7.79,0.71,10.88 +fbnetv3_d,256,66.05,15.125,1,10.31,0.68,11.1 +xcit_tiny_12_p16_384,384,65.58,15.233,1,6.72,3.64,18.26 +vit_relpos_small_patch16_224,224,65.51,15.25,1,21.98,4.59,13.05 +resnet50c,224,65.48,15.257,1,25.58,4.35,11.92 +dpn68,224,65.46,15.261,1,12.61,2.35,10.47 +ecaresnet50d_pruned,288,65.44,15.265,1,19.94,4.19,10.61 +efficientnet_b1,288,65.21,15.322,1,7.79,0.97,15.46 +repvit_m1_0,224,65.07,15.356,1,7.3,1.13,8.69 +twins_pcpvt_small,224,65.06,15.355,1,24.11,3.83,18.08 +mobilenetv4_hybrid_medium,320,65.04,15.36,1,11.07,2.05,14.36 +sedarknet21,256,64.98,15.373,1,20.95,3.93,7.47 +resnet50d,224,64.83,15.41,1,25.58,4.35,11.92 +resnetaa34d,288,64.72,15.435,1,21.82,7.33,8.38 +hiera_tiny_224,224,64.52,15.483,1,27.91,4.91,17.13 +poolformerv2_s24,224,64.33,15.529,1,21.34,3.42,10.68 +dpn68b,224,64.26,15.545,1,12.61,2.35,10.47 +ghostnetv2_100,224,64.11,15.585,1,6.16,0.18,4.55 +resnet50t,224,63.72,15.678,1,25.57,4.32,11.82 +resnetv2_50,224,63.72,15.68,1,25.55,4.11,11.11 +ghostnetv2_130,224,63.65,15.697,1,8.96,0.28,5.9 +resnetaa50,224,63.65,15.694,1,25.56,5.15,11.64 +seresnet50,160,63.58,15.713,1,28.09,2.1,5.69 +regnetz_b16,288,63.55,15.72,1,9.72,2.39,16.43 +convmixer_1024_20_ks9_p14,224,63.5,15.734,1,24.38,5.55,5.51 +efficientnet_b2,288,63.48,15.739,1,9.11,1.12,16.2 +resnext50_32x4d,288,63.36,15.767,1,25.03,7.04,23.81 +coatnet_pico_rw_224,224,63.31,15.781,1,10.85,2.05,14.62 +regnetx_064,224,63.21,15.805,1,26.21,6.49,16.37 +regnety_016,224,62.98,15.864,1,11.2,1.63,8.04 +poolformer_s24,224,62.88,15.889,1,21.39,3.41,10.68 +rexnetr_300,224,62.86,15.891,1,34.81,3.39,22.16 +nf_resnet26,224,62.76,15.919,1,16.0,2.41,7.35 +sam2_hiera_tiny,224,62.71,15.931,1,26.85,4.91,17.12 +hgnet_small,224,62.6,15.958,1,24.36,8.53,8.79 +mobilevitv2_175,256,62.59,15.963,1,14.25,5.54,28.13 +efficientvit_b2,288,62.24,16.05,1,24.33,2.64,24.03 +seresnext50_32x4d,224,62.23,16.053,1,27.56,4.26,14.42 +resnext50d_32x4d,288,62.18,16.068,1,25.05,7.44,25.13 +convnext_nano,224,62.15,16.076,1,15.59,2.46,8.37 +regnety_032,224,61.92,16.135,1,19.44,3.2,11.26 +resnet50_clip_gap,224,61.9,16.138,1,23.53,5.39,12.44 +resnetaa50d,224,61.9,16.139,1,25.58,5.39,12.44 +resnetv2_50d,224,61.83,16.157,1,25.57,4.35,11.92 +resnetblur50,224,61.61,16.216,1,25.56,5.16,12.02 +tiny_vit_11m_224,224,61.55,16.233,1,20.35,2.04,13.49 +resnet50s,224,61.42,16.265,1,25.68,5.47,13.52 +visformer_small,224,61.4,16.271,1,40.22,4.88,11.43 +gcresnet33ts,288,61.33,16.29,1,19.88,6.02,14.78 +convnextv2_tiny,224,61.3,16.298,1,28.64,4.47,13.44 +regnetz_c16,256,61.07,16.36,1,13.46,2.51,16.57 +cs3darknet_l,256,61.04,16.366,1,21.16,4.86,8.55 +tf_efficientnetv2_b3,240,61.03,16.372,1,14.36,1.93,9.95 +resnet51q,256,61.0,16.378,1,35.7,6.38,16.55 +legacy_seresnext50_32x4d,224,60.86,16.415,1,27.56,4.26,14.42 +vit_base_patch32_224,224,60.58,16.492,1,88.22,4.41,5.01 +resnetv2_50t,224,60.55,16.501,1,25.57,4.32,11.82 +mobilevit_s,256,60.52,16.509,1,5.58,2.03,19.94 +xcit_tiny_12_p8_224,224,60.28,16.576,1,6.71,4.81,23.6 +efficientvit_b2,256,60.14,16.612,1,24.33,2.09,19.03 +resnetrs50,160,60.13,16.615,1,35.69,2.29,6.2 +tf_efficientnet_b2,260,60.06,16.636,1,9.11,1.02,13.83 +hgnetv2_b4,288,59.96,16.662,1,19.8,4.54,11.08 +twins_svt_small,224,59.94,16.668,1,24.06,2.94,13.75 +efficientnet_el_pruned,300,59.77,16.716,1,10.59,8.0,30.7 +seresnet34,288,59.66,16.747,1,21.96,6.07,6.18 +vit_base_patch32_clip_quickgelu_224,224,59.58,16.77,1,87.85,4.41,5.01 +efficientnet_cc_b1_8e,240,59.51,16.79,1,39.72,0.75,15.44 +hrnet_w18_small,224,59.47,16.799,1,13.19,1.61,5.72 +efficientformer_l3,224,59.4,16.82,1,31.41,3.93,12.01 +tf_mixnet_s,224,59.38,16.826,1,4.13,0.25,6.25 +efficientnet_el,300,59.35,16.834,1,10.59,8.0,30.7 +ecaresnetlight,224,59.32,16.843,1,30.16,4.11,8.42 +rexnet_300,224,59.29,16.85,1,34.71,3.44,22.4 +volo_d1_224,224,59.28,16.853,1,26.63,6.94,24.43 +resnetblur50d,224,59.19,16.88,1,25.58,5.4,12.82 +vit_base_patch32_clip_224,224,59.16,16.887,1,88.22,4.41,5.01 +tf_efficientnet_el,300,59.06,16.916,1,10.59,8.0,30.7 +seresnet33ts,256,59.02,16.928,1,19.78,4.76,11.66 +efficientnet_lite4,380,59.0,16.933,1,13.01,4.04,45.66 +ecaresnet50t,224,58.9,16.963,1,25.57,4.32,11.83 +resmlp_24_224,224,58.64,17.039,1,30.02,5.96,10.91 +ghostnetv2_160,224,58.59,17.053,1,12.39,0.42,7.23 +nf_ecaresnet26,224,58.55,17.063,1,16.0,2.41,7.36 +ecaresnet50d,224,58.5,17.08,1,25.58,4.35,11.93 +mobilenetv4_conv_large,256,58.28,17.144,1,32.59,2.86,12.14 +mixnet_s,224,58.26,17.15,1,4.13,0.25,6.25 +mobilenetv4_hybrid_medium,384,57.89,17.259,1,11.07,3.01,21.18 +maxvit_pico_rw_256,256,57.53,17.368,1,7.46,1.83,22.3 +eva02_tiny_patch14_336,336,57.45,17.391,1,5.76,4.68,27.16 +resnetv2_50d_frn,224,57.35,17.421,1,25.59,4.33,11.92 +cs3darknet_focus_l,256,57.29,17.438,1,21.15,4.66,8.03 +repvgg_a2,224,57.04,17.514,1,28.21,5.7,6.26 +regnety_040,224,56.92,17.552,1,20.65,4.0,12.29 +edgenext_base,320,56.81,17.588,1,18.51,6.01,24.32 +convit_small,224,56.77,17.601,1,27.78,5.76,17.87 +crossvit_15_240,240,56.47,17.694,1,27.53,5.81,19.77 +gcresnext50ts,256,56.33,17.737,1,15.67,3.75,15.46 +nf_seresnet26,224,56.21,17.774,1,17.4,2.41,7.36 +nf_regnet_b1,256,56.15,17.796,1,10.22,0.82,7.27 +legacy_seresnet50,224,55.97,17.851,1,28.09,3.88,10.6 +dpn68b,288,55.95,17.857,1,12.61,3.89,17.3 +inception_next_tiny,224,55.95,17.858,1,28.06,4.19,11.98 +resnet50,288,55.91,17.869,1,25.56,6.8,18.37 +efficientnet_b3_pruned,300,55.81,17.904,1,9.86,1.04,11.86 +cait_xxs24_224,224,55.71,17.936,1,11.96,2.53,20.29 +hiera_small_224,224,55.7,17.938,1,35.01,6.42,20.75 +lambda_resnet50ts,256,55.57,17.981,1,21.54,5.07,17.48 +tf_efficientnet_lite4,380,55.5,18.002,1,13.01,4.04,45.66 +resnet50d,288,55.43,18.027,1,25.58,7.19,19.7 +haloregnetz_b,224,55.38,18.043,1,11.68,1.97,11.94 +resnet50_mlp,256,55.38,18.042,1,26.65,7.05,16.25 +fbnetv3_g,240,55.16,18.115,1,16.62,1.28,14.87 +vit_base_patch32_clip_256,256,54.94,18.186,1,87.86,5.76,6.65 +seresnet33ts,288,54.78,18.24,1,19.78,6.02,14.76 +resnet50t,288,54.68,18.272,1,25.57,7.14,19.53 +resnet51q,288,54.6,18.301,1,35.7,8.07,20.94 +sehalonet33ts,256,54.54,18.322,1,13.69,3.55,14.7 +caformer_s18,224,54.34,18.388,1,26.34,4.13,19.39 +ecaresnet50t,256,54.3,18.402,1,25.57,5.64,15.45 +regnetv_040,224,54.21,18.43,1,20.64,4.0,12.29 +seresnet50,224,54.16,18.45,1,28.09,4.11,11.13 +swin_s3_tiny_224,224,54.08,18.477,1,28.33,4.64,19.13 +tnt_s_patch16_224,224,53.96,18.516,1,23.76,5.24,24.37 +nf_regnet_b1,288,53.92,18.531,1,10.22,1.02,9.2 +tf_efficientnet_cc_b1_8e,240,53.91,18.536,1,39.72,0.75,15.44 +efficientnetv2_rw_t,224,53.9,18.539,1,13.65,1.93,9.94 +vit_srelpos_medium_patch16_224,224,53.78,18.578,1,38.74,7.96,16.21 +deit3_medium_patch16_224,224,53.65,18.623,1,38.85,8.0,15.93 +vit_small_resnet26d_224,224,53.52,18.668,1,63.61,5.07,11.12 +nest_tiny,224,53.5,18.675,1,17.06,5.83,25.48 +xcit_small_12_p16_224,224,53.49,18.68,1,26.25,4.82,12.58 +cs3darknet_l,288,53.41,18.707,1,21.16,6.16,10.83 +crossvit_15_dagger_240,240,53.29,18.75,1,28.21,6.13,20.43 +seresnet50t,224,53.29,18.749,1,28.1,4.32,11.83 +regnetz_b16_evos,288,53.13,18.805,1,9.74,2.36,16.43 +mobilenetv4_conv_large,320,52.96,18.867,1,32.59,4.47,18.97 +pvt_v2_b2_li,224,52.85,18.908,1,22.55,3.91,27.6 +skresnet50,224,52.83,18.915,1,25.8,4.11,12.5 +mobilevitv2_200,256,52.49,19.037,1,18.45,7.22,32.15 +resnetaa50,288,52.3,19.104,1,25.56,8.52,19.24 +ese_vovnet39b,288,52.13,19.169,1,24.57,11.71,11.13 +efficientnet_b3,288,51.98,19.224,1,12.23,1.63,21.49 +resnet50_clip,224,51.98,19.223,1,38.32,6.14,12.98 +tf_efficientnetv2_b3,300,51.94,19.237,1,14.36,3.04,15.74 +resnet61q,256,51.91,19.248,1,36.85,7.8,17.01 +coatnet_bn_0_rw_224,224,51.84,19.274,1,27.44,4.67,22.04 +crossvit_small_240,240,51.78,19.299,1,26.86,5.63,18.17 +cs3darknet_focus_l,288,51.73,19.315,1,21.15,5.9,10.16 +regnety_032,288,51.72,19.319,1,19.44,5.29,18.61 +skresnet50d,224,51.66,19.343,1,25.82,4.36,13.31 +resnetrs50,224,51.65,19.346,1,35.69,4.48,12.14 +nest_tiny_jx,224,51.64,19.349,1,17.06,5.83,25.48 +regnetz_c16,320,51.62,19.359,1,13.46,3.92,25.88 +mobilenetv4_hybrid_medium,448,51.57,19.376,1,11.07,4.2,29.64 +vovnet57a,224,51.53,19.39,1,36.64,8.95,7.52 +seresnetaa50d,224,51.35,19.459,1,28.11,5.4,12.46 +gcresnext50ts,288,51.14,19.538,1,15.67,4.75,19.57 +resnetblur50,288,51.1,19.555,1,25.56,8.52,19.87 +convnext_pico,288,51.06,19.57,1,9.05,2.27,10.08 +regnetz_c16_evos,256,51.06,19.57,1,13.49,2.48,16.57 +res2net50_26w_4s,224,50.93,19.621,1,25.7,4.28,12.61 +dla102x,224,50.88,19.637,1,26.31,5.89,19.42 +nf_regnet_b2,240,50.85,19.653,1,14.31,0.97,7.23 +convnextv2_nano,224,50.84,19.655,1,15.62,2.46,8.37 +tiny_vit_21m_224,224,50.8,19.67,1,33.22,4.29,20.08 +resnetaa50d,288,50.77,19.683,1,25.58,8.92,20.57 +nf_regnet_b2,272,50.75,19.691,1,14.31,1.22,9.27 +skresnet34,224,50.69,19.712,1,22.28,3.67,5.13 +eva02_small_patch14_224,224,50.62,19.74,1,21.62,6.14,18.28 +pvt_v2_b1,224,50.59,19.752,1,14.01,2.12,15.39 +mobilenetv4_hybrid_large_075,256,50.54,19.772,1,22.75,2.06,11.64 +vit_relpos_base_patch32_plus_rpn_256,256,50.44,19.809,1,119.42,7.68,8.01 +fbnetv3_g,288,50.35,19.845,1,16.62,1.77,21.09 +dla60_res2next,224,50.32,19.857,1,17.03,3.49,13.17 +convnext_pico_ols,288,50.27,19.879,1,9.06,2.37,10.74 +sebotnet33ts_256,256,50.06,19.961,1,13.7,3.89,17.46 +res2net50d,224,50.05,19.963,1,25.72,4.52,13.41 +ese_vovnet57b,224,49.97,19.996,1,38.61,8.95,7.52 +ecaresnetlight,288,49.96,20.0,1,30.16,6.79,13.91 +fastvit_mci0,256,49.92,20.017,1,11.41,2.42,18.29 +vit_relpos_medium_patch16_rpn_224,224,49.84,20.05,1,38.73,7.97,17.02 +regnetx_080,224,49.83,20.051,1,39.57,8.02,14.06 +maxvit_rmlp_pico_rw_256,256,49.77,20.08,1,7.52,1.85,24.86 +seresnext50_32x4d,288,49.63,20.133,1,27.56,7.04,23.82 +resnest50d,224,49.59,20.15,1,27.48,5.4,14.36 +dla60_res2net,224,49.54,20.171,1,20.85,4.15,12.34 +skresnext50_32x4d,224,49.52,20.179,1,27.48,4.5,17.18 +regnety_040,288,49.44,20.21,1,20.65,6.61,20.3 +convnext_tiny,288,49.43,20.215,1,28.59,7.39,22.21 +nextvit_small,224,49.39,20.232,1,31.76,5.81,18.44 +repvit_m3,224,49.37,20.243,1,10.68,1.89,13.94 +vit_medium_patch16_gap_240,240,49.3,20.268,1,44.4,9.22,18.81 +coatnet_nano_cc_224,224,49.22,20.303,1,13.76,2.24,15.02 +res2next50,224,49.07,20.363,1,24.67,4.2,13.71 +vit_base_patch32_plus_256,256,49.04,20.376,1,119.48,7.79,7.76 +mobilenetv4_conv_large,384,48.88,20.443,1,32.59,6.43,27.31 +resnetblur50d,288,48.87,20.447,1,25.58,8.92,21.19 +ecaresnet50d,288,48.86,20.451,1,25.58,7.19,19.72 +resnest50d_1s4x24d,224,48.8,20.474,1,25.68,4.43,13.57 +poolformerv2_s36,224,48.68,20.527,1,30.79,5.01,15.82 +vit_medium_patch16_clip_224,224,48.65,20.539,1,38.59,8.0,15.93 +vit_relpos_medium_patch16_224,224,48.6,20.561,1,38.75,7.97,17.02 +rexnetr_300,288,48.34,20.671,1,34.81,5.59,36.61 +cs3sedarknet_l,256,48.22,20.723,1,21.91,4.86,8.56 +gcvit_xxtiny,224,48.16,20.748,1,12.0,2.14,15.36 +davit_tiny,224,48.09,20.778,1,28.36,4.54,18.89 +lamhalobotnet50ts_256,256,48.01,20.812,1,22.57,5.02,18.44 +convnext_nano_ols,288,47.75,20.929,1,15.65,4.38,15.5 +convnextv2_pico,288,47.57,21.008,1,9.07,2.27,10.08 +gc_efficientnetv2_rw_t,224,47.43,21.07,1,13.68,1.94,9.97 +coatnet_0_rw_224,224,47.41,21.079,1,27.44,4.43,18.73 +hgnet_small,288,47.4,21.08,1,24.36,14.09,14.53 +regnety_040_sgn,224,47.27,21.142,1,20.65,4.03,12.29 +coatnet_0_224,224,47.25,21.147,1,25.04,4.58,24.01 +efficientnet_b3,320,47.25,21.147,1,12.23,2.01,26.52 +regnetv_040,288,47.25,21.15,1,20.64,6.6,20.3 +swinv2_cr_tiny_ns_224,224,47.24,21.152,1,28.33,4.66,28.45 +resnest50d_4s2x40d,224,47.16,21.189,1,30.42,4.4,17.94 +maxvit_nano_rw_256,256,47.1,21.214,1,15.45,4.46,30.28 +resnetv2_50,288,47.07,21.228,1,25.55,6.79,18.37 +cspresnet50d,256,46.79,21.355,1,21.64,4.86,12.55 +resnet61q,288,46.75,21.373,1,36.85,9.87,21.52 +swinv2_cr_tiny_224,224,46.75,21.377,1,28.33,4.66,28.45 +coatnet_nano_rw_224,224,46.73,21.383,1,15.14,2.41,15.41 +regnety_080_tv,224,46.7,21.397,1,39.38,8.51,19.73 +cspresnet50,256,46.68,21.408,1,21.62,4.54,11.5 +regnety_080,224,46.66,21.415,1,39.18,8.0,17.97 +crossvit_18_240,240,46.59,21.449,1,43.27,9.05,26.26 +efficientnetv2_rw_t,288,46.49,21.496,1,13.65,3.19,16.42 +ecaresnet50t,288,46.37,21.552,1,25.57,7.14,19.55 +resnet101,160,46.35,21.558,1,44.55,4.0,8.28 +tf_efficientnet_b3,300,46.18,21.641,1,12.23,1.87,23.83 +legacy_xception,299,46.16,21.647,1,22.86,8.4,35.83 +mobilenetv4_conv_aa_large,384,46.14,21.66,1,32.59,7.07,32.29 +swin_tiny_patch4_window7_224,224,46.12,21.667,1,28.29,4.51,17.06 +coatnet_rmlp_nano_rw_224,224,45.97,21.738,1,15.15,2.62,20.34 +vit_medium_patch16_gap_256,256,45.76,21.84,1,38.86,10.59,22.15 +seresnet50,288,45.63,21.898,1,28.09,6.8,18.39 +regnety_064,224,45.62,21.905,1,30.58,6.39,16.41 +fastvit_sa24,256,45.57,21.931,1,21.55,3.8,24.32 +mobileone_s0,224,45.48,21.974,1,5.29,1.09,15.48 +seresnet50t,288,45.48,21.971,1,28.1,7.14,19.55 +efficientvit_l1,224,45.46,21.98,1,52.65,5.27,15.85 +dla102,224,45.32,22.05,1,33.27,7.19,14.18 +mixer_b16_224,224,44.95,22.231,1,59.88,12.62,14.53 +tresnet_m,224,44.93,22.24,1,31.39,5.75,7.31 +convnext_nano,288,44.91,22.252,1,15.59,4.06,13.84 +levit_512d,224,44.82,22.297,1,92.5,5.85,11.3 +tf_mixnet_m,224,44.67,22.372,1,5.01,0.36,8.19 +poolformer_s36,224,44.56,22.427,1,30.86,5.0,15.82 +resnet101,176,44.51,22.453,1,44.55,4.92,10.08 +hiera_small_abswin_256,256,44.37,22.521,1,34.36,8.29,26.38 +efficientformerv2_s2,224,44.2,22.612,1,12.71,1.27,11.77 +levit_512,224,44.15,22.633,1,95.17,5.64,10.22 +ecaresnet50t,320,44.14,22.639,1,25.57,8.82,24.13 +coatnet_rmlp_0_rw_224,224,44.13,22.643,1,27.45,4.72,24.89 +regnetv_064,224,44.1,22.658,1,30.58,6.39,16.41 +nf_regnet_b3,288,44.02,22.703,1,18.59,1.67,11.84 +ese_vovnet39b_evos,224,43.96,22.734,1,24.58,7.07,6.74 +regnetz_d8,256,43.94,22.743,1,23.37,3.97,23.74 +mixnet_m,224,43.88,22.776,1,5.01,0.36,8.19 +maxxvitv2_nano_rw_256,256,43.77,22.829,1,23.7,6.26,23.05 +xcit_tiny_24_p16_224,224,43.51,22.97,1,12.12,2.34,11.82 +inception_v3,299,43.48,22.986,1,23.83,5.73,8.97 +mobilenetv4_conv_large,448,43.42,23.016,1,32.59,8.75,37.17 +cs3sedarknet_l,288,43.36,23.047,1,21.91,6.16,10.83 +mvitv2_tiny,224,43.15,23.162,1,24.17,4.7,21.16 +mobilevitv2_150,384,43.14,23.163,1,10.59,9.2,54.25 +gcresnet50t,256,43.13,23.17,1,25.9,5.42,14.67 +halonet50ts,256,43.09,23.191,1,22.73,5.3,19.2 +convnextv2_tiny,288,42.95,23.27,1,28.64,7.39,22.21 +hieradet_small,256,42.83,23.331,1,34.72,8.51,27.76 +resnext101_32x4d,224,42.71,23.4,1,44.18,8.01,21.23 +darknetaa53,256,42.69,23.407,1,36.02,7.97,12.39 +regnetx_120,224,42.68,23.416,1,46.11,12.13,21.37 +xception41p,299,42.58,23.471,1,26.91,9.25,39.86 +crossvit_18_dagger_240,240,42.54,23.491,1,44.27,9.5,27.03 +densenetblur121d,224,42.38,23.584,1,8.0,3.11,7.9 +cs3sedarknet_xdw,256,42.26,23.645,1,21.6,5.97,17.18 +regnetz_c16_evos,320,41.93,23.833,1,13.49,3.86,25.88 +maxxvit_rmlp_nano_rw_256,256,41.91,23.848,1,16.78,4.37,26.05 +vit_small_r26_s32_224,224,41.84,23.884,1,36.43,3.56,9.85 +nf_regnet_b3,320,41.83,23.893,1,18.59,2.05,14.61 +tf_mixnet_l,224,41.77,23.924,1,7.33,0.58,10.84 +xcit_nano_12_p8_384,384,41.57,24.039,1,3.05,6.34,46.08 +densenet121,224,41.48,24.095,1,7.98,2.87,6.9 +gc_efficientnetv2_rw_t,288,41.42,24.127,1,13.68,3.2,16.45 +dpn92,224,41.37,24.158,1,37.67,6.54,18.21 +coat_lite_small,224,41.08,24.326,1,19.84,3.96,22.09 +efficientnetv2_s,288,41.07,24.333,1,21.46,4.75,20.13 +mobilenetv4_conv_aa_large,448,40.96,24.396,1,32.59,9.63,43.94 +regnetz_040,256,40.92,24.423,1,27.12,4.06,24.19 +ecaresnet101d_pruned,224,40.84,24.468,1,24.88,3.48,7.69 +maxvit_rmlp_nano_rw_256,256,40.78,24.507,1,15.5,4.47,31.92 +regnetz_d8_evos,256,40.78,24.507,1,23.46,4.5,24.92 +efficientnet_b0_gn,224,40.77,24.511,1,5.29,0.42,6.75 +botnet50ts_256,256,40.7,24.554,1,22.74,5.54,22.23 +seresnetaa50d,288,40.59,24.622,1,28.11,8.92,20.59 +regnetz_040_h,256,40.56,24.64,1,28.94,4.12,24.29 +convformer_s18,224,40.53,24.655,1,26.77,3.96,15.82 +cspresnet50w,256,40.48,24.69,1,28.12,5.04,12.19 +convnext_tiny_hnf,224,40.44,24.711,1,28.59,4.47,13.44 +xception41,299,40.35,24.765,1,26.97,9.28,39.86 +gcresnet50t,288,40.34,24.775,1,25.9,6.86,18.57 +focalnet_tiny_srf,224,40.29,24.806,1,28.43,4.42,16.32 +coatnext_nano_rw_224,224,40.21,24.856,1,14.7,2.47,12.8 +gcvit_xtiny,224,40.09,24.927,1,19.98,2.93,20.26 +efficientnet_b3_gn,288,39.94,25.025,1,11.73,1.74,23.35 +mixnet_l,224,39.77,25.13,1,7.33,0.58,10.84 +twins_pcpvt_base,224,39.74,25.15,1,43.83,6.68,25.25 +cs3darknet_x,256,39.65,25.205,1,35.05,8.38,11.35 +darknetaa53,288,39.58,25.247,1,36.02,10.08,15.68 +resmlp_36_224,224,39.56,25.262,1,44.69,8.91,16.33 +efficientnet_b0_g8_gn,224,39.45,25.332,1,6.56,0.66,6.75 +res2net50_14w_8s,224,39.38,25.381,1,25.06,4.21,13.28 +cs3darknet_focus_x,256,39.3,25.428,1,35.02,8.03,10.69 +vit_base_resnet26d_224,224,39.28,25.441,1,101.4,6.97,13.16 +tf_efficientnetv2_s,300,39.24,25.472,1,21.46,5.35,22.73 +resnet50_gn,224,39.23,25.474,1,25.56,4.14,11.11 +levit_conv_512,224,39.13,25.542,1,95.17,5.64,10.22 +hrnet_w18_small_v2,224,39.05,25.592,1,15.6,2.62,9.65 +hiera_base_224,224,38.8,25.755,1,51.52,9.4,30.42 +regnetz_d32,256,38.77,25.776,1,27.58,5.98,23.74 +repvit_m1_5,224,38.77,25.781,1,14.64,2.31,15.7 +ecaresnet101d_pruned,288,38.73,25.803,1,24.88,5.75,12.71 +efficientnetv2_rw_s,288,38.61,25.887,1,23.94,4.91,21.41 +eca_nfnet_l0,224,38.59,25.896,1,24.14,4.35,10.47 +regnety_040_sgn,288,38.56,25.92,1,20.65,6.67,20.3 +convnext_tiny,384,38.48,25.972,1,28.59,13.14,39.48 +darknet53,256,38.45,25.996,1,41.61,9.31,12.39 +resnet101c,224,38.43,26.009,1,44.57,8.08,17.04 +resnet101d,224,38.39,26.031,1,44.57,8.08,17.04 +levit_conv_512d,224,38.29,26.101,1,92.5,5.85,11.3 +efficientnet_b4,320,38.25,26.128,1,19.34,3.13,34.76 +vit_medium_patch16_reg1_gap_256,256,38.21,26.156,1,38.88,10.63,22.26 +cait_xxs36_224,224,38.09,26.236,1,17.3,3.77,30.34 +resnetv2_101,224,37.98,26.313,1,44.54,7.83,16.23 +resnet101,224,37.96,26.325,1,44.55,7.83,16.23 +vit_medium_patch16_reg4_gap_256,256,37.91,26.362,1,38.88,10.76,22.6 +mobilenetv4_conv_aa_large,480,37.86,26.401,1,32.59,11.05,50.45 +vit_base_patch32_384,384,37.85,26.406,1,88.3,13.06,16.5 +resnetv2_50d_gn,224,37.84,26.412,1,25.57,4.38,11.92 +dla102x2,224,37.76,26.469,1,41.28,9.34,29.91 +nf_ecaresnet50,224,37.64,26.55,1,25.56,4.21,11.13 +resnetv2_101d,224,37.5,26.654,1,44.56,8.07,17.04 +regnety_080,288,37.29,26.802,1,39.18,13.22,29.69 +vit_betwixt_patch16_reg4_gap_256,256,37.28,26.811,1,60.4,16.52,28.24 +xcit_tiny_24_p16_384,384,37.27,26.819,1,12.12,6.87,34.29 +resnet101s,224,37.22,26.85,1,44.67,9.19,18.64 +resnet101_clip_gap,224,37.2,26.866,1,42.52,9.11,17.56 +vit_betwixt_patch16_reg1_gap_256,256,37.17,26.887,1,60.4,16.32,27.83 +regnety_120,224,37.07,26.958,1,51.82,12.14,21.38 +resnetaa101d,224,37.03,26.988,1,44.57,9.12,17.56 +vit_base_patch32_clip_384,384,36.96,27.038,1,88.3,13.06,16.5 +focalnet_tiny_lrf,224,36.82,27.146,1,28.65,4.49,17.76 +regnety_064,288,36.7,27.234,1,30.58,10.56,27.11 +resnetblur101d,224,36.7,27.233,1,44.57,9.12,17.94 +swinv2_tiny_window8_256,256,36.67,27.255,1,28.35,5.96,24.57 +efficientvit_b3,256,36.65,27.272,1,48.65,5.2,35.01 +vit_small_resnet50d_s16_224,224,36.62,27.293,1,57.53,13.48,24.82 +convnextv2_nano,288,36.54,27.35,1,15.62,4.06,13.84 +hgnetv2_b5,224,36.31,27.528,1,39.57,6.56,11.19 +nfnet_l0,224,36.3,27.529,1,35.07,4.36,10.47 +efficientnet_b3_g8_gn,288,36.2,27.606,1,14.25,2.59,23.35 +res2net50_26w_6s,224,36.18,27.623,1,37.05,6.33,15.28 +efficientvit_b3,224,36.11,27.679,1,48.65,3.99,26.9 +resnet50_gn,288,36.07,27.705,1,25.56,6.85,18.37 +mobilevitv2_175,384,36.06,27.718,1,14.25,12.47,63.29 +resnetv2_50d_evos,224,36.01,27.757,1,25.59,4.33,11.92 +vit_medium_patch16_rope_reg1_gap_256,256,35.85,27.875,1,38.74,10.63,22.26 +selecsls60,224,35.65,28.033,1,30.67,3.59,5.52 +darknet53,288,35.59,28.085,1,41.61,11.78,15.68 +vit_betwixt_patch16_rope_reg4_gap_256,256,35.51,28.145,1,60.23,16.52,28.24 +nf_seresnet50,224,35.24,28.358,1,28.09,4.21,11.13 +resnet101d,256,35.24,28.36,1,44.57,10.55,22.25 +regnetx_160,224,35.17,28.416,1,54.28,15.99,25.52 +efficientnet_b3_gn,320,35.01,28.544,1,11.73,2.14,28.83 +vit_base_patch16_xp_224,224,34.85,28.679,1,86.51,17.56,23.9 +cs3darknet_x,288,34.83,28.698,1,35.05,10.6,14.36 +efficientvit_l2,224,34.79,28.728,1,63.71,6.97,19.58 +resnext101_32x4d,288,34.76,28.749,1,44.18,13.24,35.09 +efficientvit_l2,256,34.74,28.768,1,63.71,9.09,25.49 +convnext_small,224,34.73,28.78,1,50.22,8.71,21.56 +ecaresnet101d,224,34.73,28.782,1,44.57,8.08,17.07 +nextvit_base,224,34.7,28.806,1,44.82,8.29,23.71 +densenet121,288,34.67,28.829,1,7.98,4.74,11.41 +regnetv_064,288,34.58,28.905,1,30.58,10.55,27.11 +seresnext101_32x4d,224,34.52,28.956,1,48.96,8.02,21.26 +nf_resnet50,256,34.49,28.98,1,25.56,5.46,14.52 +resnetrs101,192,34.47,28.999,1,63.62,6.04,12.7 +legacy_seresnext101_32x4d,224,34.44,29.022,1,48.96,8.02,21.26 +maxvit_tiny_rw_224,224,34.35,29.1,1,29.06,5.11,33.11 +repvgg_b1g4,224,34.32,29.124,1,39.97,8.15,10.64 +vit_base_patch16_gap_224,224,34.16,29.258,1,86.57,17.49,25.59 +efficientvit_b3,288,34.1,29.309,1,48.65,6.58,44.2 +halo2botnet50ts_256,256,34.05,29.351,1,22.64,5.02,21.78 +selecsls42,224,33.89,29.496,1,30.35,2.94,4.62 +eca_nfnet_l0,288,33.8,29.569,1,24.14,7.12,17.29 +vit_base_patch16_siglip_gap_224,224,33.75,29.618,1,85.8,17.49,23.75 +resnet101_clip,224,33.7,29.656,1,56.26,9.81,18.08 +resnet152,160,33.49,29.847,1,60.19,5.9,11.51 +deit3_small_patch16_384,384,33.42,29.906,1,22.21,15.52,50.78 +mobilenetv4_hybrid_large,384,33.38,29.94,1,37.76,7.77,34.52 +regnetz_040_h,320,33.3,30.017,1,28.94,6.43,37.94 +xcit_tiny_24_p8_224,224,33.26,30.048,1,12.11,9.21,45.39 +densenetblur121d,288,33.25,30.063,1,8.0,5.14,13.06 +densenet169,224,33.23,30.075,1,14.15,3.4,7.3 +resnet101,288,33.13,30.169,1,44.55,12.95,26.83 +twins_svt_base,224,33.13,30.173,1,56.07,8.59,26.33 +deit3_base_patch16_224,224,33.11,30.189,1,86.59,17.58,23.9 +legacy_seresnet101,224,33.08,30.217,1,49.33,7.61,15.74 +regnetz_040,320,32.88,30.4,1,27.12,6.35,37.78 +resnetv2_50x1_bit,224,32.87,30.406,1,25.55,4.23,11.11 +vit_base_resnet50d_224,224,32.87,30.408,1,110.97,8.73,16.92 +vit_base_patch16_rpn_224,224,32.84,30.438,1,86.54,17.49,23.75 +poolformerv2_m36,224,32.8,30.47,1,56.08,8.81,22.02 +tf_efficientnetv2_s,384,32.72,30.552,1,21.46,8.44,35.77 +mobilenetv4_conv_aa_large,544,32.67,30.598,1,32.59,14.19,64.79 +vit_base_patch16_siglip_224,224,32.65,30.616,1,92.88,17.73,24.06 +mixnet_xl,224,32.6,30.656,1,11.9,0.93,14.57 +selecsls60b,224,32.6,30.659,1,32.77,3.63,5.52 +mvitv2_small,224,32.58,30.68,1,34.87,7.0,28.08 +vit_small_patch16_384,384,32.55,30.711,1,22.2,15.52,50.78 +caformer_s36,224,32.53,30.726,1,39.3,8.0,37.53 +tnt_b_patch16_224,224,32.53,30.728,1,65.41,14.09,39.01 +vitamin_small_224,224,32.51,30.744,1,22.03,5.92,26.38 +nf_resnet50,288,32.5,30.752,1,25.56,6.88,18.37 +hgnetv2_b5,288,32.49,30.76,1,39.57,10.84,18.5 +deit_base_distilled_patch16_224,224,32.46,30.788,1,87.34,17.68,24.05 +vit_base_patch16_clip_quickgelu_224,224,32.46,30.79,1,86.19,17.58,23.9 +vit_base_patch16_224,224,32.37,30.879,1,86.57,17.58,23.9 +convnextv2_tiny,384,32.27,30.975,1,28.64,13.14,39.48 +cs3sedarknet_x,256,32.26,30.981,1,35.4,8.38,11.35 +hiera_base_plus_224,224,32.24,31.003,1,69.9,12.67,37.98 +seresnet101,224,32.23,31.014,1,49.33,7.84,16.27 +poolformer_m36,224,32.19,31.046,1,56.17,8.8,22.02 +nest_small,224,32.07,31.17,1,38.35,10.35,40.04 +vit_base_patch16_clip_224,224,32.07,31.165,1,86.57,17.58,23.9 +convnextv2_small,224,32.06,31.176,1,50.32,8.71,21.56 +efficientnetv2_s,384,32.05,31.19,1,21.46,8.44,35.77 +beitv2_base_patch16_224,224,31.94,31.292,1,86.53,17.58,23.9 +pit_b_224,224,31.91,31.326,1,73.76,12.42,32.94 +resnet152,176,31.85,31.385,1,60.19,7.22,13.99 +deit_base_patch16_224,224,31.84,31.396,1,86.57,17.58,23.9 +xcit_small_12_p16_384,384,31.81,31.426,1,26.25,14.14,36.51 +vit_base_patch16_224_miil,224,31.79,31.443,1,94.4,17.59,23.91 +efficientvit_l2,288,31.76,31.475,1,63.71,11.51,32.19 +cspdarknet53,256,31.74,31.496,1,27.64,6.57,16.81 +fastvit_sa36,256,31.72,31.513,1,31.53,5.64,34.61 +efficientformerv2_l,224,31.66,31.575,1,26.32,2.59,18.54 +cs3edgenet_x,256,31.65,31.583,1,47.82,11.53,12.92 +vit_base_mci_224,224,31.6,31.632,1,86.35,17.73,24.65 +efficientnet_b4,384,31.56,31.668,1,19.34,4.51,50.04 +nest_small_jx,224,31.45,31.779,1,38.35,10.35,40.04 +vit_base_patch32_clip_448,448,31.42,31.814,1,88.34,17.93,23.9 +beit_base_patch16_224,224,31.4,31.831,1,86.53,17.58,23.9 +nf_regnet_b4,320,31.39,31.841,1,30.21,3.29,19.88 +efficientnet_b3_g8_gn,320,31.38,31.853,1,14.25,3.2,28.83 +dpn98,224,31.36,31.872,1,61.57,11.73,25.2 +nfnet_l0,288,31.34,31.893,1,35.07,7.13,17.29 +selecsls42b,224,31.3,31.933,1,32.46,2.98,4.62 +vit_relpos_base_patch16_224,224,31.26,31.976,1,86.43,17.51,24.97 +efficientformer_l7,224,31.23,32.009,1,82.23,10.17,24.45 +vit_relpos_base_patch16_rpn_224,224,31.19,32.049,1,86.41,17.51,24.97 +efficientnetv2_rw_s,384,31.17,32.062,1,23.94,8.72,38.03 +regnetz_d8,320,31.14,32.1,1,23.37,6.19,37.08 +resnetaa101d,288,31.04,32.202,1,44.57,15.07,29.03 +repvit_m2_3,224,30.84,32.413,1,23.69,4.57,26.21 +hiera_base_abswin_256,256,30.8,32.449,1,51.27,12.46,40.7 +pit_b_distilled_224,224,30.65,32.611,1,74.79,12.5,33.07 +regnetz_d8_evos,320,30.64,32.621,1,23.46,7.03,38.92 +resnetv2_50d_gn,288,30.59,32.672,1,25.57,7.24,19.7 +tf_efficientnet_b4,380,30.51,32.765,1,19.34,4.49,49.49 +inception_next_small,224,30.42,32.862,1,49.37,8.36,19.27 +pvt_v2_b2,224,30.36,32.925,1,25.36,4.05,27.53 +eva02_small_patch14_336,336,30.29,33.0,1,22.13,15.48,54.33 +resnetblur101d,288,30.04,33.271,1,44.57,15.07,29.65 +vit_base_r26_s32_224,224,30.0,33.321,1,101.38,6.81,12.36 +resnext101_32x8d,176,29.83,33.513,1,88.79,10.33,19.37 +repvgg_b1,224,29.67,33.688,1,57.42,13.16,10.64 +resnetv2_50d_evos,288,29.65,33.713,1,25.59,7.15,19.7 +tresnet_v2_l,224,29.6,33.773,1,46.17,8.85,16.34 +gcvit_tiny,224,29.49,33.891,1,28.22,4.79,29.82 +resnet101d,320,29.39,34.011,1,44.57,16.48,34.77 +flexivit_base,240,29.33,34.08,1,86.59,20.29,28.36 +regnetz_d32,320,29.32,34.088,1,27.58,9.33,37.08 +resnetv2_101,288,29.31,34.106,1,44.54,12.94,26.83 +mobilenetv4_hybrid_large,448,29.27,34.151,1,37.76,10.74,48.61 +ecaresnet101d,288,29.15,34.285,1,44.57,13.35,28.19 +samvit_base_patch16_224,224,29.14,34.296,1,86.46,17.54,24.54 +res2net50_26w_8s,224,29.07,34.384,1,48.4,8.37,17.95 +seresnext101_32x4d,288,29.02,34.449,1,48.96,13.25,35.12 +resnet152c,224,29.01,34.452,1,60.21,11.8,23.36 +coatnet_1_rw_224,224,28.9,34.585,1,41.72,8.04,34.6 +fastvit_ma36,256,28.73,34.79,1,44.07,7.88,41.09 +res2net101_26w_4s,224,28.72,34.8,1,45.21,8.1,18.45 +xcit_small_24_p16_224,224,28.72,34.804,1,47.67,9.1,23.64 +tresnet_l,224,28.63,34.917,1,55.99,10.9,11.9 +fastvit_mci1,256,28.61,34.943,1,21.54,4.72,32.84 +nf_regnet_b4,384,28.57,34.992,1,30.21,4.7,28.61 +mvitv2_small_cls,224,28.51,35.066,1,34.87,7.04,28.17 +dla169,224,28.48,35.101,1,53.39,11.6,20.2 +ese_vovnet99b,224,28.47,35.103,1,63.2,16.51,11.27 +cs3sedarknet_x,288,28.29,35.337,1,35.4,10.6,14.37 +xception65p,299,28.23,35.414,1,39.82,13.91,52.48 +cs3edgenet_x,288,28.14,35.519,1,47.82,14.59,16.36 +res2net101d,224,28.14,35.527,1,45.23,8.35,19.25 +xception65,299,28.1,35.573,1,39.92,13.96,52.48 +twins_pcpvt_large,224,28.07,35.609,1,60.99,9.84,35.82 +resnet152,224,27.96,35.749,1,60.19,11.56,22.56 +vit_base_patch16_siglip_gap_256,256,27.94,35.78,1,85.84,23.13,33.23 +regnety_120,288,27.92,35.806,1,51.82,20.06,35.34 +volo_d2_224,224,27.78,35.98,1,58.68,14.34,41.34 +cait_s24_224,224,27.64,36.167,1,46.92,9.35,40.58 +regnetz_e8,256,27.54,36.301,1,57.7,9.91,40.94 +coatnet_rmlp_1_rw_224,224,27.53,36.312,1,41.69,7.85,35.47 +resnet152d,224,27.31,36.603,1,60.21,11.8,23.36 +resnetv2_152,224,27.25,36.685,1,60.19,11.55,22.56 +cs3se_edgenet_x,256,27.22,36.716,1,50.72,11.53,12.94 +regnety_160,224,27.12,36.857,1,83.59,15.96,23.04 +resnet152d,256,27.11,36.874,1,60.21,15.41,30.51 +seresnet101,288,27.02,36.999,1,49.33,12.95,26.87 +resnet152s,224,26.88,37.185,1,60.32,12.92,24.96 +vit_small_patch16_36x1_224,224,26.68,37.464,1,64.67,13.71,35.69 +resnetv2_152d,224,26.66,37.494,1,60.2,11.8,23.36 +resnest101e,256,26.65,37.508,1,48.28,13.38,28.66 +vit_base_patch16_siglip_256,256,26.57,37.619,1,92.93,23.44,33.63 +coatnet_rmlp_1_rw2_224,224,26.54,37.661,1,41.72,8.11,40.13 +resnext101_64x4d,224,26.53,37.681,1,83.46,15.52,31.21 +davit_small,224,26.46,37.776,1,49.75,8.8,30.49 +eva02_base_patch16_clip_224,224,26.43,37.819,1,86.26,17.62,26.32 +convnext_tiny_hnf,288,26.37,37.908,1,28.59,7.39,22.21 +vit_small_patch16_18x2_224,224,26.34,37.946,1,64.67,13.71,35.69 +xcit_tiny_12_p8_384,384,26.34,37.947,1,6.71,14.13,69.14 +swinv2_cr_small_224,224,26.28,38.042,1,49.7,9.07,50.27 +levit_384_s8,224,26.26,38.061,1,39.12,9.98,35.86 +convmixer_768_32,224,26.25,38.075,1,21.11,19.55,25.95 +nextvit_large,224,26.22,38.123,1,57.87,10.78,28.99 +coatnet_1_224,224,26.15,38.229,1,42.23,8.7,39.0 +fastvit_mci2,256,26.12,38.263,1,35.82,7.91,43.34 +tresnet_m,448,26.11,38.285,1,31.39,22.99,29.21 +resnetrs101,288,25.92,38.57,1,63.62,13.56,28.53 +gmlp_b16_224,224,25.89,38.602,1,73.08,15.78,30.21 +vit_base_patch16_rope_reg1_gap_256,256,25.73,38.851,1,86.43,23.22,33.39 +vit_small_r26_s32_384,384,25.73,38.852,1,36.47,10.43,29.85 +maxxvit_rmlp_tiny_rw_256,256,25.67,38.943,1,29.64,6.66,39.76 +poolformerv2_m48,224,25.65,38.971,1,73.35,11.59,29.17 +swinv2_tiny_window16_256,256,25.64,38.993,1,28.35,6.68,39.02 +mixnet_xxl,224,25.63,38.998,1,23.96,2.04,23.43 +vit_base_patch16_reg4_gap_256,256,25.52,39.177,1,86.62,23.5,33.89 +densenet201,224,25.51,39.186,1,20.01,4.34,7.85 +inception_v4,299,25.47,39.242,1,42.68,12.28,15.09 +swinv2_cr_small_ns_224,224,25.44,39.297,1,49.7,9.08,50.27 +xception71,299,25.35,39.425,1,42.34,18.09,69.92 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+tf_efficientnet_l2,475,1.68,594.956,1,480.31,172.11,609.89 +vitamin_xlarge_336,336,1.67,597.981,1,436.06,230.18,347.33 +vit_huge_patch14_clip_336,336,1.65,604.321,1,632.46,390.97,407.54 +vitamin_large2_384,384,1.6,626.786,1,333.97,234.44,440.16 +maxvit_xlarge_tf_384,384,1.59,627.883,1,475.32,292.78,668.76 +vitamin_large_384,384,1.59,629.157,1,333.71,234.44,440.16 +regnety_1280,384,1.58,633.28,1,644.81,374.99,210.2 +resnetv2_101x3_bit,448,1.57,635.087,1,387.93,280.33,194.78 +vit_so400m_patch14_siglip_gap_448,448,1.52,659.739,1,413.33,487.18,764.26 +volo_d5_512,512,1.52,658.657,1,296.09,425.09,1105.37 +vit_large_patch14_dinov2,518,1.5,668.176,1,304.37,507.15,1058.82 +dm_nfnet_f6,576,1.49,670.137,1,438.36,378.69,452.2 +nfnet_f6,576,1.48,676.503,1,438.36,378.69,452.2 +vit_large_patch14_reg4_dinov2,518,1.48,674.657,1,304.37,508.9,1064.02 +beit_large_patch16_512,512,1.43,700.682,1,305.67,362.24,656.39 +vit_huge_patch14_clip_378,378,1.34,745.367,1,632.68,503.79,572.79 +vit_huge_patch14_clip_quickgelu_378,378,1.34,744.209,1,632.68,503.79,572.79 +vit_gigantic_patch14_clip_224,224,1.26,792.869,1,1844.91,483.96,275.37 +vit_huge_patch16_gap_448,448,1.26,792.181,1,631.67,544.7,636.83 +swinv2_cr_giant_224,224,1.25,799.001,1,2598.76,483.85,309.15 +swinv2_cr_huge_384,384,1.25,802.044,1,657.94,352.04,583.18 +vit_gigantic_patch14_224,224,1.25,797.556,1,1844.44,483.95,275.37 +nfnet_f7,608,1.21,825.686,1,499.5,480.39,570.85 +resnetv2_152x4_bit,224,1.15,870.095,1,936.53,186.9,90.22 +convnextv2_huge,512,1.11,898.321,1,660.29,600.81,413.07 +eva_giant_patch14_336,336,1.07,931.905,1,1013.01,620.64,550.67 +davit_huge_fl,768,0.82,1222.73,1,360.64,744.84,1060.3 +maxvit_xlarge_tf_512,512,0.82,1224.526,1,475.77,534.14,1413.22 +samvit_base_patch16,1024,0.8,1252.765,1,89.67,486.43,1343.27 +regnety_2560,384,0.72,1397.042,1,1282.6,747.83,296.49 +cait_m48_448,448,0.7,1431.241,1,356.46,329.41,1708.23 +sam2_hiera_large,1024,0.7,1429.016,1,212.15,907.48,2190.34 +efficientnet_l2,800,0.59,1701.073,1,480.31,479.12,1707.39 +tf_efficientnet_l2,800,0.59,1687.246,1,480.31,479.12,1707.39 +resnetv2_152x4_bit,480,0.56,1780.62,1,936.53,844.84,414.26 +eva_giant_patch14_560,560,0.47,2137.135,1,1014.45,1906.76,2577.17 +vit_giant_patch14_dinov2,518,0.46,2152.578,1,1136.48,1784.2,2757.89 +vit_giant_patch14_reg4_dinov2,518,0.46,2172.897,1,1136.48,1790.08,2771.21 +eva02_enormous_patch14_clip_224,224,0.45,2211.904,1,4350.56,1132.46,497.58 +swinv2_cr_giant_384,384,0.37,2667.378,1,2598.76,1450.71,1394.86 +samvit_large_patch16,1024,0.33,3039.952,1,308.28,1493.86,2553.78 +vit_so400m_patch14_siglip_gap_896,896,0.28,3633.38,1,416.87,2731.49,8492.88 +samvit_huge_patch16,1024,0.22,4556.317,1,637.03,2982.23,3428.16 diff --git a/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv b/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..de3025b15c6f36acbc140a4b3ca0e28851e3afb0 --- /dev/null +++ b/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv @@ -0,0 +1,839 @@ +model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count +tinynet_e,10001.12,50.423,512,106,2.04 +mobilenetv3_small_050,7406.47,68.392,512,224,1.59 +tf_mobilenetv3_small_minimal_100,6438.14,78.983,512,224,2.04 +mobilenetv3_small_075,6186.83,82.006,512,224,2.04 +tf_mobilenetv3_small_075,5783.46,87.782,512,224,2.04 +mobilenetv3_small_100,5749.13,88.315,512,224,2.54 +lcnet_035,5673.53,89.75,512,224,1.64 +tf_mobilenetv3_small_100,5383.9,94.36,512,224,2.54 +levit_128s,5298.88,95.701,512,224,7.78 +lcnet_050,5280.37,96.452,512,224,1.88 +tinynet_d,5161.83,98.416,512,152,2.34 +mixer_s32_224,4696.33,108.475,512,224,19.1 +resnet10t,4669.46,109.393,512,176,5.44 +vit_small_patch32_224,4447.28,114.289,512,224,22.88 +lcnet_075,4278.23,119.175,512,224,2.36 +vit_tiny_r_s16_p8_224,4137.87,122.895,512,224,6.34 +levit_128,3895.0,130.318,512,224,9.21 +regnetx_002,3718.05,137.026,512,224,2.68 +lcnet_100,3569.0,142.969,512,224,2.95 +mnasnet_small,3450.28,147.453,512,224,2.03 +regnety_002,3414.18,149.006,512,224,3.16 +cs3darknet_focus_s,3251.91,156.949,512,256,3.27 +mobilenetv2_035,3160.04,161.202,512,224,1.68 +levit_192,3046.5,166.9,512,224,10.95 +gernet_s,3034.31,168.028,512,224,8.17 +tinynet_c,2919.98,174.314,512,184,2.46 +mnasnet_050,2847.14,179.025,512,224,2.22 +cs3darknet_s,2821.27,180.951,512,256,3.28 +resnet18,2764.22,184.877,512,224,11.69 +ssl_resnet18,2760.71,185.109,512,224,11.69 +mobilenetv2_050,2751.58,185.257,512,224,1.97 +swsl_resnet18,2742.47,186.338,512,224,11.69 +semnasnet_050,2741.67,185.816,512,224,2.08 +gluon_resnet18_v1b,2741.53,186.395,512,224,11.69 +lcnet_150,2713.5,188.193,512,224,4.5 +regnetx_004,2695.23,188.875,512,224,5.16 +ese_vovnet19b_slim_dw,2588.37,197.313,512,224,1.9 +seresnet18,2562.51,199.293,512,224,11.78 +nf_regnet_b0,2561.76,198.646,512,192,8.76 +legacy_seresnet18,2500.8,204.207,512,224,11.78 +tf_efficientnetv2_b0,2483.22,204.949,512,192,7.14 +levit_256,2482.39,205.091,512,224,18.89 +mobilenetv3_large_075,2392.41,213.119,512,224,3.99 +resnet14t,2385.69,214.281,512,176,10.08 +tf_mobilenetv3_large_minimal_100,2347.68,217.368,512,224,3.92 +vit_tiny_patch16_224,2293.54,222.408,512,224,5.72 +regnetx_006,2293.09,222.433,512,224,6.2 +deit_tiny_patch16_224,2290.53,222.68,512,224,5.72 +tf_mobilenetv3_large_075,2259.6,225.688,512,224,3.99 +deit_tiny_distilled_patch16_224,2253.36,226.358,512,224,5.91 +edgenext_xx_small,2231.33,228.598,512,256,1.33 +ghostnet_050,2189.91,232.414,512,224,2.59 +mobilenetv3_rw,2184.31,233.512,512,224,5.48 +mnasnet_075,2176.02,234.492,512,224,3.17 +mobilenetv3_large_100,2167.29,235.344,512,224,5.48 +mobilenetv3_large_100_miil,2165.63,235.504,512,224,5.48 +levit_256d,2159.5,235.516,512,224,26.21 +resnet18d,2129.12,240.084,512,224,11.71 +hardcorenas_a,2118.32,240.968,512,224,5.26 +regnety_004,2100.99,242.536,512,224,4.34 +pit_ti_distilled_224,2086.5,244.504,512,224,5.1 +pit_ti_224,2079.54,245.311,512,224,4.85 +ese_vovnet19b_slim,2066.1,247.446,512,224,3.17 +mnasnet_100,2053.84,248.477,512,224,4.38 +tf_mobilenetv3_large_100,2053.63,248.437,512,224,5.48 +mnasnet_b1,2053.54,248.485,512,224,4.38 +semnasnet_075,2008.51,253.986,512,224,2.91 +hardcorenas_b,2008.46,253.96,512,224,5.18 +mobilenetv2_075,1983.69,257.32,512,224,2.64 +hardcorenas_c,1977.37,257.94,512,224,5.52 +xcit_nano_12_p16_224_dist,1970.62,258.036,512,224,3.05 +xcit_nano_12_p16_224,1969.78,258.084,512,224,3.05 +tinynet_b,1965.95,259.368,512,188,3.73 +hardcorenas_d,1880.3,271.085,512,224,7.5 +tf_efficientnetv2_b1,1876.23,271.395,512,192,8.14 +resnetblur18,1872.21,273.11,512,224,11.69 +spnasnet_100,1862.13,273.955,512,224,4.42 +mnasnet_a1,1859.21,274.476,512,224,3.89 +semnasnet_100,1857.75,274.693,512,224,3.89 +mobilenetv2_100,1832.14,278.633,512,224,3.5 +regnety_006,1809.24,281.912,512,224,6.06 +visformer_tiny,1802.41,283.384,512,224,10.32 +mixer_b32_224,1784.58,286.101,512,224,60.29 +skresnet18,1730.13,295.275,512,224,11.96 +tinynet_a,1710.13,298.117,512,192,6.19 +vit_base_patch32_224_sam,1703.64,299.668,512,224,88.22 +vit_base_patch32_224,1703.57,299.695,512,224,88.22 +efficientnet_lite0,1674.68,304.971,512,224,4.65 +cs3darknet_focus_m,1668.48,306.209,512,256,9.3 +hardcorenas_e,1650.74,309.021,512,224,8.07 +hardcorenas_f,1646.88,309.777,512,224,8.2 +gluon_resnet34_v1b,1634.03,312.731,512,224,21.8 +regnetx_008,1632.2,312.851,512,224,7.26 +tv_resnet34,1630.02,313.513,512,224,21.8 +resnet34,1622.41,314.992,512,224,21.8 +ghostnet_100,1601.5,318.319,512,224,5.18 +tf_efficientnet_lite0,1591.79,320.884,512,224,4.65 +fbnetc_100,1567.77,325.605,512,224,5.57 +pit_xs_distilled_224,1551.83,329.02,512,224,11.0 +pit_xs_224,1549.02,329.642,512,224,10.62 +mixer_s16_224,1543.23,331.197,512,224,18.53 +dla46_c,1532.94,333.18,512,224,1.3 +mnasnet_140,1525.17,334.879,512,224,7.12 +seresnet34,1505.77,339.147,512,224,21.96 +cs3darknet_m,1499.82,340.716,512,256,9.31 +regnety_008,1498.63,340.596,512,224,6.26 +levit_384,1491.26,342.207,512,224,39.13 +edgenext_x_small,1481.71,344.446,512,256,2.34 +ese_vovnet19b_dw,1466.46,348.623,512,224,6.54 +legacy_seresnet34,1465.81,348.38,512,224,21.96 +efficientnet_b0,1459.11,262.1,384,224,5.29 +gernet_m,1456.76,350.74,512,224,21.14 +vit_small_patch32_384,1448.56,352.604,512,384,22.92 +regnetz_005,1448.06,352.165,512,224,7.12 +rexnet_100,1447.81,264.049,384,224,4.8 +rexnetr_100,1441.71,265.216,384,224,4.88 +nf_resnet26,1422.76,359.346,512,224,16.0 +hrnet_w18_small,1410.43,361.614,512,224,13.19 +selecsls42,1405.04,363.736,512,224,30.35 +selecsls42b,1401.22,364.735,512,224,32.46 +mobilenetv2_110d,1400.15,273.199,384,224,4.52 +tf_efficientnet_b0_ap,1398.67,273.43,384,224,5.29 +mobilevitv2_050,1396.45,365.664,512,256,1.37 +tf_efficientnet_b0_ns,1395.54,274.064,384,224,5.29 +tf_efficientnet_b0,1395.32,274.114,384,224,5.29 +tf_efficientnetv2_b2,1392.9,365.948,512,208,10.1 +vit_tiny_r_s16_p8_384,1392.75,274.873,384,384,6.36 +resnet34d,1379.64,370.514,512,224,21.82 +ghostnet_130,1364.55,373.824,512,224,7.36 +gmixer_12_224,1352.72,377.701,512,224,12.7 +crossvit_tiny_240,1349.19,377.902,512,240,7.01 +gmlp_ti16_224,1340.6,284.894,384,224,5.87 +semnasnet_140,1340.57,380.992,512,224,6.11 +dla46x_c,1338.33,381.81,512,224,1.07 +xcit_tiny_12_p16_224,1323.84,384.926,512,224,6.72 +xcit_tiny_12_p16_224_dist,1317.19,386.895,512,224,6.72 +resnetrs50,1317.01,387.565,512,160,35.69 +mobilevit_xxs,1316.84,290.489,384,256,1.27 +resnet26,1312.7,389.566,512,224,16.0 +efficientnet_b1_pruned,1301.95,391.798,512,240,6.33 +mobilenetv2_140,1267.4,302.189,384,224,6.11 +dla60x_c,1262.98,404.404,512,224,1.32 +crossvit_9_240,1260.08,303.33,384,240,8.55 +convnext_nano_hnf,1235.34,413.703,512,224,15.59 +convnext_nano_ols,1234.94,413.902,512,224,15.6 +poolformer_s12,1234.11,414.201,512,224,11.92 +convnext_nano,1233.61,414.261,512,224,15.59 +resmlp_12_distilled_224,1232.37,414.645,512,224,15.35 +resmlp_12_224,1232.04,414.762,512,224,15.35 +fbnetv3_b,1226.89,415.617,512,224,8.6 +nf_regnet_b2,1219.45,418.235,512,240,14.31 +repvgg_b0,1217.24,419.512,512,224,15.82 +selecsls60b,1214.07,420.825,512,224,32.77 +selecsls60,1211.7,421.663,512,224,30.67 +nf_regnet_b1,1209.03,421.975,512,256,10.22 +crossvit_9_dagger_240,1206.16,316.906,384,240,8.78 +nf_seresnet26,1198.39,426.558,512,224,17.4 +mixnet_s,1181.75,431.958,512,224,4.13 +nf_ecaresnet26,1174.85,435.233,512,224,16.0 +efficientnet_lite1,1171.46,217.556,256,240,5.42 +darknet17,1164.06,439.537,512,256,14.3 +efficientnet_es_pruned,1160.76,440.317,512,224,5.44 +efficientnet_es,1160.37,440.47,512,224,5.44 +regnetx_016,1139.3,448.473,512,224,9.19 +fbnetv3_d,1138.14,335.598,384,224,10.31 +tf_efficientnet_es,1136.29,449.83,512,224,5.44 +rexnetr_130,1133.04,224.76,256,224,7.61 +dla34,1132.96,451.315,512,224,15.74 +resnet26d,1119.56,456.822,512,224,16.01 +tf_mixnet_s,1118.11,456.605,512,224,4.13 +tf_efficientnet_lite1,1110.94,229.444,256,240,5.42 +edgenext_small,1109.37,460.388,512,256,5.59 +convit_tiny,1095.04,466.531,512,224,5.71 +rexnet_130,1094.78,232.699,256,224,7.56 +mobilenetv2_120d,1078.49,236.158,256,224,5.83 +darknet21,1073.87,476.43,512,256,20.86 +ecaresnet50d_pruned,1067.01,478.899,512,224,19.94 +deit_small_patch16_224,1053.64,363.563,384,224,22.05 +vit_small_patch16_224,1052.92,363.872,384,224,22.05 +deit_small_distilled_patch16_224,1032.61,370.971,384,224,22.44 +sedarknet21,1031.46,495.893,512,256,20.95 +gernet_l,1030.31,496.058,512,256,31.08 +efficientnet_b1,1030.3,246.963,256,224,7.79 +rexnetr_150,1022.06,249.288,256,224,9.78 +repvgg_a2,1010.18,506.008,512,224,28.21 +edgenext_small_rw,1009.52,506.183,512,256,7.83 +skresnet34,1008.96,506.323,512,224,22.28 +resnest14d,979.06,522.497,512,224,10.61 +cs3darknet_focus_l,977.57,391.957,384,256,21.15 +deit3_small_patch16_224,977.26,391.961,384,224,22.06 +deit3_small_patch16_224_in21ft1k,976.5,392.276,384,224,22.06 +rexnet_150,965.2,264.04,256,224,9.73 +regnety_016,954.26,534.657,512,224,11.2 +vit_base_patch32_plus_256,951.64,537.091,512,256,119.48 +mobilevitv2_075,947.54,269.157,256,256,2.87 +legacy_seresnext26_32x4d,946.21,405.17,384,224,16.79 +pit_s_224,942.8,270.615,256,224,23.46 +pit_s_distilled_224,939.97,271.455,256,224,24.04 +vit_srelpos_small_patch16_224,922.29,415.451,384,224,21.97 +vit_relpos_small_patch16_224,921.7,415.439,384,224,21.98 +efficientnet_b0_g16_evos,909.42,421.149,384,224,8.11 +resnext26ts,905.09,423.733,384,256,10.3 +cs3darknet_l,902.18,282.891,256,256,21.16 +coat_lite_tiny,893.97,428.624,384,224,5.72 +resnet26t,890.89,574.188,512,256,16.01 +efficientnet_b0_gn,881.54,289.263,256,224,5.29 +resnetv2_50,880.1,580.976,512,224,25.55 +efficientnet_b2_pruned,874.13,291.317,256,260,8.31 +seresnext26ts,867.62,294.407,256,256,10.39 +eca_resnext26ts,867.54,294.527,256,256,10.3 +tf_efficientnet_b1,863.78,294.816,256,240,7.79 +tf_efficientnet_b1_ap,863.54,294.906,256,240,7.79 +tf_efficientnet_b1_ns,863.39,294.941,256,240,7.79 +cs3sedarknet_l,861.38,444.523,384,256,21.91 +tf_efficientnetv2_b3,855.0,297.539,256,240,14.36 +efficientnet_lite2,852.1,299.402,256,260,6.09 +twins_svt_small,851.73,449.18,384,224,24.06 +gcresnext26ts,850.58,300.113,256,256,10.48 +efficientnetv2_rw_t,850.16,298.981,256,224,13.65 +botnet26t_256,849.43,451.465,384,256,12.49 +ecaresnetlight,846.78,603.683,512,224,30.16 +seresnext26t_32x4d,845.6,453.458,384,224,16.81 +seresnext26tn_32x4d,845.31,453.612,384,224,16.81 +seresnext26d_32x4d,844.96,453.775,384,224,16.81 +coat_lite_mini,842.06,455.115,384,224,11.01 +tf_efficientnet_cc_b0_8e,837.03,457.594,384,224,24.01 +ecaresnet101d_pruned,837.02,609.921,512,224,24.88 +ecaresnext26t_32x4d,835.25,459.196,384,224,15.41 +ecaresnext50t_32x4d,834.39,459.653,384,224,15.41 +cspresnet50,830.57,461.498,384,256,21.62 +swsl_resnet50,829.79,616.192,512,224,25.56 +ssl_resnet50,829.64,616.294,512,224,25.56 +gluon_resnet50_v1b,829.63,616.32,512,224,25.56 +visformer_small,828.8,462.625,384,224,40.22 +tv_resnet50,826.55,618.618,512,224,25.56 +resnet50,826.06,618.983,512,224,25.56 +vgg11,825.98,619.706,512,224,132.86 +halonet26t,824.96,464.902,384,256,12.48 +vovnet39a,817.65,625.544,512,224,22.6 +tf_efficientnet_lite2,816.76,312.458,256,260,6.09 +convnext_tiny_hnf,815.48,312.97,256,224,28.59 +convnext_tiny_hnfd,815.19,313.078,256,224,28.59 +vit_small_resnet26d_224,813.66,470.891,384,224,63.61 +convnext_tiny,813.16,313.859,256,224,28.59 +efficientnet_cc_b0_8e,812.96,471.165,384,224,24.01 +vit_relpos_base_patch32_plus_rpn_256,811.26,630.0,512,256,119.42 +mixnet_m,810.2,630.361,512,224,5.01 +efficientnet_cc_b0_4e,808.8,473.577,384,224,13.31 +convnext_tiny_in22ft1k,808.5,315.666,256,224,28.59 +efficientnet_b2a,800.27,318.401,256,256,9.11 +efficientnet_b2,799.96,318.544,256,256,9.11 +regnetz_b16,796.72,319.811,256,224,9.72 +tresnet_m,792.8,643.233,512,224,31.39 +mobilevit_xs,792.23,321.979,256,256,2.32 +ecaresnet26t,791.55,484.557,384,256,16.01 +gc_efficientnetv2_rw_t,791.43,320.691,256,224,13.68 +resnetv2_50t,790.83,646.602,512,224,25.57 +resnetv2_50d,790.11,647.216,512,224,25.57 +regnetx_032,787.5,486.368,384,224,15.3 +ese_vovnet39b,786.37,650.429,512,224,24.57 +tf_efficientnet_cc_b0_4e,784.54,488.254,384,224,13.31 +eca_botnext26ts_256,781.43,326.976,256,256,10.59 +resnet32ts,781.02,327.191,256,256,17.96 +tf_mixnet_m,777.93,492.059,384,224,5.01 +resnet33ts,767.78,332.812,256,256,19.68 +gluon_resnet50_v1c,763.32,502.196,384,224,25.58 +eca_halonext26ts,763.09,334.836,256,256,10.76 +rexnetr_200,762.4,250.686,192,224,16.52 +dpn68b,756.57,506.252,384,224,12.61 +lambda_resnet26t,751.39,510.429,384,256,10.96 +vit_relpos_small_patch16_rpn_224,751.37,510.029,384,224,21.97 +resnet50t,748.03,512.487,384,224,25.57 +cspresnet50d,746.91,341.842,256,256,21.64 +gluon_resnet50_v1d,746.5,513.543,384,224,25.58 +resnet50d,744.99,514.575,384,224,25.58 +legacy_seresnet50,744.88,514.356,384,224,28.09 +cspresnet50w,744.04,343.166,256,256,28.12 +eca_resnet33ts,743.27,343.735,256,256,19.68 +efficientnet_b0_g8_gn,743.27,343.315,256,224,6.56 +seresnet33ts,742.4,344.003,256,256,19.78 +resnetaa50,741.95,516.711,384,224,25.56 +selecsls84,740.99,689.736,512,224,50.95 +dpn68,739.97,517.747,384,224,12.61 +res2net50_48w_2s,738.06,519.427,384,224,25.29 +vit_small_r26_s32_224,737.22,345.982,256,224,36.43 +eca_vovnet39b,735.59,695.354,512,224,22.6 +lambda_resnet26rpt_256,735.09,260.579,192,256,10.99 +nf_regnet_b3,732.33,522.471,384,288,18.59 +rexnet_200,731.68,261.239,192,224,16.37 +densenet121,730.11,348.758,256,224,7.98 +resnest26d,728.94,526.039,384,224,17.07 +bat_resnext26ts,728.42,350.197,256,256,10.73 +mobilevitv2_100,727.72,262.852,192,256,4.9 +tv_densenet121,727.58,350.07,256,224,7.98 +nf_seresnet50,727.17,526.884,384,224,28.09 +gcresnet33ts,725.89,351.666,256,256,19.88 +eca_nfnet_l0,723.69,706.434,512,224,24.14 +nfnet_l0,719.96,532.162,384,224,35.07 +seresnet50,714.65,536.208,384,224,28.09 +twins_pcpvt_small,714.45,356.63,256,224,24.11 +nf_ecaresnet50,713.69,537.063,384,224,25.56 +dla60,709.61,540.13,384,224,22.04 +efficientnet_em,708.33,360.423,256,240,6.9 +hrnet_w18_small_v2,705.02,723.712,512,224,15.6 +resnetblur50d,704.94,362.275,256,224,25.58 +vgg11_bn,703.05,545.962,384,224,132.87 +resnetblur50,698.61,548.824,384,224,25.56 +regnety_032,696.58,549.77,384,224,19.44 +nf_resnet50,696.17,550.716,384,256,25.56 +efficientnet_b3_pruned,694.05,367.106,256,300,9.86 +tf_efficientnet_em,690.66,369.697,256,240,6.9 +skresnet50,685.67,371.92,256,224,25.8 +xcit_tiny_24_p16_224,683.44,371.201,256,224,12.12 +poolformer_s24,681.93,374.176,256,224,21.39 +xcit_tiny_24_p16_224_dist,681.85,371.937,256,224,12.12 +vit_base_resnet26d_224,680.96,562.594,384,224,101.4 +vovnet57a,678.75,564.837,384,224,36.64 +densenet121d,678.22,375.614,256,224,8.0 +resnetaa50d,673.73,569.117,384,224,25.58 +gluon_resnet50_v1s,669.16,573.001,384,224,25.68 +gmixer_24_224,666.22,382.715,256,224,24.72 +swsl_resnext50_32x4d,663.66,577.766,384,224,25.03 +resnext50_32x4d,663.39,577.966,384,224,25.03 +ssl_resnext50_32x4d,663.18,578.185,384,224,25.03 +tv_resnext50_32x4d,662.37,578.888,384,224,25.03 +gluon_resnext50_32x4d,662.06,579.185,384,224,25.03 +haloregnetz_b,660.09,386.296,256,224,11.68 +ese_vovnet57b,656.27,584.17,384,224,38.61 +cspresnext50,656.07,389.365,256,256,20.57 +seresnet50t,655.71,584.407,384,224,28.1 +vit_relpos_medium_patch16_cls_224,654.69,389.857,256,224,38.76 +seresnetaa50d,654.11,390.147,256,224,28.11 +densenetblur121d,649.47,392.249,256,224,8.0 +res2net50_26w_4s,648.76,590.62,384,224,25.7 +fbnetv3_g,647.3,294.603,192,240,16.62 +swin_tiny_patch4_window7_224,646.76,394.841,256,224,28.29 +ecaresnet50d,643.9,595.437,384,224,25.58 +regnety_040,640.15,598.298,384,224,20.65 +gmlp_s16_224,638.67,299.017,192,224,19.42 +crossvit_small_240,637.47,399.952,256,240,26.86 +resnext50d_32x4d,635.21,402.121,256,224,25.05 +nfnet_f0,634.03,806.334,512,192,71.49 +vit_srelpos_medium_patch16_224,629.85,405.54,256,224,38.74 +mobilevit_s,629.67,303.779,192,256,5.58 +skresnet50d,628.92,405.574,256,224,25.82 +vit_relpos_medium_patch16_224,628.2,406.369,256,224,38.75 +resnest50d_1s4x24d,628.12,406.263,256,224,25.68 +mixnet_l,627.47,406.445,256,224,7.33 +tf_efficientnet_b2_ns,627.11,304.591,192,260,9.11 +tf_efficientnet_b2_ap,626.79,304.757,192,260,9.11 +tf_efficientnet_b2,626.11,305.153,192,260,9.11 +regnetx_040,624.89,613.356,384,224,22.12 +regnetv_040,622.71,409.581,256,224,20.64 +darknetaa53,614.47,415.819,256,256,36.02 +seresnext50_32x4d,613.62,416.021,256,224,27.56 +gluon_seresnext50_32x4d,613.35,416.206,256,224,27.56 +sehalonet33ts,613.13,416.664,256,256,13.69 +legacy_seresnext50_32x4d,612.89,416.52,256,224,27.56 +dla60x,612.79,416.731,256,224,17.35 +gcresnet50t,611.79,626.12,384,256,25.9 +xcit_nano_12_p16_384_dist,611.55,416.81,256,384,3.05 +resmlp_24_224,609.69,418.351,256,224,30.02 +resmlp_24_distilled_224,609.51,418.474,256,224,30.02 +gcresnext50ts,606.82,314.923,192,256,15.67 +tf_inception_v3,603.29,635.057,384,299,23.83 +gluon_inception_v3,603.22,635.143,384,299,23.83 +adv_inception_v3,603.01,635.347,384,299,23.83 +inception_v3,602.27,636.205,384,299,23.83 +tf_mixnet_l,600.24,424.956,256,224,7.33 +dm_nfnet_f0,600.1,638.573,384,192,71.49 +xcit_small_12_p16_224,598.44,425.955,256,224,26.25 +xcit_small_12_p16_224_dist,598.22,426.013,256,224,26.25 +semobilevit_s,597.07,320.258,192,256,5.74 +densenet169,592.78,429.221,256,224,14.15 +res2next50,591.98,431.144,256,224,24.67 +resnetv2_101,590.74,431.806,256,224,44.54 +darknet53,590.64,432.606,256,256,41.61 +resnetv2_50x1_bit_distilled,587.0,326.262,192,224,25.55 +res2net50_14w_8s,586.94,433.992,256,224,25.06 +swin_s3_tiny_224,586.39,435.576,256,224,28.33 +repvgg_b1g4,584.26,875.234,512,224,39.97 +dla60_res2net,583.07,437.618,256,224,20.85 +crossvit_15_240,576.62,331.16,192,240,27.53 +cait_xxs24_224,576.52,441.46,256,224,11.96 +cs3darknet_focus_x,569.86,448.292,256,256,35.02 +resnet101,568.98,448.321,256,224,44.55 +gluon_resnet101_v1b,568.4,448.834,256,224,44.55 +tv_resnet101,566.24,450.547,256,224,44.55 +resnetrs101,564.72,451.1,256,192,63.62 +efficientnet_cc_b1_8e,564.18,452.061,256,240,39.72 +crossvit_15_dagger_240,558.23,342.033,192,240,28.21 +vit_base_resnet50d_224,557.61,457.501,256,224,110.97 +mobilevitv2_125,557.38,343.473,192,256,7.48 +xcit_nano_12_p8_224_dist,555.43,459.069,256,224,3.05 +xcit_nano_12_p8_224,555.18,459.311,256,224,3.05 +sebotnet33ts_256,554.49,230.012,128,256,13.7 +resnet51q,551.31,463.504,256,256,35.7 +resnetv2_101d,548.02,465.6,256,224,44.56 +tf_efficientnet_cc_b1_8e,547.16,466.173,256,240,39.72 +resnetv2_50d_gn,546.54,350.469,192,224,25.57 +nf_resnet101,543.9,704.337,384,224,44.55 +vit_base_patch32_384,542.76,470.804,256,384,88.3 +gluon_resnet101_v1c,537.15,475.0,256,224,44.57 +cspdarknet53,537.1,475.617,256,256,27.64 +cs3darknet_x,534.86,477.64,256,256,35.05 +vit_base_r26_s32_224,534.67,357.767,192,224,101.38 +resnest50d,534.66,477.434,256,224,27.48 +resnet50_gn,531.78,360.235,192,224,25.56 +regnetz_c16,530.35,360.552,192,256,13.46 +gluon_resnet101_v1d,528.59,482.76,256,224,44.57 +mixer_b16_224,528.02,484.004,256,224,59.88 +mixer_l32_224,527.33,362.504,192,224,206.94 +mixer_b16_224_miil,526.58,485.347,256,224,59.88 +vit_large_patch32_224,521.73,489.021,256,224,306.54 +dla60_res2next,520.31,490.572,256,224,17.03 +ecaresnet50t,516.29,494.896,256,256,25.57 +cs3sedarknet_xdw,516.24,246.008,128,256,21.6 +lambda_resnet50ts,515.16,371.658,192,256,21.54 +vit_tiny_patch16_384,512.2,249.072,128,384,5.79 +resnet61q,510.55,375.027,192,256,36.85 +swinv2_cr_tiny_224,505.83,504.823,256,224,28.33 +halonet50ts,503.76,380.122,192,256,22.73 +repvgg_b1,503.57,1015.623,512,224,57.42 +swinv2_cr_tiny_ns_224,502.5,508.144,256,224,28.33 +cs3sedarknet_x,501.96,508.547,256,256,35.4 +dla102,497.28,513.14,256,224,33.27 +wide_resnet50_2,495.68,773.85,384,224,68.88 +res2net50_26w_6s,493.57,516.914,256,224,37.05 +resnetaa101d,490.51,520.338,256,224,44.57 +convnext_small,489.81,390.224,192,224,50.22 +convnext_small_in22ft1k,489.45,390.576,192,224,50.22 +legacy_seresnet101,487.73,522.616,256,224,49.33 +vit_relpos_medium_patch16_rpn_224,485.5,526.221,256,224,38.73 +efficientnet_lite3,484.47,263.098,128,300,8.2 +gluon_resnet101_v1s,483.47,527.891,256,224,44.67 +seresnet101,480.65,530.38,256,224,49.33 +cs3edgenet_x,477.59,535.019,256,256,47.82 +nest_tiny,476.68,267.593,128,224,17.06 +nf_seresnet101,474.46,537.213,256,224,49.33 +mobilevitv2_150_in22ft1k,473.86,269.132,128,256,10.59 +mobilevitv2_150,473.84,269.144,128,256,10.59 +resnetblur101d,472.23,540.497,256,224,44.57 +jx_nest_tiny,472.22,270.163,128,224,17.06 +nf_ecaresnet101,469.53,543.375,256,224,44.55 +vgg13_bn,468.37,546.3,256,224,133.05 +twins_pcpvt_base,466.47,408.848,192,224,43.83 +tf_efficientnet_lite3,465.4,273.895,128,300,8.2 +vgg16,465.36,824.954,384,224,138.36 +sequencer2d_s,462.43,412.819,192,224,27.65 +mixnet_xl,460.21,554.308,256,224,11.9 +coat_lite_small,457.06,418.568,192,224,19.84 +efficientnet_b3a,456.95,278.403,128,288,12.23 +efficientnet_b3,456.81,278.448,128,288,12.23 +regnetx_080,454.56,843.629,384,224,39.57 +regnetx_064,452.43,564.953,256,224,26.21 +halo2botnet50ts_256,451.35,424.392,192,256,22.64 +ecaresnet101d,447.44,570.33,256,224,44.57 +densenet201,447.44,425.987,192,224,20.01 +nf_regnet_b4,445.8,428.533,192,320,30.21 +convit_small,443.63,431.737,192,224,27.78 +efficientnetv2_s,433.27,293.157,128,288,21.46 +skresnext50_32x4d,432.3,590.802,256,224,27.48 +cs3se_edgenet_x,431.68,443.324,192,256,50.72 +botnet50ts_256,428.8,297.529,128,256,22.74 +ssl_resnext101_32x4d,427.28,447.74,192,224,44.18 +resnext101_32x4d,427.18,447.921,192,224,44.18 +swsl_resnext101_32x4d,427.16,447.915,192,224,44.18 +gluon_resnext101_32x4d,427.13,447.97,192,224,44.18 +poolformer_s36,425.0,449.906,192,224,30.86 +ese_vovnet39b_evos,421.31,302.862,128,224,24.58 +resnet101d,418.0,457.739,192,256,44.57 +dla102x,417.16,458.658,192,224,26.31 +res2net101_26w_4s,416.51,612.121,256,224,45.21 +lamhalobotnet50ts_256,413.09,463.774,192,256,22.57 +twins_svt_base,411.8,464.138,192,224,56.07 +crossvit_18_240,406.79,312.611,128,240,43.27 +tresnet_l,404.84,1261.389,512,224,55.99 +efficientnetv2_rw_s,402.34,315.806,128,288,23.94 +volo_d1_224,401.47,476.8,192,224,26.63 +resmlp_36_224,401.06,476.505,192,224,44.69 +res2net50_26w_8s,400.52,636.999,256,224,48.4 +resmlp_36_distilled_224,400.14,477.557,192,224,44.69 +swin_small_patch4_window7_224,399.67,478.499,192,224,49.61 +resnest50d_4s2x40d,396.0,645.092,256,224,30.42 +vit_base_patch16_224_miil,395.78,484.311,192,224,86.54 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+gluon_seresnext101_32x4d,375.02,509.78,192,224,48.96 +regnetz_040,374.91,339.544,128,256,27.12 +seresnext101_32x4d,374.73,510.176,192,224,48.96 +regnetv_064,372.69,513.522,192,224,30.58 +regnetz_040h,372.64,341.593,128,256,28.94 +legacy_seresnext101_32x4d,372.06,513.705,192,224,48.96 +deit3_base_patch16_224_in21ft1k,371.79,515.431,192,224,86.59 +deit3_base_patch16_224,371.73,515.464,192,224,86.59 +tf_efficientnet_b3,370.15,344.089,128,300,12.23 +tf_efficientnet_b3_ap,370.14,344.111,128,300,12.23 +tf_efficientnet_b3_ns,370.1,344.134,128,300,12.23 +resnet152,370.08,516.516,192,224,60.19 +vit_relpos_base_patch16_clsgap_224,369.76,518.105,192,224,86.43 +vit_relpos_base_patch16_cls_224,369.34,518.67,192,224,86.43 +resnetv2_50d_frn,369.16,345.594,128,224,25.59 +gluon_resnet152_v1b,369.02,517.998,192,224,60.19 +tv_resnet152,369.0,518.088,192,224,60.19 +regnetz_b16_evos,365.3,348.518,128,224,9.74 +sequencer2d_m,363.12,525.505,192,224,38.31 +ese_vovnet99b_iabn,362.9,1055.043,384,224,63.2 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+convnext_base,336.98,378.091,128,224,88.59 +repvgg_b2,335.01,1527.215,512,224,89.02 +repvgg_b3g4,334.01,1148.557,384,224,83.83 +vgg13,331.37,1544.923,512,224,133.05 +pit_b_224,331.17,385.577,128,224,73.76 +vgg19_bn,330.96,773.109,256,224,143.68 +pit_b_distilled_224,329.46,387.568,128,224,74.79 +regnetx_120,327.41,780.952,256,224,46.11 +twins_pcpvt_large,322.96,392.17,128,224,60.99 +hrnet_w30,321.86,790.607,256,224,37.71 +legacy_seresnet152,319.56,397.245,128,224,66.82 +inception_v4,316.87,603.734,192,299,42.68 +seresnet152,313.75,608.677,192,224,66.82 +vit_small_patch16_36x1_224,310.56,409.448,128,224,64.67 +dla102x2,309.09,412.537,128,224,41.28 +xcit_small_24_p16_224_dist,307.83,412.466,128,224,47.67 +convmixer_1024_20_ks9_p14,307.81,830.813,256,224,24.38 +vit_small_patch16_18x2_224,307.61,413.3,128,224,64.67 +xcit_small_24_p16_224,307.46,412.867,128,224,47.67 +regnety_120,307.05,623.971,192,224,51.82 +poolformer_m36,303.34,420.132,128,224,56.17 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+crossvit_base_240,253.73,376.737,96,240,105.03 +seresnext101_32x8d,251.44,506.792,128,224,93.57 +vit_relpos_base_patch16_rpn_224,250.62,765.002,192,224,86.41 +vit_base_patch16_plus_240,248.4,514.352,128,240,117.56 +tf_efficientnet_lite4,247.32,257.437,64,380,13.01 +efficientnet_b3_gn,245.44,258.998,64,288,11.73 +dm_nfnet_f1,244.51,521.138,128,224,132.63 +seresnext101d_32x8d,242.49,525.503,128,224,93.59 +seresnet152d,242.25,392.75,96,256,66.84 +xcit_tiny_24_p16_384_dist,241.62,526.318,128,384,12.12 +vit_small_patch16_384,239.05,266.865,64,384,22.2 +vit_relpos_base_patch16_plus_240,238.57,535.322,128,240,117.38 +vit_large_r50_s32_224,237.94,401.033,96,224,328.99 +resnetrs152,237.63,535.199,128,256,86.62 +swinv2_tiny_window16_256,237.41,403.072,96,256,28.35 +seresnextaa101d_32x8d,228.0,559.15,128,224,93.59 +xcit_medium_24_p16_224_dist,227.91,558.239,128,224,84.4 +deit3_small_patch16_384_in21ft1k,227.77,280.008,64,384,22.21 +deit3_small_patch16_384,227.76,280.015,64,384,22.21 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+vit_base_patch16_18x2_224,116.55,546.378,64,224,256.73 +convnext_base_384_in22ft1k,115.97,412.084,48,384,88.59 +convnext_xlarge_in22ft1k,115.91,550.445,64,224,350.2 +deit3_large_patch16_224_in21ft1k,113.19,563.501,64,224,304.37 +deit3_large_patch16_224,113.17,563.634,64,224,304.37 +xcit_small_24_p16_384_dist,112.88,421.839,48,384,47.67 +beit_large_patch16_224,107.8,591.544,64,224,304.43 +swinv2_base_window16_256,103.68,460.461,48,256,87.92 +swinv2_base_window12to16_192to256_22kft1k,103.56,461.021,48,256,87.92 +tresnet_l_448,103.2,1236.839,128,448,55.99 +volo_d1_384,99.39,320.613,32,384,26.78 +cait_xxs24_384,97.83,488.033,48,384,12.03 +vit_base_patch16_384,96.96,329.192,32,384,86.86 +deit_base_patch16_384,96.37,331.171,32,384,86.86 +volo_d4_224,95.75,498.748,48,224,192.96 +deit_base_distilled_patch16_384,94.83,336.556,32,384,87.63 +efficientnet_b5,93.71,338.901,32,456,30.39 +deit3_base_patch16_384,93.22,342.328,32,384,86.88 +deit3_base_patch16_384_in21ft1k,92.68,344.327,32,384,86.88 +tf_efficientnet_b5,92.16,344.785,32,456,30.39 +tf_efficientnet_b5_ns,92.11,344.939,32,456,30.39 +tf_efficientnet_b5_ap,91.98,345.405,32,456,30.39 +resnetv2_152x2_bit_teacher,89.37,355.711,32,224,236.34 +crossvit_18_dagger_408,88.76,358.492,32,408,44.61 +xcit_small_24_p8_224,87.25,546.829,48,224,47.63 +xcit_small_24_p8_224_dist,86.87,549.24,48,224,47.63 +convmixer_768_32,85.53,1121.025,96,224,21.11 +vit_large_patch14_224,85.27,561.239,48,224,304.2 +eca_nfnet_l3,84.61,563.702,48,352,72.04 +resnetv2_101x1_bitm,84.61,187.399,16,448,44.54 +beit_base_patch16_384,83.67,381.291,32,384,86.74 +resnest200e,83.33,570.802,48,320,70.2 +tf_efficientnetv2_l_in21ft1k,83.27,379.678,32,384,118.52 +efficientnetv2_l,83.27,379.867,32,384,118.52 +tf_efficientnetv2_l,82.74,382.367,32,384,118.52 +ecaresnet269d,82.15,579.477,48,320,102.09 +tresnet_xl_448,78.31,1222.487,96,448,78.44 +xcit_medium_24_p16_384_dist,77.9,407.346,32,384,84.4 +vit_large_r50_s32_384,77.49,410.426,32,384,329.09 +swinv2_cr_small_384,76.31,416.793,32,384,49.7 +swin_base_patch4_window12_384,74.03,430.327,32,384,87.9 +pnasnet5large,68.77,461.392,32,331,86.06 +resnetrs350,68.24,460.967,32,288,163.96 +nfnet_f3,67.87,703.087,48,320,254.92 +nasnetalarge,67.38,469.785,32,331,88.75 +resmlp_big_24_distilled_224,67.03,475.867,32,224,129.14 +resmlp_big_24_224_in22ft1k,67.03,475.857,32,224,129.14 +resmlp_big_24_224,67.02,475.97,32,224,129.14 +cait_xs24_384,65.59,485.229,32,384,26.67 +convnext_large_384_in22ft1k,63.62,501.159,32,384,197.77 +vit_base_patch8_224,63.42,377.591,24,224,86.58 +cait_xxs36_384,63.23,502.345,32,384,17.37 +ig_resnext101_32x32d,62.72,508.666,32,224,468.53 +xcit_tiny_24_p8_384_dist,62.14,511.676,32,384,12.11 +volo_d5_224,61.58,516.467,32,224,295.46 +vit_base_resnet50_384,61.06,391.43,24,384,98.95 +vit_base_r50_s16_384,61.0,391.773,24,384,98.95 +swinv2_large_window12to16_192to256_22kft1k,60.93,391.352,24,256,196.74 +xcit_medium_24_p8_224,60.43,526.111,32,224,84.32 +xcit_medium_24_p8_224_dist,60.03,529.652,32,224,84.32 +xcit_small_12_p8_384_dist,57.75,413.763,24,384,26.21 +dm_nfnet_f3,57.26,554.375,32,320,254.92 +volo_d2_384,55.56,286.247,16,384,58.87 +efficientnet_b6,54.98,288.003,16,528,43.04 +swinv2_cr_base_384,54.71,436.265,24,384,87.88 +tf_efficientnet_b6,54.38,291.338,16,528,43.04 +tf_efficientnet_b6_ns,54.21,292.241,16,528,43.04 +tf_efficientnet_b6_ap,54.17,292.479,16,528,43.04 +efficientnetv2_xl,53.77,291.666,16,384,208.12 +tf_efficientnetv2_xl_in21ft1k,53.07,295.611,16,384,208.12 +convmixer_1536_20,50.1,957.271,48,224,51.63 +swinv2_cr_huge_224,49.51,482.114,24,224,657.83 +cait_s24_384,49.37,483.331,24,384,47.06 +resnetrs420,48.12,489.4,24,320,191.89 +xcit_large_24_p16_384_dist,45.6,522.909,24,384,189.1 +swin_large_patch4_window12_384,41.65,382.145,16,384,196.74 +convnext_xlarge_384_in22ft1k,40.18,595.51,24,384,350.2 +vit_huge_patch14_224,39.94,398.436,16,224,632.05 +deit3_huge_patch14_224_in21ft1k,38.36,414.578,16,224,632.13 +deit3_huge_patch14_224,38.33,414.855,16,224,632.13 +nfnet_f4,36.85,646.047,24,384,316.07 +resnest269e,35.75,664.499,24,416,110.93 +resnetv2_50x3_bitm,34.88,457.822,16,448,217.32 +xcit_large_24_p8_224_dist,33.7,471.344,16,224,188.93 +xcit_large_24_p8_224,33.68,471.512,16,224,188.93 +resnetv2_152x2_bit_teacher_384,32.69,487.138,16,384,236.34 +ig_resnext101_32x48d,32.39,492.417,16,224,828.41 +swinv2_cr_large_384,32.36,491.839,16,384,196.68 +cait_s36_384,31.92,497.404,16,384,68.37 +efficientnet_b7,31.74,248.492,8,600,66.35 +dm_nfnet_f4,31.4,758.349,24,384,316.07 +tf_efficientnet_b7,31.4,251.12,8,600,66.35 +tf_efficientnet_b7_ns,31.37,251.394,8,600,66.35 +tf_efficientnet_b7_ap,31.35,251.548,8,600,66.35 +xcit_small_24_p8_384_dist,29.2,544.65,16,384,47.63 +vit_large_patch16_384,29.07,411.127,12,384,304.72 +deit3_large_patch16_384,28.22,423.365,12,384,304.76 +deit3_large_patch16_384_in21ft1k,28.19,423.825,12,384,304.76 +swinv2_base_window12to24_192to384_22kft1k,28.14,423.938,12,384,87.92 +beit_large_patch16_384,25.12,475.56,12,384,305.0 +volo_d3_448,23.79,333.686,8,448,86.63 +nfnet_f5,22.84,694.067,16,416,377.21 +resnetv2_152x2_bitm,22.69,350.236,8,448,236.34 +vit_giant_patch14_224,22.29,356.113,8,224,1012.61 +dm_nfnet_f5,20.95,756.72,16,416,377.21 +xcit_medium_24_p8_384_dist,19.97,397.272,8,384,84.32 +efficientnet_b8,19.9,297.659,6,672,87.41 +tf_efficientnet_b8_ap,19.66,301.198,6,672,87.41 +tf_efficientnet_b8,19.65,301.246,6,672,87.41 +nfnet_f6,18.5,641.193,12,448,438.36 +resnetv2_101x3_bitm,18.0,442.742,8,448,387.93 +volo_d4_448,16.87,353.154,6,448,193.41 +swinv2_large_window12to24_192to384_22kft1k,16.59,359.187,6,384,196.74 +dm_nfnet_f6,15.07,522.261,8,448,438.36 +swinv2_cr_huge_384,12.92,461.964,6,384,657.94 +nfnet_f7,12.67,622.861,8,480,499.5 +cait_m36_384,11.76,506.439,6,384,271.22 +xcit_large_24_p8_384_dist,11.53,516.755,6,384,188.93 +volo_d5_448,11.08,357.783,4,448,295.91 +tf_efficientnet_l2_ns_475,10.91,360.832,4,475,480.31 +beit_large_patch16_512,9.42,422.333,4,512,305.67 +volo_d5_512,7.72,385.462,3,512,296.09 +resnetv2_152x4_bitm,4.91,404.529,2,480,936.53 +cait_m48_448,4.71,419.69,2,448,356.46 +efficientnet_l2,3.43,285.826,1,800,480.31 +tf_efficientnet_l2_ns,3.42,287.247,1,800,480.31 diff --git a/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv b/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv new file mode 100644 index 0000000000000000000000000000000000000000..3c0025c8025b8763ba72c1dc40868718dc000821 --- /dev/null +++ b/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv @@ -0,0 +1,835 @@ +model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count +tinynet_e,11915.85,41.681,512,106,2.04 +mobilenetv3_small_050,11290.99,44.293,512,224,1.59 +lcnet_035,10015.98,50.125,512,224,1.64 +lcnet_050,9286.37,54.37,512,224,1.88 +tf_mobilenetv3_small_minimal_100,9042.22,55.986,512,224,2.04 +mobilenetv3_small_075,8679.98,58.254,512,224,2.04 +mobilenetv3_small_100,8035.08,62.981,512,224,2.54 +tinynet_d,7990.69,63.223,512,152,2.34 +tf_mobilenetv3_small_075,7930.1,63.8,512,224,2.04 +tf_mobilenetv3_small_100,7330.24,69.047,512,224,2.54 +lcnet_075,6950.91,73.156,512,224,2.36 +levit_128s,6539.16,77.346,512,224,7.78 +resnet10t,6318.63,80.774,512,176,5.44 +mnasnet_small,5607.09,90.422,512,224,2.03 +lcnet_100,5354.67,95.126,512,224,2.95 +mixer_s32_224,4943.04,103.013,512,224,19.1 +mobilenetv2_035,4789.43,106.101,512,224,1.68 +mnasnet_050,4680.08,108.62,512,224,2.22 +levit_128,4558.28,111.213,512,224,9.21 +cs3darknet_focus_s,4469.48,114.041,512,256,3.27 +vit_small_patch32_224,4445.76,114.324,512,224,22.88 +tinynet_c,4167.16,121.826,512,184,2.46 +gernet_s,4165.03,122.198,512,224,8.17 +cs3darknet_s,4110.51,124.007,512,256,3.28 +regnetx_002,4105.04,124.027,512,224,2.68 +mobilenetv2_050,4051.14,125.606,512,224,1.97 +vit_tiny_r_s16_p8_224,4025.23,126.328,512,224,6.34 +semnasnet_050,3904.91,130.185,512,224,2.08 +regnety_002,3777.81,134.562,512,224,3.16 +levit_192,3727.29,136.213,512,224,10.95 +ghostnet_050,3670.99,138.144,512,224,2.59 +ese_vovnet19b_slim_dw,3629.92,140.575,512,224,1.9 +lcnet_150,3576.28,142.665,512,224,4.5 +gluon_resnet18_v1b,3482.17,146.691,512,224,11.69 +resnet18,3481.78,146.713,512,224,11.69 +swsl_resnet18,3480.5,146.765,512,224,11.69 +ssl_resnet18,3477.04,146.904,512,224,11.69 +resnet14t,3472.37,147.102,512,176,10.08 +tf_efficientnetv2_b0,3428.08,148.143,512,192,7.14 +tf_mobilenetv3_large_minimal_100,3366.45,151.356,512,224,3.92 +mnasnet_075,3238.88,157.273,512,224,3.17 +tf_mobilenetv3_large_075,3189.08,159.67,512,224,3.99 +seresnet18,3138.91,162.608,512,224,11.78 +mobilenetv3_large_075,3095.0,164.56,512,224,3.99 +legacy_seresnet18,3076.04,165.928,512,224,11.78 +hardcorenas_a,2971.63,171.576,512,224,5.26 +levit_256,2956.43,172.043,512,224,18.89 +mnasnet_b1,2930.02,173.933,512,224,4.38 +mnasnet_100,2929.31,173.976,512,224,4.38 +tf_mobilenetv3_large_100,2907.93,175.204,512,224,5.48 +resnet18d,2875.3,177.69,512,224,11.71 +tinynet_b,2851.82,178.435,512,188,3.73 +hardcorenas_b,2772.42,183.73,512,224,5.18 +hardcorenas_c,2763.94,184.272,512,224,5.52 +mobilenetv3_rw,2754.46,184.981,512,224,5.48 +nf_regnet_b0,2740.89,185.595,512,192,8.76 +mobilenetv3_large_100_miil,2733.62,186.4,512,224,5.48 +mobilenetv3_large_100,2732.43,186.472,512,224,5.48 +ese_vovnet19b_slim,2684.58,190.344,512,224,3.17 +spnasnet_100,2610.47,195.171,512,224,4.42 +mobilenetv2_075,2609.91,195.379,512,224,2.64 +semnasnet_075,2603.1,195.762,512,224,2.91 +hardcorenas_d,2566.48,198.271,512,224,7.5 +tf_efficientnetv2_b1,2548.95,199.349,512,192,8.14 +levit_256d,2522.09,201.424,512,224,26.21 +fbnetc_100,2397.58,212.548,512,224,5.57 +tinynet_a,2334.41,218.035,512,192,6.19 +mobilenetv2_100,2313.1,220.563,512,224,3.5 +vit_tiny_patch16_224,2299.56,221.804,512,224,5.72 +mnasnet_a1,2291.94,222.453,512,224,3.89 +deit_tiny_patch16_224,2290.33,222.697,512,224,5.72 +semnasnet_100,2279.15,223.737,512,224,3.89 +edgenext_xx_small,2271.04,224.572,512,256,1.33 +dla46_c,2266.89,225.115,512,224,1.3 +hardcorenas_f,2252.64,226.141,512,224,8.2 +deit_tiny_distilled_patch16_224,2248.67,226.799,512,224,5.91 +hardcorenas_e,2245.94,226.861,512,224,8.07 +xcit_nano_12_p16_224_dist,2177.52,233.052,512,224,3.05 +xcit_nano_12_p16_224,2170.17,234.054,512,224,3.05 +tf_efficientnet_lite0,2134.89,239.057,512,224,4.65 +ghostnet_100,2129.82,239.0,512,224,5.18 +hrnet_w18_small,2121.96,239.906,512,224,13.19 +regnety_004,2085.76,244.311,512,224,4.34 +efficientnet_lite0,2079.28,245.485,512,224,4.65 +cs3darknet_focus_m,2062.98,247.547,512,256,9.3 +pit_ti_distilled_224,2061.94,247.414,512,224,5.1 +mnasnet_140,2060.59,247.645,512,224,7.12 +pit_ti_224,2057.02,247.989,512,224,4.85 +gluon_resnet34_v1b,2039.68,250.446,512,224,21.8 +tv_resnet34,2038.39,250.573,512,224,21.8 +resnet34,2036.51,250.813,512,224,21.8 +ese_vovnet19b_dw,1999.58,255.562,512,224,6.54 +resnet26,1962.0,260.488,512,224,16.0 +tf_efficientnetv2_b2,1951.52,260.748,512,208,10.1 +skresnet18,1943.81,262.753,512,224,11.96 +cs3darknet_m,1940.79,263.122,512,256,9.31 +regnetz_005,1916.17,265.765,512,224,7.12 +resnetblur18,1897.99,269.406,512,224,11.69 +rexnetr_100,1893.12,201.724,384,224,4.88 +nf_resnet26,1869.64,273.344,512,224,16.0 +mobilenetv2_110d,1868.27,204.505,384,224,4.52 +visformer_tiny,1861.63,274.356,512,224,10.32 +mixer_b32_224,1856.75,274.965,512,224,60.29 +seresnet34,1837.21,277.783,512,224,21.96 +fbnetv3_b,1825.5,278.744,512,224,8.6 +mobilevitv2_050,1824.87,279.552,512,256,1.37 +gernet_m,1822.12,280.293,512,224,21.14 +resnet34d,1813.16,281.758,512,224,21.82 +levit_384,1801.0,283.153,512,224,39.13 +legacy_seresnet34,1781.32,286.529,512,224,21.96 +regnetx_004,1780.61,286.47,512,224,5.16 +tf_efficientnet_b0_ns,1779.24,214.77,384,224,5.29 +tf_efficientnet_b0,1779.02,214.765,384,224,5.29 +tf_efficientnet_b0_ap,1777.73,214.898,384,224,5.29 +efficientnet_b0,1751.72,291.183,512,224,5.29 +selecsls42,1718.76,297.231,512,224,30.35 +selecsls42b,1710.18,298.726,512,224,32.46 +vit_base_patch32_224,1708.63,298.818,512,224,88.22 +vit_base_patch32_224_sam,1707.5,298.997,512,224,88.22 +efficientnet_es_pruned,1687.32,302.688,512,224,5.44 +resnetrs50,1686.45,302.42,512,160,35.69 +efficientnet_es,1686.11,302.906,512,224,5.44 +mixer_s16_224,1660.76,307.737,512,224,18.53 +darknet17,1654.01,309.253,512,256,14.3 +mobilenetv2_140,1637.57,233.691,384,224,6.11 +fbnetv3_d,1634.54,233.136,384,224,10.31 +tf_efficientnet_es,1623.9,314.542,512,224,5.44 +resnet26d,1623.31,314.899,512,224,16.01 +mobilevit_xxs,1602.81,238.427,384,256,1.27 +resmlp_12_distilled_224,1577.54,323.769,512,224,15.35 +resmlp_12_224,1577.31,323.803,512,224,15.35 +pit_xs_224,1555.66,328.198,512,224,10.62 +pit_xs_distilled_224,1555.48,328.255,512,224,11.0 +semnasnet_140,1546.19,330.184,512,224,6.11 +ghostnet_130,1542.47,330.535,512,224,7.36 +repvgg_b0,1538.07,331.828,512,224,15.82 +efficientnet_lite1,1530.99,166.26,256,240,5.42 +dla34,1524.02,335.337,512,224,15.74 +edgenext_x_small,1512.48,337.399,512,256,2.34 +darknet21,1486.14,344.159,512,256,20.86 +selecsls60,1482.76,344.397,512,224,30.67 +selecsls60b,1478.62,345.378,512,224,32.77 +nf_seresnet26,1473.71,346.754,512,224,17.4 +vit_small_patch32_384,1455.89,350.818,512,384,22.92 +gmixer_12_224,1448.32,352.721,512,224,12.7 +efficientnet_b1_pruned,1446.82,352.35,512,240,6.33 +tf_efficientnet_lite1,1443.47,176.394,256,240,5.42 +nf_ecaresnet26,1440.41,354.896,512,224,16.0 +xcit_tiny_12_p16_224_dist,1426.36,357.157,512,224,6.72 +xcit_tiny_12_p16_224,1426.18,357.168,512,224,6.72 +sedarknet21,1401.98,364.696,512,256,20.95 +rexnetr_130,1388.84,183.199,256,224,7.61 +dla46x_c,1388.59,367.953,512,224,1.07 +gmlp_ti16_224,1381.11,276.449,384,224,5.87 +mixnet_s,1365.54,373.667,512,224,4.13 +rexnet_100,1364.31,280.319,384,224,4.8 +regnety_006,1361.43,374.963,512,224,6.06 +mobilenetv2_120d,1352.9,188.013,256,224,5.83 +legacy_seresnext26_32x4d,1349.26,378.798,512,224,16.79 +crossvit_tiny_240,1348.01,378.219,512,240,7.01 +vit_tiny_r_s16_p8_384,1345.31,284.562,384,384,6.36 +poolformer_s12,1342.54,380.659,512,224,11.92 +dla60x_c,1341.77,380.621,512,224,1.32 +efficientnet_b1,1325.85,191.544,256,224,7.79 +resnetv2_50,1288.61,396.553,512,224,25.55 +regnetx_006,1286.44,397.176,512,224,6.2 +crossvit_9_240,1258.73,303.637,384,240,8.55 +convnext_nano_ols,1252.33,408.151,512,224,15.6 +convnext_nano,1249.89,408.864,512,224,15.59 +convnext_nano_hnf,1249.05,409.138,512,224,15.59 +resnet26t,1237.34,413.275,512,256,16.01 +tf_mixnet_s,1236.15,412.905,512,224,4.13 +nf_regnet_b2,1229.56,414.759,512,240,14.31 +rexnetr_150,1224.57,207.878,256,224,9.78 +gluon_resnet50_v1b,1219.23,419.12,512,224,25.56 +tv_resnet50,1218.99,419.17,512,224,25.56 +crossvit_9_dagger_240,1218.38,313.701,384,240,8.78 +resnet50,1218.01,419.528,512,224,25.56 +swsl_resnet50,1217.39,419.737,512,224,25.56 +ssl_resnet50,1217.38,419.757,512,224,25.56 +cs3darknet_focus_l,1216.61,314.788,384,256,21.15 +repvgg_a2,1214.87,420.579,512,224,28.21 +cs3darknet_l,1203.14,318.267,384,256,21.16 +gernet_l,1201.09,425.379,512,256,31.08 +efficientnet_lite2,1191.67,213.855,256,260,6.09 +nf_regnet_b1,1181.15,431.966,512,256,10.22 +seresnext26d_32x4d,1178.86,325.051,384,224,16.81 +botnet26t_256,1178.34,325.281,384,256,12.49 +seresnext26tn_32x4d,1177.85,325.355,384,224,16.81 +seresnext26t_32x4d,1176.65,325.669,384,224,16.81 +mobilevitv2_075,1174.29,217.001,256,256,2.87 +ecaresnext50t_32x4d,1159.52,330.605,384,224,15.41 +ecaresnext26t_32x4d,1158.26,330.961,384,224,15.41 +gluon_resnet50_v1c,1147.86,333.697,384,224,25.58 +halonet26t,1136.15,337.402,384,256,12.48 +resnetv2_50d,1134.86,450.316,512,224,25.57 +resnetv2_50t,1132.89,451.133,512,224,25.57 +edgenext_small,1127.71,452.849,512,256,5.59 +tf_efficientnet_lite2,1121.02,227.403,256,260,6.09 +convit_tiny,1118.98,456.53,512,224,5.71 +skresnet34,1113.08,458.799,512,224,22.28 +tf_efficientnet_b1,1099.77,231.299,256,240,7.79 +tf_efficientnet_b1_ap,1099.37,231.402,256,240,7.79 +efficientnetv2_rw_t,1098.86,230.78,256,224,13.65 +tf_efficientnet_b1_ns,1098.29,231.567,256,240,7.79 +ecaresnetlight,1091.16,468.275,512,224,30.16 +gluon_resnet50_v1d,1084.38,353.226,384,224,25.58 +dpn68b,1083.77,353.123,384,224,12.61 +cs3sedarknet_l,1083.42,353.12,384,256,21.91 +resnet50d,1078.0,355.348,384,224,25.58 +resnet50t,1076.81,355.721,384,224,25.57 +resnet32ts,1075.86,237.337,256,256,17.96 +resnet33ts,1061.36,240.599,256,256,19.68 +vit_small_patch16_224,1057.92,362.157,384,224,22.05 +resnetaa50,1057.73,362.204,384,224,25.56 +vit_small_resnet26d_224,1057.57,362.04,384,224,63.61 +deit_small_patch16_224,1050.7,364.638,384,224,22.05 +cspresnet50,1042.19,367.617,384,256,21.62 +tf_efficientnetv2_b3,1041.71,243.94,256,240,14.36 +regnetx_008,1034.73,493.971,512,224,7.26 +ecaresnet26t,1033.34,371.048,384,256,16.01 +deit_small_distilled_patch16_224,1028.8,372.398,384,224,22.44 +vit_relpos_base_patch32_plus_rpn_256,1021.86,499.989,512,256,119.42 +dla60,1020.05,375.488,384,224,22.04 +res2net50_48w_2s,1018.83,376.079,384,224,25.29 +gc_efficientnetv2_rw_t,1014.65,249.524,256,224,13.68 +vit_relpos_small_patch16_rpn_224,1013.69,377.786,384,224,21.97 +edgenext_small_rw,1011.18,505.339,512,256,7.83 +pit_s_224,1010.83,378.943,384,224,23.46 +seresnet33ts,1007.26,253.362,256,256,19.78 +efficientnet_em,1007.19,253.179,256,240,6.9 +vovnet39a,1006.62,507.995,512,224,22.6 +legacy_seresnet50,1003.5,381.52,384,224,28.09 +gluon_resnext50_32x4d,1001.3,382.689,384,224,25.03 +tv_resnext50_32x4d,1001.18,382.711,384,224,25.03 +resnext50_32x4d,1001.03,382.776,384,224,25.03 +ssl_resnext50_32x4d,1000.68,382.908,384,224,25.03 +eca_resnet33ts,999.77,255.368,256,256,19.68 +swsl_resnext50_32x4d,997.37,384.186,384,224,25.03 +regnety_008,993.3,514.408,512,224,6.26 +dpn68,992.27,385.859,384,224,12.61 +deit3_small_patch16_224,987.86,387.777,384,224,22.06 +deit3_small_patch16_224_in21ft1k,987.15,388.058,384,224,22.06 +gcresnet33ts,985.12,258.855,256,256,19.88 +efficientnet_b2a,980.29,259.63,256,256,9.11 +tf_efficientnet_em,980.0,260.253,256,240,6.9 +efficientnet_b2,978.68,260.092,256,256,9.11 +seresnet50,971.79,394.011,384,224,28.09 +gluon_resnet50_v1s,970.71,394.714,384,224,25.68 +vit_srelpos_small_patch16_224,969.18,395.281,384,224,21.97 +vit_relpos_small_patch16_224,965.13,396.742,384,224,21.98 +ecaresnet50d_pruned,964.18,530.07,512,224,19.94 +cspresnet50d,956.82,266.672,256,256,21.64 +vgg11,954.03,536.508,512,224,132.86 +cspresnet50w,952.27,267.927,256,256,28.12 +ese_vovnet39b,951.93,537.173,512,224,24.57 +vit_base_patch32_plus_256,951.5,537.138,512,256,119.48 +resnetaa50d,950.79,403.026,384,224,25.58 +eca_vovnet39b,948.4,539.184,512,224,22.6 +lambda_resnet26rpt_256,942.15,203.17,192,256,10.99 +pit_s_distilled_224,934.29,273.079,256,224,24.04 +mobilevit_xs,924.5,275.792,256,256,2.32 +tv_densenet121,917.93,277.067,256,224,7.98 +densenet121,913.65,278.353,256,224,7.98 +resnetblur50,911.91,420.254,384,224,25.56 +hrnet_w18_small_v2,910.26,559.998,512,224,15.6 +coat_lite_tiny,909.29,421.406,384,224,5.72 +mobilevitv2_100,907.45,281.094,256,256,4.9 +nf_resnet50,900.11,425.722,384,256,25.56 +resnext50d_32x4d,894.57,285.293,256,224,25.05 +nf_seresnet50,892.73,428.967,384,224,28.09 +rexnetr_200,890.57,214.407,192,224,16.52 +efficientnet_cc_b0_4e,890.34,430.073,384,224,13.31 +efficientnet_cc_b0_8e,889.37,430.553,384,224,24.01 +dla60x,886.5,287.775,256,224,17.35 +twins_svt_small,885.48,432.048,384,224,24.06 +seresnet50t,879.71,435.29,384,224,28.1 +mixnet_m,878.04,581.529,512,224,5.01 +nf_ecaresnet50,875.38,437.674,384,224,25.56 +efficientnet_b2_pruned,873.9,291.355,256,260,8.31 +densenet121d,873.44,291.238,256,224,8.0 +cspresnext50,868.23,294.006,256,256,20.57 +rexnet_150,866.26,294.391,256,224,9.73 +ecaresnet50d,862.65,444.205,384,224,25.58 +fbnetv3_g,862.32,220.642,192,240,16.62 +regnetz_b16,862.05,295.457,256,224,9.72 +tf_efficientnet_cc_b0_4e,861.1,444.691,384,224,13.31 +tf_efficientnet_cc_b0_8e,857.16,446.822,384,224,24.01 +gcresnet50t,854.99,447.633,384,256,25.9 +res2net50_26w_4s,851.03,449.921,384,224,25.7 +coat_lite_mini,849.82,450.985,384,224,11.01 +tf_efficientnet_b2_ap,849.52,224.466,192,260,9.11 +tf_efficientnet_b2,848.58,224.736,192,260,9.11 +tf_efficientnet_b2_ns,847.86,224.983,192,260,9.11 +vit_base_resnet26d_224,844.62,453.315,384,224,101.4 +vgg11_bn,832.74,460.889,384,224,132.87 +vovnet57a,832.06,614.449,512,224,36.64 +selecsls84,830.17,615.492,512,224,50.95 +resnetblur50d,826.31,308.964,256,224,25.58 +convnext_tiny_hnfd,820.9,310.941,256,224,28.59 +convnext_tiny_hnf,819.46,311.471,256,224,28.59 +convnext_tiny,819.24,311.536,256,224,28.59 +convnext_tiny_in22ft1k,818.81,311.724,256,224,28.59 +rexnet_130,816.78,312.226,256,224,7.56 +seresnext50_32x4d,814.69,313.102,256,224,27.56 +legacy_seresnext50_32x4d,813.61,313.477,256,224,27.56 +gluon_seresnext50_32x4d,813.13,313.678,256,224,27.56 +skresnet50,808.8,473.357,384,224,25.8 +visformer_small,806.27,475.588,384,224,40.22 +res2net50_14w_8s,794.56,319.93,256,224,25.06 +densenetblur121d,789.33,322.521,256,224,8.0 +seresnetaa50d,785.32,324.779,256,224,28.11 +gluon_inception_v3,782.59,489.263,384,299,23.83 +inception_v3,782.35,489.427,384,299,23.83 +adv_inception_v3,778.18,491.976,384,299,23.83 +resmlp_24_distilled_224,777.24,327.895,256,224,30.02 +resmlp_24_224,776.95,327.972,256,224,30.02 +tf_inception_v3,775.41,493.776,384,299,23.83 +ese_vovnet57b,774.18,495.058,384,224,38.61 +tf_mixnet_m,773.08,495.127,384,224,5.01 +resnetv2_101,772.45,329.834,256,224,44.54 +dla60_res2net,767.35,332.099,256,224,20.85 +nf_regnet_b3,766.23,499.321,384,288,18.59 +sehalonet33ts,763.66,334.4,256,256,13.69 +ecaresnet101d_pruned,754.9,676.449,512,224,24.88 +darknet53,753.16,339.081,256,256,41.61 +densenet169,752.52,337.551,256,224,14.15 +resnet101,747.89,340.74,256,224,44.55 +gluon_resnet101_v1b,747.04,341.055,256,224,44.55 +tv_resnet101,746.84,341.219,256,224,44.55 +skresnet50d,739.17,344.891,256,224,25.82 +twins_pcpvt_small,738.11,345.194,256,224,24.11 +vit_small_r26_s32_224,733.9,347.477,256,224,36.43 +mobilevit_s,733.0,260.821,192,256,5.58 +darknetaa53,732.7,348.577,256,256,36.02 +xcit_tiny_24_p16_224_dist,727.98,348.335,256,224,12.12 +xcit_tiny_24_p16_224,727.1,348.63,256,224,12.12 +efficientnet_b0_gn,724.56,352.174,256,224,5.29 +efficientnet_b3_pruned,722.23,352.701,256,300,9.86 +gluon_resnet101_v1c,717.66,355.143,256,224,44.57 +resnext26ts,717.15,534.946,384,256,10.3 +resnetv2_101d,715.45,356.238,256,224,44.56 +gmixer_24_224,714.67,356.582,256,224,24.72 +resnetrs101,714.37,356.071,256,192,63.62 +nf_resnet101,712.1,537.603,384,224,44.55 +efficientnet_lite3,702.44,181.104,128,300,8.2 +mixnet_l,702.18,545.327,384,224,7.33 +eca_resnext26ts,694.05,368.289,256,256,10.3 +semobilevit_s,692.92,368.16,256,256,5.74 +seresnext26ts,691.18,369.699,256,256,10.39 +poolformer_s24,689.84,369.792,256,224,21.39 +gluon_resnet101_v1d,688.26,370.323,256,224,44.57 +dla102,688.03,370.524,256,224,33.27 +vit_relpos_medium_patch16_rpn_224,687.13,371.514,256,224,38.73 +sebotnet33ts_256,686.07,279.058,192,256,13.7 +gcresnext26ts,683.09,373.929,256,256,10.48 +regnetx_016,682.73,749.012,512,224,9.19 +haloregnetz_b,680.78,374.495,256,224,11.68 +cspdarknet53,679.01,375.961,256,256,27.64 +vgg13,677.45,566.653,384,224,133.05 +xcit_nano_12_p16_384_dist,671.72,379.231,256,384,3.05 +wide_resnet50_2,668.78,573.358,384,224,68.88 +tf_efficientnet_lite3,665.78,191.165,128,300,8.2 +vit_relpos_medium_patch16_cls_224,661.77,385.665,256,224,38.76 +vit_srelpos_medium_patch16_224,659.88,386.996,256,224,38.74 +rexnet_200,659.06,290.146,192,224,16.37 +vit_base_resnet50d_224,658.84,386.945,256,224,110.97 +ecaresnet50t,657.78,388.237,256,256,25.57 +gmlp_s16_224,657.63,290.408,192,224,19.42 +vit_relpos_medium_patch16_224,657.05,388.484,256,224,38.75 +tf_efficientnet_cc_b1_8e,654.82,389.25,256,240,39.72 +regnety_016,650.07,785.757,512,224,11.2 +swin_tiny_patch4_window7_224,648.69,393.641,256,224,28.29 +xcit_small_12_p16_224,640.82,397.688,256,224,26.25 +gluon_resnet101_v1s,640.79,397.908,256,224,44.67 +xcit_small_12_p16_224_dist,639.99,398.193,256,224,26.25 +crossvit_small_240,638.8,399.076,256,240,26.86 +efficientnet_cc_b1_8e,637.42,399.94,256,240,39.72 +resnetaa101d,634.86,401.619,256,224,44.57 +cs3sedarknet_xdw,630.82,302.41,192,256,21.6 +repvgg_b1,623.55,820.034,512,224,57.42 +mobilevitv2_125,620.51,308.406,192,256,7.48 +bat_resnext26ts,613.61,415.954,256,256,10.73 +gluon_resnext101_32x4d,609.67,418.333,256,224,44.18 +swsl_resnext101_32x4d,609.02,418.731,256,224,44.18 +resnext101_32x4d,609.01,418.74,256,224,44.18 +tf_mixnet_l,606.88,420.297,256,224,7.33 +ssl_resnext101_32x4d,606.28,420.718,256,224,44.18 +legacy_seresnet101,601.55,423.316,256,224,49.33 +cs3darknet_focus_x,600.02,425.715,256,256,35.02 +dla102x,598.42,319.231,192,224,26.31 +halonet50ts,597.8,320.205,192,256,22.73 +xcit_nano_12_p8_224,595.07,428.358,256,224,3.05 +xcit_nano_12_p8_224_dist,593.27,429.695,256,224,3.05 +cait_xxs24_224,593.22,428.92,256,224,11.96 +seresnet101,590.42,431.41,256,224,49.33 +swin_s3_tiny_224,588.57,433.98,256,224,28.33 +resnetv2_50x1_bit_distilled,585.83,326.889,192,224,25.55 +efficientnet_b0_g8_gn,582.67,438.264,256,224,6.56 +crossvit_15_240,580.46,328.975,192,240,27.53 +resnetblur101d,576.87,442.155,256,224,44.57 +res2net50_26w_6s,573.8,444.339,256,224,37.05 +vgg13_bn,573.47,446.125,256,224,133.05 +efficientnet_b3a,572.29,221.925,128,288,12.23 +efficientnet_b3,572.18,221.941,128,288,12.23 +cs3darknet_x,571.12,447.259,256,256,35.05 +densenet201,562.52,338.221,192,224,20.01 +crossvit_15_dagger_240,562.49,339.489,192,240,28.21 +efficientnetv2_s,559.15,226.702,128,288,21.46 +eca_botnext26ts_256,558.6,457.666,256,256,10.59 +mixer_b16_224,556.6,459.152,256,224,59.88 +mixer_b16_224_miil,556.5,459.202,256,224,59.88 +eca_halonext26ts,547.96,466.574,256,256,10.76 +ecaresnet101d,546.58,466.555,256,224,44.57 +vgg16,546.06,702.994,384,224,138.36 +mixer_l32_224,543.38,351.819,192,224,206.94 +vit_base_patch32_384,543.37,470.294,256,384,88.3 +nf_seresnet101,540.53,471.014,256,224,49.33 +resnetv2_152,536.63,474.697,256,224,60.19 +botnet50ts_256,534.71,238.412,128,256,22.74 +mobilevitv2_150,533.38,238.97,128,256,10.59 +vit_base_r26_s32_224,533.28,358.697,192,224,101.38 +mobilevitv2_150_in22ft1k,532.99,239.183,128,256,10.59 +cs3sedarknet_x,531.85,479.872,256,256,35.4 +nf_ecaresnet101,531.3,479.947,256,224,44.55 +cs3edgenet_x,529.37,482.632,256,256,47.82 +res2next50,528.59,483.023,256,224,24.67 +res2net101_26w_4s,527.01,483.179,256,224,45.21 +vit_large_patch32_224,524.74,486.172,256,224,306.54 +resnet101d,523.85,364.964,192,256,44.57 +efficientnetv2_rw_s,520.77,243.564,128,288,23.94 +halo2botnet50ts_256,517.51,369.975,192,256,22.64 +resmlp_36_distilled_224,513.23,371.84,192,224,44.69 +vit_tiny_patch16_384,510.99,249.657,128,384,5.79 +resmlp_36_224,509.53,374.55,192,224,44.69 +swinv2_cr_tiny_224,506.82,503.861,256,224,28.33 +mixnet_xl,505.67,504.387,256,224,11.9 +resnetv2_50d_gn,505.25,379.149,192,224,25.57 +swinv2_cr_tiny_ns_224,504.1,506.527,256,224,28.33 +gluon_resnet152_v1b,502.02,380.204,192,224,60.19 +regnetz_d8,501.8,253.463,128,256,23.37 +resnet152,501.44,380.547,192,224,60.19 +tv_resnet152,501.12,380.811,192,224,60.19 +xception,497.64,256.367,128,299,22.86 +regnety_032,496.85,771.405,384,224,19.44 +tf_efficientnet_b3_ap,496.0,256.348,128,300,12.23 +tf_efficientnet_b3,494.58,257.101,128,300,12.23 +tf_efficientnet_b3_ns,492.45,258.213,128,300,12.23 +convnext_small_in22ft1k,490.79,389.411,192,224,50.22 +res2net50_26w_8s,489.22,520.921,256,224,48.4 +tf_efficientnetv2_s_in21ft1k,488.77,259.622,128,300,21.46 +tf_efficientnetv2_s,488.25,259.918,128,300,21.46 +gluon_resnet152_v1c,488.2,390.894,192,224,60.21 +convnext_small,487.15,392.394,192,224,50.22 +twins_pcpvt_base,487.13,391.314,192,224,43.83 +resnetv2_152d,486.82,392.059,192,224,60.2 +legacy_seresnext101_32x4d,484.7,393.829,192,224,48.96 +resnet50_gn,482.45,397.141,192,224,25.56 +gluon_seresnext101_32x4d,480.63,397.197,192,224,48.96 +hrnet_w32,480.24,528.215,256,224,41.23 +sequencer2d_s,480.16,264.213,128,224,27.65 +seresnext101_32x4d,479.03,398.526,192,224,48.96 +nest_tiny,477.97,266.889,128,224,17.06 +dla60_res2next,477.74,534.408,256,224,17.03 +gluon_resnet152_v1d,476.32,400.788,192,224,60.21 +regnetz_c16,475.79,402.061,192,256,13.46 +hrnet_w18,473.42,535.867,256,224,21.3 +jx_nest_tiny,472.84,269.807,128,224,17.06 +regnetz_d32,471.85,269.596,128,256,27.58 +regnetz_040,471.81,269.412,128,256,27.12 +xception41p,471.79,270.424,128,299,26.91 +vgg16_bn,470.3,543.999,256,224,138.37 +regnetz_040h,469.27,270.846,128,256,28.94 +poolformer_s36,467.39,408.832,192,224,30.86 +resnet51q,463.81,551.102,256,256,35.7 +efficientnet_el_pruned,461.98,275.957,128,300,10.59 +efficientnet_el,461.97,275.94,128,300,10.59 +coat_lite_small,461.36,414.606,192,224,19.84 +nf_regnet_b4,457.97,417.049,192,320,30.21 +vgg19,457.37,839.347,384,224,143.67 +cs3se_edgenet_x,457.05,418.615,192,256,50.72 +dla169,455.51,419.044,192,224,53.39 +convit_small,454.72,421.197,192,224,27.78 +gluon_resnet152_v1s,452.53,421.917,192,224,60.32 +tf_efficientnet_el,449.8,283.446,128,300,10.59 +gcresnext50ts,445.12,429.834,192,256,15.67 +regnetx_040,442.35,866.937,384,224,22.12 +vit_small_resnet50d_s16_224,437.26,437.826,192,224,57.53 +volo_d1_224,437.04,437.842,192,224,26.63 +mobilevitv2_175_in22ft1k,434.9,293.339,128,256,14.25 +mobilevitv2_175,434.88,293.341,128,256,14.25 +resnet61q,433.95,441.405,192,256,36.85 +ese_vovnet99b,433.24,589.371,256,224,63.2 +ese_vovnet39b_evos,430.54,296.328,128,224,24.58 +twins_svt_base,425.02,449.64,192,224,56.07 +resnest14d,411.87,1242.634,512,224,10.61 +dla102x2,405.87,313.766,128,224,41.28 +mobilevitv2_200_in22ft1k,405.83,314.414,128,256,18.45 +mobilevitv2_200,405.76,314.447,128,256,18.45 +inception_v4,405.43,471.399,192,299,42.68 +crossvit_18_240,404.58,314.332,128,240,43.27 +swin_small_patch4_window7_224,400.37,477.632,192,224,49.61 +densenet161,399.17,318.189,128,224,28.68 +vgg19_bn,398.5,642.012,256,224,143.68 +legacy_seresnet152,398.4,478.588,192,224,66.82 +vit_base_patch16_224_miil,397.86,481.79,192,224,86.54 +sequencer2d_m,396.83,480.626,192,224,38.31 +crossvit_18_dagger_240,396.31,320.926,128,240,44.27 +resnetv2_50d_frn,394.18,323.553,128,224,25.59 +vit_base_patch16_224,392.99,487.729,192,224,86.57 +vit_base_patch16_224_sam,392.92,487.774,192,224,86.57 +vit_base_patch16_rpn_224,391.32,489.846,192,224,86.54 +xception41,391.05,326.045,128,299,26.97 +deit_base_patch16_224,390.04,491.437,192,224,86.57 +cait_xxs36_224,387.24,492.066,192,224,17.3 +efficientnet_b0_g16_evos,386.23,993.13,384,224,8.11 +deit_base_distilled_patch16_224,384.06,499.086,192,224,87.34 +xcit_tiny_12_p16_384_dist,383.09,499.371,192,384,6.72 +vit_relpos_base_patch16_rpn_224,382.95,500.328,192,224,86.41 +seresnet152,379.38,334.127,128,224,66.82 +resnetv2_50d_evos,374.27,340.812,128,224,25.59 +deit3_base_patch16_224,374.05,512.341,192,224,86.59 +deit3_base_patch16_224_in21ft1k,373.84,512.639,192,224,86.59 +vit_relpos_base_patch16_clsgap_224,370.76,516.704,192,224,86.43 +vit_relpos_base_patch16_cls_224,370.22,517.437,192,224,86.43 +hrnet_w30,369.35,688.202,256,224,37.71 +vit_relpos_base_patch16_224,368.93,519.279,192,224,86.43 +gluon_resnext101_64x4d,363.93,350.084,128,224,83.46 +resnext101_64x4d,363.79,350.21,128,224,83.46 +beit_base_patch16_224,358.77,534.02,192,224,86.53 +ens_adv_inception_resnet_v2,358.6,532.08,192,299,55.84 +wide_resnet101_2,358.56,533.868,192,224,126.89 +inception_resnet_v2,358.54,532.143,192,299,55.84 +resnet200,357.55,355.04,128,224,64.67 +resnet152d,357.54,355.729,128,256,60.21 +swinv2_tiny_window8_256,357.0,536.56,192,256,28.35 +efficientnet_b4,354.99,268.381,96,320,19.34 +dpn92,353.0,723.721,256,224,37.67 +repvgg_b2,352.05,1453.222,512,224,89.02 +resnest50d_1s4x24d,349.97,730.142,256,224,25.68 +regnetz_b16_evos,347.19,366.83,128,224,9.74 +tnt_s_patch16_224,342.71,558.25,192,224,23.76 +xception65p,341.43,373.588,128,299,39.82 +convnext_base_in22ft1k,339.67,374.996,128,224,88.59 +convnext_base,338.68,376.119,128,224,88.59 +efficientnet_lite4,338.39,187.783,64,380,13.01 +twins_pcpvt_large,333.52,379.633,128,224,60.99 +pit_b_224,331.08,385.636,128,224,73.76 +pit_b_distilled_224,328.87,388.208,128,224,74.79 +xcit_small_24_p16_224_dist,326.41,388.817,128,224,47.67 +xcit_small_24_p16_224,326.38,388.806,128,224,47.67 +tf_efficientnet_lite4,324.6,195.748,64,380,13.01 +eca_nfnet_l0,319.84,1599.745,512,224,24.14 +nfnet_l0,319.69,1600.317,512,224,35.07 +gluon_seresnext101_64x4d,319.51,398.398,128,224,88.23 +repvgg_b3,316.57,1211.922,384,224,123.09 +skresnext50_32x4d,315.84,809.121,256,224,27.48 +poolformer_m36,315.69,403.496,128,224,56.17 +ssl_resnext101_32x8d,313.35,406.924,128,224,88.79 +resnext101_32x8d,312.8,407.622,128,224,88.79 +swsl_resnext101_32x8d,312.76,407.724,128,224,88.79 +ig_resnext101_32x8d,311.13,409.865,128,224,88.79 +vit_small_patch16_36x1_224,309.04,411.365,128,224,64.67 +regnetx_032,308.88,1241.936,384,224,15.3 +vit_small_patch16_18x2_224,306.57,414.654,128,224,64.67 +xcit_tiny_12_p8_224,306.37,415.93,128,224,6.71 +cait_s24_224,305.74,415.965,128,224,46.92 +xcit_tiny_12_p8_224_dist,304.23,418.886,128,224,6.71 +swinv2_cr_small_ns_224,300.99,422.86,128,224,49.7 +twins_svt_large,300.69,423.548,128,224,99.27 +swinv2_cr_small_224,299.81,424.482,128,224,49.7 +coat_tiny,298.83,426.275,128,224,5.5 +resnest26d,298.23,1286.829,384,224,17.07 +nest_small,296.79,321.765,96,224,38.35 +jx_nest_small,293.75,325.094,96,224,38.35 +swin_s3_small_224,290.56,438.612,128,224,49.74 +dpn98,290.11,439.591,128,224,61.57 +resnetv2_50d_evob,289.65,330.197,96,224,25.59 +seresnet152d,283.9,447.414,128,256,66.84 +gluon_xception65,283.18,337.068,96,299,39.92 +convnext_tiny_384_in22ft1k,282.39,338.982,96,384,28.59 +resnetrs152,282.26,450.046,128,256,86.62 +xception65,281.11,339.548,96,299,39.92 +swin_base_patch4_window7_224,281.0,453.662,128,224,87.77 +hrnet_w48,279.44,682.135,192,224,77.47 +mixnet_xxl,278.4,457.833,128,224,23.96 +seresnext101_32x8d,278.13,458.033,128,224,93.57 +gmlp_b16_224,275.97,346.253,96,224,73.08 +seresnext101d_32x8d,270.35,471.144,128,224,93.59 +resnet200d,267.1,476.272,128,256,64.69 +nfnet_f0,265.61,1926.394,512,192,71.49 +regnetz_e8,256.51,247.489,64,256,57.7 +xcit_tiny_24_p16_384_dist,255.73,371.975,96,384,12.12 +crossvit_base_240,254.6,375.374,96,240,105.03 +dm_nfnet_f0,251.38,1526.301,384,192,71.49 +hrnet_w40,249.23,765.525,192,224,57.56 +vit_base_patch16_plus_240,246.96,517.368,128,240,117.56 +efficientnetv2_m,246.55,256.379,64,320,54.14 +vit_relpos_base_patch16_plus_240,244.88,521.493,128,240,117.38 +seresnextaa101d_32x8d,243.89,522.629,128,224,93.59 +tf_efficientnet_b4_ap,242.14,262.218,64,380,19.34 +tf_efficientnet_b4,241.83,262.52,64,380,19.34 +tf_efficientnet_b4_ns,241.46,263.01,64,380,19.34 +xcit_medium_24_p16_224,241.39,526.926,128,224,84.4 +xcit_medium_24_p16_224_dist,241.08,527.466,128,224,84.4 +xcit_small_12_p16_384_dist,240.61,397.192,96,384,26.25 +vit_small_patch16_384,239.06,266.856,64,384,22.2 +volo_d2_224,238.89,400.019,96,224,58.68 +swinv2_tiny_window16_256,238.79,400.76,96,256,28.35 +mobilevitv2_150_384_in22ft1k,238.1,267.77,64,384,10.59 +vit_large_r50_s32_224,236.27,403.797,96,224,328.99 +tresnet_m,233.24,2192.365,512,224,31.39 +hrnet_w44,232.48,820.975,192,224,67.06 +poolformer_m48,232.43,410.471,96,224,73.47 +densenet264,231.27,411.12,96,224,72.69 +convit_base,231.06,552.947,128,224,86.54 +nf_regnet_b5,228.54,417.354,96,384,49.74 +deit3_small_patch16_384,226.74,281.318,64,384,22.21 +deit3_small_patch16_384_in21ft1k,226.44,281.652,64,384,22.21 +vit_small_r26_s32_384,226.15,281.726,64,384,36.47 +coat_mini,225.14,566.497,128,224,10.34 +efficientnetv2_rw_m,224.46,281.565,64,320,53.24 +swin_s3_base_224,224.0,425.728,96,224,71.13 +tnt_b_patch16_224,223.52,570.669,128,224,65.41 +hrnet_w64,223.29,568.417,128,224,128.06 +sequencer2d_l,220.03,286.022,64,224,54.3 +dpn131,216.53,588.962,128,224,79.25 +vit_base_r50_s16_224,215.49,443.851,96,224,98.66 +swinv2_cr_base_ns_224,214.73,444.647,96,224,87.88 +xception71,214.09,296.77,64,299,42.34 +swinv2_cr_base_224,213.21,447.81,96,224,87.88 +swinv2_small_window8_256,213.06,448.048,96,256,49.73 +nest_base,210.25,302.717,64,224,67.72 +jx_nest_base,209.06,304.441,64,224,67.72 +seresnet200d,203.53,467.209,96,256,71.86 +resnetrs200,201.84,471.293,96,256,93.21 +resnest50d,201.6,1268.493,256,224,27.48 +ecaresnet200d,201.55,472.938,96,256,64.69 +xcit_nano_12_p8_384_dist,201.45,315.854,64,384,3.05 +efficientnet_b3_gn,197.65,322.123,64,288,11.73 +xcit_tiny_24_p8_224_dist,195.37,488.075,96,224,12.11 +xcit_tiny_24_p8_224,195.11,488.622,96,224,12.11 +dpn107,194.08,492.913,96,224,86.92 +regnetz_c16_evos,193.89,328.188,64,256,13.49 +regnety_040,190.14,2017.916,384,224,20.65 +mobilevitv2_175_384_in22ft1k,189.6,336.534,64,384,14.25 +regnetv_040,188.29,1358.084,256,224,20.64 +convnext_large,187.93,509.087,96,224,197.77 +convnext_large_in22ft1k,187.83,509.365,96,224,197.77 +convmixer_768_32,187.17,511.603,96,224,21.11 +regnetx_080,181.41,1409.979,256,224,39.57 +resnest50d_4s2x40d,180.44,1417.38,256,224,30.42 +xcit_small_12_p8_224,179.5,354.768,64,224,26.21 +xcit_small_12_p8_224_dist,179.34,355.047,64,224,26.21 +halonet_h1,176.7,360.706,64,256,8.1 +tf_efficientnetv2_m_in21ft1k,175.14,270.794,48,384,54.14 +mobilevitv2_200_384_in22ft1k,175.13,273.08,48,384,18.45 +tf_efficientnetv2_m,173.37,273.617,48,384,54.14 +mixer_l16_224,171.41,558.471,96,224,208.2 +efficientnet_b3_g8_gn,168.79,377.376,64,288,14.25 +repvgg_b1g4,167.59,3053.943,512,224,39.97 +vit_large_patch32_384,167.04,573.058,96,384,306.63 +convnext_small_384_in22ft1k,165.65,384.557,64,384,50.22 +volo_d3_224,162.19,392.021,64,224,86.33 +regnetz_d8_evos,155.31,307.002,48,256,23.46 +swin_large_patch4_window7_224,153.79,414.289,64,224,196.53 +swinv2_base_window8_256,151.21,420.663,64,256,87.92 +convmixer_1024_20_ks9_p14,149.3,1713.726,256,224,24.38 +resnetv2_50x1_bitm,147.75,215.764,32,448,25.55 +seresnet269d,145.59,433.61,64,256,113.67 +resnetrs270,144.14,437.83,64,256,129.86 +swinv2_small_window16_256,143.52,443.487,64,256,49.73 +regnety_040s_gn,142.58,896.132,128,224,20.65 +repvgg_b2g4,133.72,3827.892,512,224,61.76 +eca_nfnet_l1,133.59,1435.413,192,256,41.41 +xcit_large_24_p16_224,132.6,479.222,64,224,189.1 +swinv2_cr_tiny_384,131.94,483.86,64,384,28.33 +xcit_large_24_p16_224_dist,131.66,482.65,64,224,189.1 +xcit_tiny_12_p8_384_dist,131.64,362.75,48,384,6.71 +regnetx_064,129.82,1970.916,256,224,26.21 +swinv2_cr_large_224,124.15,513.018,64,224,196.68 +xcit_small_24_p16_384_dist,120.64,394.328,48,384,47.67 +regnety_064,119.44,2141.523,256,224,30.58 +regnety_080,117.88,2170.37,256,224,39.18 +crossvit_15_dagger_408,117.86,269.618,32,408,28.5 +vit_large_patch16_224,117.2,544.512,64,224,304.33 +regnetv_064,117.03,1638.944,192,224,30.58 +ese_vovnet99b_iabn,117.02,3278.167,384,224,63.2 +convnext_xlarge_in22ft1k,116.37,548.167,64,224,350.2 +vit_base_patch16_18x2_224,116.0,548.972,64,224,256.73 +convnext_base_384_in22ft1k,115.58,413.454,48,384,88.59 +efficientnet_b5,113.63,279.129,32,456,30.39 +deit3_large_patch16_224_in21ft1k,112.51,567.041,64,224,304.37 +deit3_large_patch16_224,112.48,567.139,64,224,304.37 +tf_efficientnet_b5,111.42,284.665,32,456,30.39 +tf_efficientnet_b5_ap,111.14,285.451,32,456,30.39 +tf_efficientnet_b5_ns,111.14,285.33,32,456,30.39 +legacy_senet154,110.98,861.567,96,224,115.09 +senet154,110.82,862.828,96,224,115.09 +gluon_senet154,110.77,863.12,96,224,115.09 +beit_large_patch16_224,109.02,584.818,64,224,304.43 +repvgg_b3g4,108.77,3529.239,384,224,83.83 +regnetx_160,107.6,1783.261,192,224,54.28 +nfnet_f1,107.01,1791.907,192,224,132.63 +volo_d1_384,105.69,301.347,32,384,26.78 +swinv2_base_window16_256,103.88,459.56,48,256,87.92 +swinv2_base_window12to16_192to256_22kft1k,103.79,460.002,48,256,87.92 +tresnet_l,102.82,4975.916,512,224,55.99 +dm_nfnet_f1,101.59,1257.525,128,224,132.63 +volo_d4_224,101.08,472.359,48,224,192.96 +cait_xxs24_384,99.39,480.268,48,384,12.03 +ecaresnet269d,99.06,479.988,48,320,102.09 +efficientnetv2_l,98.76,319.521,32,384,118.52 +tf_efficientnetv2_l_in21ft1k,98.35,320.759,32,384,118.52 +tf_efficientnetv2_l,97.56,323.47,32,384,118.52 +deit_base_patch16_384,97.3,328.042,32,384,86.86 +vit_base_patch16_384,97.1,328.712,32,384,86.86 +resnest101e,96.09,1329.413,128,256,48.28 +deit_base_distilled_patch16_384,94.63,337.315,32,384,87.63 +regnetx_120,94.03,2721.558,256,224,46.11 +deit3_base_patch16_384,93.5,341.294,32,384,86.88 +deit3_base_patch16_384_in21ft1k,93.49,341.309,32,384,86.88 +xcit_small_24_p8_224_dist,92.61,514.968,48,224,47.63 +xcit_small_24_p8_224,92.51,515.466,48,224,47.63 +regnety_120,92.07,2083.952,192,224,51.82 +tresnet_xl,91.15,4209.119,384,224,78.44 +crossvit_18_dagger_408,89.17,356.787,32,408,44.61 +resnetv2_152x2_bit_teacher,89.16,356.538,32,224,236.34 +vit_large_patch14_224,85.06,562.673,48,224,304.2 +resnetv2_101x1_bitm,84.72,187.286,16,448,44.54 +resnetrs350,84.14,372.211,32,288,163.96 +beit_base_patch16_384,83.87,380.424,32,384,86.74 +regnety_160,83.24,2305.144,192,224,83.59 +pnasnet5large,83.16,380.801,32,331,86.06 +xcit_medium_24_p16_384_dist,82.74,383.266,32,384,84.4 +vit_large_r50_s32_384,77.34,411.186,32,384,329.09 +nasnetalarge,77.32,408.633,32,331,88.75 +swinv2_cr_small_384,76.42,416.277,32,384,49.7 +swin_base_patch4_window12_384,74.73,426.327,32,384,87.9 +resmlp_big_24_distilled_224,70.88,449.95,32,224,129.14 +resmlp_big_24_224_in22ft1k,70.88,449.933,32,224,129.14 +resmlp_big_24_224,70.41,452.99,32,224,129.14 +regnety_320,66.33,1928.357,128,224,145.05 +xcit_tiny_24_p8_384_dist,66.29,479.361,32,384,12.11 +cait_xs24_384,66.24,480.525,32,384,26.67 +ig_resnext101_32x16d,65.9,1455.165,96,224,194.03 +ssl_resnext101_32x16d,65.74,1458.688,96,224,194.03 +swsl_resnext101_32x16d,65.74,1458.738,96,224,194.03 +volo_d5_224,64.41,493.535,32,224,295.46 +cait_xxs36_384,64.34,493.602,32,384,17.37 +efficientnet_b6,64.08,246.77,16,528,43.04 +xcit_medium_24_p8_224,63.96,496.86,32,224,84.32 +xcit_medium_24_p8_224_dist,63.93,497.194,32,224,84.32 +convnext_large_384_in22ft1k,63.85,499.388,32,384,197.77 +vit_base_patch8_224,63.45,377.425,24,224,86.58 +tf_efficientnet_b6_ns,63.1,250.577,16,528,43.04 +tf_efficientnet_b6,62.84,251.669,16,528,43.04 +tf_efficientnet_b6_ap,62.76,252.073,16,528,43.04 +efficientnetv2_xl,62.18,251.438,16,384,208.12 +tf_efficientnetv2_xl_in21ft1k,62.14,251.721,16,384,208.12 +xcit_small_12_p8_384_dist,61.84,386.224,24,384,26.21 +vit_base_r50_s16_384,61.01,391.67,24,384,98.95 +vit_base_resnet50_384,60.98,391.903,24,384,98.95 +swinv2_large_window12to16_192to256_22kft1k,60.98,391.098,24,256,196.74 +eca_nfnet_l2,58.72,1632.112,96,320,56.72 +volo_d2_384,58.5,271.766,16,384,58.87 +resnetrs420,56.49,415.629,24,320,191.89 +swinv2_cr_base_384,55.08,433.269,24,384,87.88 +nfnet_f2,54.73,1750.573,96,256,193.78 +tresnet_m_448,53.52,3584.333,192,448,31.39 +dm_nfnet_f2,51.26,1245.084,64,256,193.78 +cait_s24_384,50.14,476.064,24,384,47.06 +swinv2_cr_huge_224,49.63,481.092,24,224,657.83 +regnetx_320,48.06,2662.024,128,224,107.81 +xcit_large_24_p16_384_dist,48.02,496.416,24,384,189.1 +swin_large_patch4_window12_384,41.75,381.31,16,384,196.74 +convnext_xlarge_384_in22ft1k,40.45,591.485,24,384,350.2 +deit3_huge_patch14_224_in21ft1k,38.41,414.036,16,224,632.13 +deit3_huge_patch14_224,38.4,414.103,16,224,632.13 +efficientnet_b7,37.97,207.232,8,600,66.35 +tf_efficientnet_b7_ap,37.27,210.986,8,600,66.35 +tf_efficientnet_b7_ns,37.25,211.249,8,600,66.35 +tf_efficientnet_b7,37.22,211.329,8,600,66.35 +eca_nfnet_l3,35.61,1344.526,48,352,72.04 +xcit_large_24_p8_224_dist,35.32,449.68,16,224,188.93 +xcit_large_24_p8_224,35.06,452.952,16,224,188.93 +resnetv2_50x3_bitm,34.68,460.605,16,448,217.32 +swinv2_cr_large_384,32.56,488.949,16,384,196.68 +cait_s36_384,32.3,491.641,16,384,68.37 +densenet264d_iabn,32.11,3982.48,128,224,72.74 +resnetv2_152x2_bit_teacher_384,31.17,382.508,12,384,236.34 +xcit_small_24_p8_384_dist,31.12,510.761,16,384,47.63 +resnest200e,30.3,1579.149,48,320,70.2 +vit_large_patch16_384,29.14,410.147,12,384,304.72 +deit3_large_patch16_384,28.26,422.768,12,384,304.76 +deit3_large_patch16_384_in21ft1k,28.25,422.94,12,384,304.76 +swinv2_base_window12to24_192to384_22kft1k,28.19,423.281,12,384,87.92 +nfnet_f3,26.1,1834.868,48,320,254.92 +beit_large_patch16_384,25.3,472.219,12,384,305.0 +tresnet_l_448,25.28,5060.321,128,448,55.99 +volo_d3_448,24.7,321.403,8,448,86.63 +dm_nfnet_f3,24.56,1297.967,32,320,254.92 +tresnet_xl_448,23.27,4122.323,96,448,78.44 +efficientnet_b8,22.93,257.589,6,672,87.41 +tf_efficientnet_b8,22.71,260.18,6,672,87.41 +tf_efficientnet_b8_ap,22.69,260.555,6,672,87.41 +resnetv2_152x2_bitm,22.58,351.774,8,448,236.34 +vit_giant_patch14_224,22.32,355.766,8,224,1012.61 +ig_resnext101_32x32d,21.03,1519.993,32,224,468.53 +xcit_medium_24_p8_384_dist,21.03,376.997,8,384,84.32 +convmixer_1536_20,20.83,2303.059,48,224,51.63 +resnetv2_101x3_bitm,18.05,441.706,8,448,387.93 +volo_d4_448,17.57,338.789,6,448,193.41 +swinv2_large_window12to24_192to384_22kft1k,16.62,358.548,6,384,196.74 +resnest269e,16.0,1493.085,24,416,110.93 +nfnet_f4,14.17,1687.74,24,384,316.07 +swinv2_cr_huge_384,13.13,454.627,6,384,657.94 +dm_nfnet_f4,12.96,1229.019,16,384,316.07 +xcit_large_24_p8_384_dist,12.19,488.758,6,384,188.93 +cait_m36_384,11.91,500.148,6,384,271.22 +volo_d5_448,11.43,346.654,4,448,295.91 +ig_resnext101_32x48d,11.2,1427.437,16,224,828.41 +tf_efficientnet_l2_ns_475,10.96,267.912,3,475,480.31 +dm_nfnet_f5,9.76,1222.345,12,416,377.21 +beit_large_patch16_512,9.42,422.548,4,512,305.67 +volo_d5_512,8.0,371.847,3,512,296.09 +nfnet_f5,8.0,1992.337,16,416,377.21 +dm_nfnet_f6,7.45,1065.231,8,448,438.36 +nfnet_f6,5.82,2052.248,12,448,438.36 +nfnet_f7,5.73,1387.07,8,480,499.5 +resnetv2_152x4_bitm,4.89,406.668,2,480,936.53 +cait_m48_448,4.76,414.936,2,448,356.46 +efficientnet_l2,3.95,247.515,1,800,480.31 +tf_efficientnet_l2_ns,3.93,248.975,1,800,480.31 diff --git a/pytorch-image-models/results/generate_csv_results.py b/pytorch-image-models/results/generate_csv_results.py new file mode 100644 index 0000000000000000000000000000000000000000..1a532819017d2c316efd5928a04d32b2c4dfde19 --- /dev/null +++ b/pytorch-image-models/results/generate_csv_results.py @@ -0,0 +1,79 @@ +import numpy as np +import pandas as pd + + +results = { + 'results-imagenet.csv': [ + 'results-imagenet-real.csv', + 'results-imagenetv2-matched-frequency.csv', + 'results-sketch.csv' + ], + 'results-imagenet-a-clean.csv': [ + 'results-imagenet-a.csv', + ], + 'results-imagenet-r-clean.csv': [ + 'results-imagenet-r.csv', + ], +} + + +def diff(base_df, test_csv): + base_df['mi'] = base_df.model + '-' + base_df.img_size.astype('str') + base_models = base_df['mi'].values + test_df = pd.read_csv(test_csv) + test_df['mi'] = test_df.model + '-' + test_df.img_size.astype('str') + test_models = test_df['mi'].values + + rank_diff = np.zeros_like(test_models, dtype='object') + top1_diff = np.zeros_like(test_models, dtype='object') + top5_diff = np.zeros_like(test_models, dtype='object') + + for rank, model in enumerate(test_models): + if model in base_models: + base_rank = int(np.where(base_models == model)[0]) + top1_d = test_df['top1'][rank] - base_df['top1'][base_rank] + top5_d = test_df['top5'][rank] - base_df['top5'][base_rank] + + # rank_diff + if rank == base_rank: + rank_diff[rank] = f'0' + elif rank > base_rank: + rank_diff[rank] = f'-{rank - base_rank}' + else: + rank_diff[rank] = f'+{base_rank - rank}' + + # top1_diff + if top1_d >= .0: + top1_diff[rank] = f'+{top1_d:.3f}' + else: + top1_diff[rank] = f'-{abs(top1_d):.3f}' + + # top5_diff + if top5_d >= .0: + top5_diff[rank] = f'+{top5_d:.3f}' + else: + top5_diff[rank] = f'-{abs(top5_d):.3f}' + + else: + rank_diff[rank] = '' + top1_diff[rank] = '' + top5_diff[rank] = '' + + test_df['top1_diff'] = top1_diff + test_df['top5_diff'] = top5_diff + test_df['rank_diff'] = rank_diff + + test_df.drop('mi', axis=1, inplace=True) + base_df.drop('mi', axis=1, inplace=True) + test_df['param_count'] = test_df['param_count'].map('{:,.2f}'.format) + test_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True) + test_df.to_csv(test_csv, index=False, float_format='%.3f') + + +for base_results, test_results in results.items(): + base_df = pd.read_csv(base_results) + base_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True) + for test_csv in test_results: + diff(base_df, test_csv) + base_df['param_count'] = base_df['param_count'].map('{:,.2f}'.format) + base_df.to_csv(base_results, index=False, float_format='%.3f') diff --git a/pytorch-image-models/results/model_metadata-in1k.csv b/pytorch-image-models/results/model_metadata-in1k.csv new file mode 100644 index 0000000000000000000000000000000000000000..7647ea5a9b332e60699e98750a9459bd60833e81 --- /dev/null +++ b/pytorch-image-models/results/model_metadata-in1k.csv @@ -0,0 +1,522 @@ +model,pretrain +adv_inception_v3,in1k-adv +bat_resnext26ts,in1k +beit_base_patch16_224,in21k-selfsl +beit_base_patch16_384,in21k-selfsl +beit_large_patch16_224,in21k-selfsl +beit_large_patch16_384,in21k-selfsl +beit_large_patch16_512,in21k-selfsl +botnet26t_256,in1k +cait_m36_384,in1k-dist +cait_m48_448,in1k-dist +cait_s24_224,in1k-dist +cait_s24_384,in1k-dist +cait_s36_384,in1k-dist +cait_xs24_384,in1k-dist +cait_xxs24_224,in1k-dist +cait_xxs24_384,in1k-dist +cait_xxs36_224,in1k-dist +cait_xxs36_384,in1k-dist +coat_lite_mini,in1k +coat_lite_small,in1k +coat_lite_tiny,in1k +coat_mini,in1k +coat_tiny,in1k +convit_base,in1k +convit_small,in1k +convit_tiny,in1k +convmixer_1024_20_ks9_p14,in1k +convmixer_1536_20,in1k +convmixer_768_32,in1k +crossvit_15_240,in1k +crossvit_15_dagger_240,in1k +crossvit_15_dagger_408,in1k +crossvit_18_240,in1k +crossvit_18_dagger_240,in1k +crossvit_18_dagger_408,in1k +crossvit_9_240,in1k +crossvit_9_dagger_240,in1k +crossvit_base_240,in1k +crossvit_small_240,in1k +crossvit_tiny_240,in1k +cspdarknet53,in1k +cspresnet50,in1k +cspresnext50,in1k +deit_base_distilled_patch16_224,in1k-dist +deit_base_distilled_patch16_384,in1k-dist +deit_base_patch16_224,in1k +deit_base_patch16_384,in1k +deit_small_distilled_patch16_224,in1k-dist +deit_small_patch16_224,in1k +deit_tiny_distilled_patch16_224,in1k-dist +deit_tiny_patch16_224,in1k +densenet121,in1k +densenet161,in1k +densenet169,in1k +densenet201,in1k +densenetblur121d,in1k +dla102,in1k +dla102x,in1k +dla102x2,in1k +dla169,in1k +dla34,in1k +dla46_c,in1k +dla46x_c,in1k +dla60,in1k +dla60_res2net,in1k +dla60_res2next,in1k +dla60x,in1k +dla60x_c,in1k +dm_nfnet_f0,in1k +dm_nfnet_f1,in1k +dm_nfnet_f2,in1k +dm_nfnet_f3,in1k +dm_nfnet_f4,in1k +dm_nfnet_f5,in1k +dm_nfnet_f6,in1k +dpn107,in1k +dpn131,in1k +dpn68,in1k +dpn68b,in1k +dpn92,in1k +dpn98,in1k +eca_botnext26ts_256,in1k +eca_halonext26ts,in1k +eca_nfnet_l0,in1k +eca_nfnet_l1,in1k +eca_nfnet_l2,in1k +eca_resnet33ts,in1k +eca_resnext26ts,in1k +ecaresnet101d,in1k +ecaresnet101d_pruned,in1k +ecaresnet269d,in1k +ecaresnet26t,in1k +ecaresnet50d,in1k +ecaresnet50d_pruned,in1k +ecaresnet50t,in1k +ecaresnetlight,in1k +efficientnet_b0,in1k +efficientnet_b1,in1k +efficientnet_b1_pruned,in1k +efficientnet_b2,in1k +efficientnet_b2_pruned,in1k +efficientnet_b3,in1k +efficientnet_b3_pruned,in1k +efficientnet_b4,in1k +efficientnet_el,in1k +efficientnet_el_pruned,in1k +efficientnet_em,in1k +efficientnet_es,in1k +efficientnet_es_pruned,in1k +efficientnet_lite0,in1k +efficientnetv2_rw_m,in1k +efficientnetv2_rw_s,in1k +efficientnetv2_rw_t,in1k +ens_adv_inception_resnet_v2,in1k-adv +ese_vovnet19b_dw,in1k +ese_vovnet39b,in1k +fbnetc_100,in1k +gc_efficientnetv2_rw_t,in1k +gcresnet33ts,in1k +gcresnet50t,in1k +gcresnext26ts,in1k +gcresnext50ts,in1k +gernet_l,in1k +gernet_m,in1k +gernet_s,in1k +ghostnet_100,in1k +gluon_inception_v3,in1k +gluon_resnet101_v1b,in1k +gluon_resnet101_v1c,in1k +gluon_resnet101_v1d,in1k +gluon_resnet101_v1s,in1k +gluon_resnet152_v1b,in1k +gluon_resnet152_v1c,in1k +gluon_resnet152_v1d,in1k +gluon_resnet152_v1s,in1k +gluon_resnet18_v1b,in1k +gluon_resnet34_v1b,in1k +gluon_resnet50_v1b,in1k +gluon_resnet50_v1c,in1k +gluon_resnet50_v1d,in1k +gluon_resnet50_v1s,in1k +gluon_resnext101_32x4d,in1k 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+resnet200d.ra2_in1k,256,96.310,3.690,99.250,0.750,64.69,0.950,bicubic +resnext101_32x8d.fb_wsl_ig1b_ft_in1k,224,96.300,3.700,99.420,0.580,88.79,0.875,bilinear +resnet101d.ra2_in1k,320,96.300,3.700,99.230,0.770,44.57,1.000,bicubic +deit3_base_patch16_224.fb_in1k,224,96.290,3.710,99.190,0.810,86.59,0.900,bicubic +tiny_vit_11m_224.dist_in22k_ft_in1k,224,96.290,3.710,99.190,0.810,11.00,0.950,bicubic +hgnetv2_b3.ssld_stage1_in22k_in1k,288,96.270,3.730,99.480,0.520,16.29,1.000,bicubic +regnetz_d8_evos.ch_in1k,256,96.270,3.730,99.340,0.660,23.46,0.950,bicubic +swin_s3_base_224.ms_in1k,224,96.270,3.730,99.160,0.840,71.13,0.900,bicubic +gcvit_small.in1k,224,96.270,3.730,99.150,0.850,51.09,0.875,bicubic +twins_svt_large.in1k,224,96.260,3.740,99.170,0.830,99.27,0.900,bicubic +inception_next_small.sail_in1k,224,96.250,3.750,99.230,0.770,49.37,0.875,bicubic +nextvit_base.bd_in1k,224,96.250,3.750,99.230,0.770,44.82,0.950,bicubic +eca_nfnet_l1.ra2_in1k,256,96.250,3.750,99.220,0.780,41.41,0.900,bicubic +nest_base_jx.goog_in1k,224,96.250,3.750,99.220,0.780,67.72,0.875,bicubic +pit_b_distilled_224.in1k,224,96.250,3.750,99.130,0.870,74.79,0.900,bicubic +convnext_tiny.fb_in22k_ft_in1k,224,96.240,3.760,99.340,0.660,28.59,0.875,bicubic +mobilenetv4_conv_large.e600_r384_in1k,448,96.240,3.760,99.310,0.690,32.59,1.000,bicubic +fastvit_sa36.apple_in1k,256,96.240,3.760,99.170,0.830,31.53,0.900,bicubic +maxvit_tiny_rw_224.sw_in1k,224,96.240,3.760,99.120,0.880,29.06,0.950,bicubic +swin_s3_small_224.ms_in1k,224,96.240,3.760,99.070,0.930,49.74,0.900,bicubic +hgnet_tiny.ssld_in1k,288,96.230,3.770,99.420,0.580,14.74,1.000,bicubic +regnetv_040.ra3_in1k,288,96.230,3.770,99.300,0.700,20.64,1.000,bicubic +vit_betwixt_patch16_reg1_gap_256.sbb_in1k,256,96.230,3.770,99.110,0.890,60.40,0.950,bicubic +ecaresnet101d.miil_in1k,288,96.220,3.780,99.310,0.690,44.57,0.950,bicubic +deit3_small_patch16_384.fb_in1k,384,96.220,3.780,99.280,0.720,22.21,1.000,bicubic +rexnetr_200.sw_in12k_ft_in1k,288,96.220,3.780,99.250,0.750,16.52,1.000,bicubic +tf_efficientnetv2_b3.in21k_ft_in1k,300,96.220,3.780,99.240,0.760,14.36,0.900,bicubic +resnet152.a1h_in1k,288,96.220,3.780,99.230,0.770,60.19,1.000,bicubic +xcit_small_24_p16_224.fb_dist_in1k,224,96.220,3.780,99.210,0.790,47.67,1.000,bicubic +swinv2_cr_small_ns_224.sw_in1k,224,96.220,3.780,99.130,0.870,49.70,0.900,bicubic +hgnetv2_b2.ssld_stage2_ft_in1k,288,96.210,3.790,99.420,0.580,11.22,1.000,bicubic +convnextv2_tiny.fcmae_ft_in1k,288,96.210,3.790,99.240,0.760,28.64,1.000,bicubic +xception65p.ra3_in1k,299,96.210,3.790,99.180,0.820,39.82,0.940,bicubic +regnetz_d32.ra3_in1k,256,96.200,3.800,99.280,0.720,27.58,0.950,bicubic +resnetrs200.tf_in1k,256,96.200,3.800,99.270,0.730,93.21,1.000,bicubic +mobilevitv2_175.cvnets_in22k_ft_in1k_384,384,96.200,3.800,99.130,0.870,14.25,1.000,bicubic +tiny_vit_21m_224.in1k,224,96.200,3.800,99.130,0.870,21.20,0.950,bicubic +convnext_small.fb_in1k,224,96.200,3.800,99.110,0.890,50.22,0.875,bicubic +gcvit_tiny.in1k,224,96.190,3.810,99.230,0.770,28.22,0.875,bicubic +focalnet_small_lrf.ms_in1k,224,96.190,3.810,99.190,0.810,50.34,0.900,bicubic +caformer_s18.sail_in1k,224,96.190,3.810,99.000,1.000,26.34,1.000,bicubic +repvit_m2_3.dist_300e_in1k,224,96.180,3.820,99.340,0.660,23.69,0.950,bicubic +twins_svt_base.in1k,224,96.180,3.820,99.050,0.950,56.07,0.900,bicubic +tf_efficientnet_b4.ap_in1k,380,96.170,3.830,99.280,0.720,19.34,0.922,bicubic +mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k,320,96.170,3.830,99.200,0.800,11.07,1.000,bicubic +deit_base_patch16_384.fb_in1k,384,96.170,3.830,99.150,0.850,86.86,1.000,bicubic +tresnet_v2_l.miil_in21k_ft_in1k,224,96.160,3.840,99.250,0.750,46.17,0.875,bilinear +fastvit_sa24.apple_dist_in1k,256,96.160,3.840,99.210,0.790,21.55,0.900,bicubic +efficientnet_b4.ra2_in1k,384,96.160,3.840,99.190,0.810,19.34,1.000,bicubic +twins_pcpvt_large.in1k,224,96.160,3.840,99.190,0.810,60.99,0.900,bicubic +efficientnetv2_rw_s.ra2_in1k,288,96.160,3.840,99.150,0.850,23.94,1.000,bicubic +regnetz_c16_evos.ch_in1k,320,96.150,3.850,99.380,0.620,13.49,0.950,bicubic +resnetv2_101.a1h_in1k,288,96.150,3.850,99.160,0.840,44.54,1.000,bicubic +mobilenetv4_conv_aa_large.e600_r384_in1k,384,96.150,3.850,99.110,0.890,32.59,0.950,bicubic +tf_efficientnetv2_s.in1k,300,96.150,3.850,99.090,0.910,21.46,1.000,bicubic +maxvit_tiny_tf_224.in1k,224,96.140,3.860,99.250,0.750,30.92,0.950,bicubic +sequencer2d_l.in1k,224,96.140,3.860,99.160,0.840,54.30,0.875,bicubic +nfnet_l0.ra2_in1k,288,96.130,3.870,99.240,0.760,35.07,1.000,bicubic +resnext101_64x4d.c1_in1k,288,96.130,3.870,99.230,0.770,83.46,1.000,bicubic +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,224,96.120,3.880,99.200,0.800,88.22,0.900,bicubic +xcit_medium_24_p8_224.fb_in1k,224,96.120,3.880,98.890,1.110,84.32,1.000,bicubic +efficientformer_l7.snap_dist_in1k,224,96.110,3.890,99.270,0.730,82.23,0.950,bicubic +xcit_small_12_p8_224.fb_in1k,224,96.110,3.890,99.170,0.830,26.21,1.000,bicubic +resnetv2_101x1_bit.goog_in21k_ft_in1k,448,96.100,3.900,99.280,0.720,44.54,1.000,bilinear +resnetv2_50x1_bit.goog_distilled_in1k,224,96.100,3.900,99.270,0.730,25.55,0.875,bicubic +deit_base_distilled_patch16_224.fb_in1k,224,96.100,3.900,99.190,0.810,87.34,0.900,bicubic +regnetv_064.ra3_in1k,224,96.090,3.910,99.310,0.690,30.58,0.950,bicubic +resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,224,96.090,3.910,99.310,0.690,236.34,0.875,bicubic +regnety_320.tv2_in1k,224,96.090,3.910,99.230,0.770,145.05,0.965,bicubic +deit3_medium_patch16_224.fb_in1k,224,96.090,3.910,99.190,0.810,38.85,0.900,bicubic +focalnet_small_srf.ms_in1k,224,96.090,3.910,99.160,0.840,49.89,0.900,bicubic +vit_betwixt_patch16_reg4_gap_256.sbb_in1k,256,96.090,3.910,99.150,0.850,60.40,0.950,bicubic +swin_base_patch4_window7_224.ms_in1k,224,96.090,3.910,99.040,0.960,87.77,0.900,bicubic +swinv2_cr_small_224.sw_in1k,224,96.090,3.910,98.870,1.130,49.70,0.900,bicubic +tf_efficientnet_b5.in1k,456,96.080,3.920,99.300,0.700,30.39,0.934,bicubic +resnet152d.ra2_in1k,256,96.070,3.930,99.300,0.700,60.21,0.950,bicubic +efficientformerv2_l.snap_dist_in1k,224,96.060,3.940,99.200,0.800,26.32,0.950,bicubic +cs3edgenet_x.c2_in1k,288,96.060,3.940,99.130,0.870,47.82,1.000,bicubic +mobilenetv4_hybrid_medium.ix_e550_r384_in1k,384,96.050,3.950,99.240,0.760,11.07,0.950,bicubic +convnextv2_nano.fcmae_ft_in22k_in1k,288,96.050,3.950,99.220,0.780,15.62,1.000,bicubic +regnety_160.deit_in1k,224,96.050,3.950,99.210,0.790,83.59,0.950,bicubic +xcit_tiny_12_p8_384.fb_dist_in1k,384,96.050,3.950,99.130,0.870,6.71,1.000,bicubic +resnet101.a1h_in1k,288,96.040,3.960,99.140,0.860,44.55,1.000,bicubic +cs3sedarknet_x.c2ns_in1k,288,96.040,3.960,99.110,0.890,35.40,1.000,bicubic +maxxvit_rmlp_nano_rw_256.sw_in1k,256,96.030,3.970,99.260,0.740,16.78,0.950,bicubic +resnext101_64x4d.tv_in1k,224,96.030,3.970,99.160,0.840,83.46,0.875,bilinear +regnety_080.ra3_in1k,224,96.030,3.970,99.110,0.890,39.18,0.950,bicubic +resnetrs152.tf_in1k,256,96.030,3.970,99.100,0.900,86.62,1.000,bicubic +mobilevitv2_200.cvnets_in22k_ft_in1k_384,384,96.030,3.970,99.090,0.910,18.45,1.000,bicubic +coatnet_1_rw_224.sw_in1k,224,96.030,3.970,99.050,0.950,41.72,0.950,bicubic +xcit_small_12_p16_224.fb_dist_in1k,224,96.020,3.980,99.150,0.850,26.25,1.000,bicubic +efficientvit_b3.r224_in1k,224,96.020,3.980,98.920,1.080,48.65,0.950,bicubic +convnext_tiny_hnf.a2h_in1k,288,96.010,3.990,99.080,0.920,28.59,1.000,bicubic +regnety_040.ra3_in1k,288,96.000,4.000,99.200,0.800,20.65,1.000,bicubic +pvt_v2_b3.in1k,224,96.000,4.000,99.190,0.810,45.24,0.900,bicubic +maxvit_rmlp_nano_rw_256.sw_in1k,256,96.000,4.000,98.960,1.040,15.50,0.950,bicubic +convnext_nano.in12k_ft_in1k,288,95.990,4.010,99.310,0.690,15.59,1.000,bicubic +xcit_tiny_24_p16_384.fb_dist_in1k,384,95.990,4.010,99.230,0.770,12.12,1.000,bicubic +regnety_032.ra_in1k,288,95.990,4.010,99.190,0.810,19.44,1.000,bicubic +regnetx_320.tv2_in1k,224,95.990,4.010,99.100,0.900,107.81,0.965,bicubic +resnext101_32x8d.tv2_in1k,224,95.990,4.010,99.090,0.910,88.79,0.965,bilinear +sequencer2d_s.in1k,224,95.990,4.010,99.050,0.950,27.65,0.875,bicubic +hrnet_w18_ssld.paddle_in1k,288,95.980,4.020,99.290,0.710,21.30,1.000,bilinear +hgnetv2_b3.ssld_stage1_in22k_in1k,224,95.980,4.020,99.280,0.720,16.29,0.965,bicubic +efficientvit_b2.r288_in1k,288,95.980,4.020,99.200,0.800,24.33,1.000,bicubic +regnety_160.tv2_in1k,224,95.980,4.020,99.160,0.840,83.59,0.965,bicubic +swinv2_tiny_window16_256.ms_in1k,256,95.980,4.020,99.150,0.850,28.35,0.900,bicubic +regnetz_c16.ra3_in1k,320,95.980,4.020,99.100,0.900,13.46,1.000,bicubic +eca_nfnet_l0.ra2_in1k,288,95.970,4.030,99.210,0.790,24.14,1.000,bicubic +tresnet_xl.miil_in1k_448,448,95.970,4.030,99.120,0.880,78.44,0.875,bilinear +nest_small_jx.goog_in1k,224,95.970,4.030,99.040,0.960,38.35,0.875,bicubic +tf_efficientnet_b4.aa_in1k,380,95.950,4.050,99.170,0.830,19.34,0.922,bicubic +coatnet_rmlp_1_rw_224.sw_in1k,224,95.950,4.050,99.160,0.840,41.69,0.950,bicubic +cs3se_edgenet_x.c2ns_in1k,256,95.950,4.050,99.120,0.880,50.72,0.950,bicubic +convformer_s18.sail_in1k,224,95.950,4.050,98.890,1.110,26.77,1.000,bicubic +repvit_m1_5.dist_450e_in1k,224,95.930,4.070,99.160,0.840,14.64,0.950,bicubic +maxvit_nano_rw_256.sw_in1k,256,95.930,4.070,99.000,1.000,15.45,0.950,bicubic +hgnetv2_b3.ssld_stage2_ft_in1k,224,95.920,4.080,99.280,0.720,16.29,0.965,bicubic +coat_small.in1k,224,95.920,4.080,99.160,0.840,21.69,0.900,bicubic +fastvit_sa24.apple_in1k,256,95.910,4.090,99.160,0.840,21.55,0.900,bicubic +resnet101d.ra2_in1k,256,95.900,4.100,99.150,0.850,44.57,0.950,bicubic +convnextv2_nano.fcmae_ft_in1k,288,95.900,4.100,99.110,0.890,15.62,1.000,bicubic +regnety_080_tv.tv2_in1k,224,95.900,4.100,99.110,0.890,39.38,0.965,bicubic +regnetx_160.tv2_in1k,224,95.900,4.100,99.080,0.920,54.28,0.965,bicubic +mobilenetv4_conv_large.e500_r256_in1k,320,95.890,4.110,99.120,0.880,32.59,1.000,bicubic +tresnet_l.miil_in1k_448,448,95.890,4.110,99.110,0.890,55.99,0.875,bilinear +resnet152.a1h_in1k,224,95.890,4.110,99.080,0.920,60.19,0.950,bicubic +maxxvitv2_nano_rw_256.sw_in1k,256,95.890,4.110,99.040,0.960,23.70,0.950,bicubic +resnext50_32x4d.fb_swsl_ig1b_ft_in1k,224,95.880,4.120,99.250,0.750,25.03,0.875,bilinear +resnet51q.ra2_in1k,288,95.880,4.120,99.110,0.890,35.70,1.000,bilinear +mvitv2_tiny.fb_in1k,224,95.880,4.120,99.090,0.910,24.17,0.900,bicubic +swin_small_patch4_window7_224.ms_in1k,224,95.880,4.120,99.020,0.980,49.61,0.900,bicubic +cs3darknet_x.c2ns_in1k,288,95.870,4.130,99.190,0.810,35.05,1.000,bicubic +regnety_064.ra3_in1k,224,95.870,4.130,99.140,0.860,30.58,0.950,bicubic +cait_xxs36_384.fb_dist_in1k,384,95.870,4.130,99.090,0.910,17.37,1.000,bicubic +nextvit_small.bd_in1k,224,95.870,4.130,99.080,0.920,31.76,0.950,bicubic +resnest101e.in1k,256,95.850,4.150,99.210,0.790,48.28,0.875,bilinear +resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,224,95.850,4.150,99.170,0.830,194.03,0.875,bilinear +vit_large_patch32_384.orig_in21k_ft_in1k,384,95.840,4.160,99.160,0.840,306.63,1.000,bicubic +rexnet_300.nav_in1k,224,95.830,4.170,99.130,0.870,34.71,0.875,bicubic +sequencer2d_m.in1k,224,95.820,4.180,99.110,0.890,38.31,0.875,bicubic +mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k,256,95.820,4.180,99.100,0.900,11.07,0.950,bicubic +tf_efficientnet_b4.in1k,380,95.820,4.180,99.060,0.940,19.34,0.922,bicubic +hgnetv2_b2.ssld_stage1_in22k_in1k,288,95.810,4.190,99.270,0.730,11.22,1.000,bicubic +resnetaa50d.sw_in12k_ft_in1k,288,95.810,4.190,99.180,0.820,25.58,1.000,bicubic +rexnetr_200.sw_in12k_ft_in1k,224,95.810,4.190,99.170,0.830,16.52,0.950,bicubic +xcit_tiny_24_p8_224.fb_dist_in1k,224,95.800,4.200,99.210,0.790,12.11,1.000,bicubic +mobilenetv4_conv_large.e600_r384_in1k,384,95.800,4.200,99.060,0.940,32.59,0.950,bicubic +resnet61q.ra2_in1k,288,95.800,4.200,99.000,1.000,36.85,1.000,bicubic +twins_pcpvt_base.in1k,224,95.790,4.210,99.130,0.870,43.83,0.900,bicubic +convnext_tiny.fb_in1k,288,95.780,4.220,99.150,0.850,28.59,1.000,bicubic +mobilenetv4_hybrid_medium.ix_e550_r256_in1k,320,95.770,4.230,99.150,0.850,11.07,1.000,bicubic +vit_relpos_base_patch16_clsgap_224.sw_in1k,224,95.770,4.230,99.040,0.960,86.43,0.900,bicubic +efficientnet_b4.ra2_in1k,320,95.760,4.240,99.160,0.840,19.34,0.875,bicubic +regnetv_040.ra3_in1k,224,95.750,4.250,99.220,0.780,20.64,0.950,bicubic +tf_efficientnet_b2.ns_jft_in1k,260,95.750,4.250,99.120,0.880,9.11,0.890,bicubic +poolformerv2_m48.sail_in1k,224,95.750,4.250,98.980,1.020,73.35,1.000,bicubic +ecaresnet101d_pruned.miil_in1k,288,95.740,4.260,99.180,0.820,24.88,0.950,bicubic +vit_little_patch16_reg4_gap_256.sbb_in1k,256,95.740,4.260,99.120,0.880,22.52,0.950,bicubic +gc_efficientnetv2_rw_t.agc_in1k,288,95.740,4.260,99.020,0.980,13.68,1.000,bicubic +tresnet_m.miil_in21k_ft_in1k,224,95.730,4.270,99.030,0.970,31.39,0.875,bilinear +seresnext50_32x4d.racm_in1k,288,95.720,4.280,99.190,0.810,27.56,0.950,bicubic +efficientnet_b3.ra2_in1k,320,95.720,4.280,99.040,0.960,12.23,1.000,bicubic +pnasnet5large.tf_in1k,331,95.720,4.280,98.920,1.080,86.06,0.911,bicubic +hgnet_tiny.ssld_in1k,224,95.710,4.290,99.260,0.740,14.74,0.965,bicubic +convnext_nano.in12k_ft_in1k,224,95.710,4.290,99.250,0.750,15.59,0.950,bicubic +resnext101_64x4d.c1_in1k,224,95.710,4.290,99.030,0.970,83.46,0.950,bicubic +regnetz_c16_evos.ch_in1k,256,95.700,4.300,99.230,0.770,13.49,0.950,bicubic 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+levit_conv_384.fb_dist_in1k,224,95.540,4.460,99.050,0.950,39.13,0.900,bicubic +convnext_tiny.fb_in1k,224,95.540,4.460,99.000,1.000,28.59,0.875,bicubic +xcit_small_24_p16_224.fb_in1k,224,95.540,4.460,98.760,1.240,47.67,1.000,bicubic +convnextv2_nano.fcmae_ft_in22k_in1k,224,95.530,4.470,99.120,0.880,15.62,0.875,bicubic +resnet152.tv2_in1k,224,95.530,4.470,98.960,1.040,60.19,0.965,bilinear +ecaresnet50t.ra2_in1k,320,95.520,4.480,99.120,0.880,25.57,0.950,bicubic +vit_relpos_medium_patch16_rpn_224.sw_in1k,224,95.520,4.480,99.080,0.920,38.73,0.900,bicubic +convnext_tiny_hnf.a2h_in1k,224,95.520,4.480,99.010,0.990,28.59,0.950,bicubic +regnetz_c16.ra3_in1k,256,95.520,4.480,98.970,1.030,13.46,0.940,bicubic +flexivit_small.300ep_in1k,240,95.520,4.480,98.960,1.040,22.06,0.950,bicubic +xcit_medium_24_p16_224.fb_in1k,224,95.520,4.480,98.760,1.240,84.40,1.000,bicubic +cs3edgenet_x.c2_in1k,256,95.510,4.490,99.030,0.970,47.82,0.887,bicubic +swinv2_tiny_window8_256.ms_in1k,256,95.500,4.500,99.120,0.880,28.35,0.900,bicubic +fbnetv3_g.ra2_in1k,288,95.500,4.500,98.990,1.010,16.62,0.950,bilinear +pvt_v2_b2.in1k,224,95.500,4.500,98.990,1.010,25.36,0.900,bicubic +resnetv2_50d_gn.ah_in1k,288,95.500,4.500,98.980,1.020,25.57,1.000,bicubic +visformer_small.in1k,224,95.500,4.500,98.900,1.100,40.22,0.900,bicubic +crossvit_base_240.in1k,240,95.500,4.500,98.820,1.180,105.03,0.875,bicubic +swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,224,95.490,4.510,99.220,0.780,28.29,0.900,bicubic +focalnet_tiny_srf.ms_in1k,224,95.490,4.510,99.140,0.860,28.43,0.900,bicubic +resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,224,95.490,4.510,99.110,0.890,88.79,0.875,bilinear +ecaresnet50d.miil_in1k,288,95.490,4.510,99.090,0.910,25.58,0.950,bicubic +resnet51q.ra2_in1k,256,95.490,4.510,98.990,1.010,35.70,0.875,bilinear +vit_relpos_medium_patch16_cls_224.sw_in1k,224,95.490,4.510,98.950,1.050,38.76,0.900,bicubic 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+xcit_large_24_p16_224.fb_in1k,224,95.460,4.540,98.630,1.370,189.10,1.000,bicubic +tresnet_xl.miil_in1k,224,95.440,4.560,99.060,0.940,78.44,0.875,bilinear +coatnet_rmlp_nano_rw_224.sw_in1k,224,95.440,4.560,98.990,1.010,15.15,0.900,bicubic +deit_base_patch16_224.fb_in1k,224,95.440,4.560,98.840,1.160,86.57,0.900,bicubic +resnext50_32x4d.a1h_in1k,288,95.440,4.560,98.840,1.160,25.03,1.000,bicubic +nfnet_l0.ra2_in1k,224,95.430,4.570,99.090,0.910,35.07,0.900,bicubic +convnextv2_nano.fcmae_ft_in1k,224,95.430,4.570,98.980,1.020,15.62,0.875,bicubic +poolformerv2_m36.sail_in1k,224,95.430,4.570,98.880,1.120,56.08,1.000,bicubic +xcit_small_12_p16_224.fb_in1k,224,95.430,4.570,98.840,1.160,26.25,1.000,bicubic +edgenext_small.usi_in1k,320,95.420,4.580,99.100,0.900,5.59,1.000,bicubic +resnetrs101.tf_in1k,288,95.420,4.580,99.030,0.970,63.62,0.940,bicubic +halo2botnet50ts_256.a1h_in1k,256,95.420,4.580,99.020,0.980,22.64,0.950,bicubic +resnet101.a2_in1k,288,95.420,4.580,98.930,1.070,44.55,1.000,bicubic +vit_small_patch16_224.augreg_in21k_ft_in1k,224,95.410,4.590,99.130,0.870,22.05,0.900,bicubic +cs3darknet_x.c2ns_in1k,256,95.410,4.590,99.020,0.980,35.05,0.950,bicubic +coatnext_nano_rw_224.sw_in1k,224,95.410,4.590,99.000,1.000,14.70,0.900,bicubic +poolformer_m36.sail_in1k,224,95.410,4.590,98.840,1.160,56.17,0.950,bicubic +regnety_032.ra_in1k,224,95.400,4.600,99.080,0.920,19.44,0.950,bicubic +vit_base_patch16_rpn_224.sw_in1k,224,95.390,4.610,98.950,1.050,86.54,0.900,bicubic +efficientformerv2_s2.snap_dist_in1k,224,95.380,4.620,98.950,1.050,12.71,0.950,bicubic +dm_nfnet_f0.dm_in1k,192,95.380,4.620,98.920,1.080,71.49,0.900,bicubic +resnet101.a1h_in1k,224,95.370,4.630,98.860,1.140,44.55,0.950,bicubic +mobilenetv4_hybrid_medium.ix_e550_r256_in1k,256,95.370,4.630,98.810,1.190,11.07,0.950,bicubic +hrnet_w18_ssld.paddle_in1k,224,95.360,4.640,99.070,0.930,21.30,0.950,bilinear +resnext101_32x8d.tv2_in1k,176,95.360,4.640,99.050,0.950,88.79,0.875,bilinear +resnetaa50d.sw_in12k_ft_in1k,224,95.360,4.640,98.980,1.020,25.58,0.950,bicubic +mixer_b16_224.miil_in21k_ft_in1k,224,95.360,4.640,98.870,1.130,59.88,0.875,bilinear +ecaresnet50t.a2_in1k,288,95.350,4.650,98.930,1.070,25.57,1.000,bicubic +tf_efficientnet_b3.ap_in1k,300,95.350,4.650,98.900,1.100,12.23,0.904,bicubic +convnext_nano.d1h_in1k,288,95.350,4.650,98.850,1.150,15.59,1.000,bicubic +vit_base_patch16_224.orig_in21k_ft_in1k,224,95.340,4.660,99.010,0.990,86.57,0.900,bicubic +tf_efficientnetv2_b3.in21k_ft_in1k,240,95.340,4.660,98.970,1.030,14.36,0.900,bicubic +resnet61q.ra2_in1k,256,95.340,4.660,98.900,1.100,36.85,0.900,bicubic +seresnet50.ra2_in1k,288,95.330,4.670,99.010,0.990,28.09,0.950,bicubic +swinv2_cr_tiny_ns_224.sw_in1k,224,95.330,4.670,98.940,1.060,28.33,0.900,bicubic +poolformerv2_s36.sail_in1k,224,95.330,4.670,98.900,1.100,30.79,1.000,bicubic +cs3sedarknet_l.c2ns_in1k,288,95.320,4.680,99.140,0.860,21.91,0.950,bicubic +resnet50d.ra4_e3600_r224_in1k,288,95.320,4.680,98.970,1.030,25.58,1.000,bicubic +efficientnet_b1.ra4_e3600_r240_in1k,288,95.320,4.680,98.820,1.180,7.79,1.000,bicubic +efficientvit_b2.r224_in1k,224,95.310,4.690,98.790,1.210,24.33,0.950,bicubic +regnety_032.tv2_in1k,224,95.300,4.700,98.930,1.070,19.44,0.965,bicubic +eca_nfnet_l0.ra2_in1k,224,95.290,4.710,99.100,0.900,24.14,0.900,bicubic +ecaresnetlight.miil_in1k,288,95.290,4.710,99.040,0.960,30.16,0.950,bicubic +vit_small_patch16_384.augreg_in1k,384,95.290,4.710,99.010,0.990,22.20,1.000,bicubic +resnet101.tv2_in1k,224,95.290,4.710,98.930,1.070,44.55,0.965,bilinear +resnet50_gn.a1h_in1k,288,95.280,4.720,99.000,1.000,25.56,0.950,bicubic +gcresnet50t.ra2_in1k,288,95.280,4.720,98.910,1.090,25.90,1.000,bicubic +tresnet_l.miil_in1k,224,95.270,4.730,99.010,0.990,55.99,0.875,bilinear +nest_tiny_jx.goog_in1k,224,95.260,4.740,98.970,1.030,17.06,0.875,bicubic +cait_xxs24_384.fb_dist_in1k,384,95.260,4.740,98.960,1.040,12.03,1.000,bicubic +convnextv2_pico.fcmae_ft_in1k,288,95.250,4.750,98.930,1.070,9.07,0.950,bicubic +mobilevitv2_175.cvnets_in22k_ft_in1k,256,95.250,4.750,98.800,1.200,14.25,0.888,bicubic +repvit_m3.dist_in1k,224,95.240,4.760,99.060,0.940,10.68,0.950,bicubic +vit_srelpos_medium_patch16_224.sw_in1k,224,95.240,4.760,98.980,1.020,38.74,0.900,bicubic +resnetaa50.a1h_in1k,288,95.240,4.760,98.910,1.090,25.56,1.000,bicubic +coatnet_nano_rw_224.sw_in1k,224,95.240,4.760,98.870,1.130,15.14,0.900,bicubic +twins_pcpvt_small.in1k,224,95.230,4.770,98.880,1.120,24.11,0.900,bicubic +hgnetv2_b2.ssld_stage1_in22k_in1k,224,95.220,4.780,99.080,0.920,11.22,0.965,bicubic +twins_svt_small.in1k,224,95.220,4.780,98.880,1.120,24.06,0.900,bicubic +efficientnet_b3.ra2_in1k,288,95.190,4.810,99.040,0.960,12.23,0.875,bicubic +swin_s3_tiny_224.ms_in1k,224,95.190,4.810,98.960,1.040,28.33,0.900,bicubic +vit_relpos_small_patch16_224.sw_in1k,224,95.190,4.810,98.950,1.050,21.98,0.900,bicubic +convit_small.fb_in1k,224,95.190,4.810,98.930,1.070,27.78,0.875,bicubic +pit_s_distilled_224.in1k,224,95.180,4.820,98.880,1.120,24.04,0.900,bicubic +tf_efficientnetv2_b3.in1k,300,95.180,4.820,98.830,1.170,14.36,0.904,bicubic +regnetz_b16.ra3_in1k,288,95.170,4.830,99.080,0.920,9.72,1.000,bicubic +mobilevitv2_200.cvnets_in22k_ft_in1k,256,95.170,4.830,98.920,1.080,18.45,0.888,bicubic +swin_tiny_patch4_window7_224.ms_in1k,224,95.170,4.830,98.860,1.140,28.29,0.900,bicubic +xcit_tiny_12_p16_384.fb_dist_in1k,384,95.160,4.840,99.000,1.000,6.72,1.000,bicubic +cs3darknet_focus_l.c2ns_in1k,288,95.160,4.840,98.960,1.040,21.15,0.950,bicubic +lamhalobotnet50ts_256.a1h_in1k,256,95.160,4.840,98.850,1.150,22.57,0.950,bicubic +tf_efficientnet_b1.ns_jft_in1k,240,95.150,4.850,99.090,0.910,7.79,0.882,bicubic +mobilevitv2_150.cvnets_in22k_ft_in1k,256,95.150,4.850,98.830,1.170,10.59,0.888,bicubic +cs3darknet_l.c2ns_in1k,288,95.140,4.860,98.990,1.010,21.16,0.950,bicubic +halonet50ts.a1h_in1k,256,95.140,4.860,98.780,1.220,22.73,0.940,bicubic +resnet152.a1_in1k,224,95.140,4.860,98.410,1.590,60.19,0.950,bicubic +crossvit_15_240.in1k,240,95.130,4.870,98.940,1.060,27.53,0.875,bicubic +fastvit_sa12.apple_dist_in1k,256,95.130,4.870,98.810,1.190,11.58,0.900,bicubic +efficientnet_el.ra_in1k,300,95.120,4.880,98.990,1.010,10.59,0.904,bicubic +vit_base_patch32_clip_224.openai_ft_in1k,224,95.120,4.880,98.990,1.010,88.22,0.900,bicubic +hgnet_small.paddle_in1k,224,95.110,4.890,99.030,0.970,24.36,0.965,bicubic +ecaresnet50d_pruned.miil_in1k,288,95.110,4.890,98.930,1.070,19.94,0.950,bicubic +gernet_l.idstcv_in1k,256,95.110,4.890,98.910,1.090,31.08,0.875,bilinear +poolformer_s36.sail_in1k,224,95.110,4.890,98.900,1.100,30.86,0.900,bicubic +regnetx_080.tv2_in1k,224,95.110,4.890,98.810,1.190,39.57,0.965,bicubic +convnext_nano_ols.d1h_in1k,288,95.110,4.890,98.710,1.290,15.65,1.000,bicubic +legacy_senet154.in1k,224,95.100,4.900,98.830,1.170,115.09,0.875,bilinear +mobilenetv4_conv_large.e500_r256_in1k,256,95.090,4.910,98.950,1.050,32.59,0.950,bicubic +xcit_tiny_12_p8_224.fb_dist_in1k,224,95.090,4.910,98.930,1.070,6.71,1.000,bicubic +convmixer_1536_20.in1k,224,95.080,4.920,99.030,0.970,51.63,0.960,bicubic +wide_resnet50_2.racm_in1k,224,95.080,4.920,98.970,1.030,68.88,0.875,bicubic +ecaresnet101d_pruned.miil_in1k,224,95.070,4.930,98.990,1.010,24.88,0.875,bicubic +vit_small_patch32_384.augreg_in21k_ft_in1k,384,95.070,4.930,98.980,1.020,22.92,1.000,bicubic +vit_srelpos_small_patch16_224.sw_in1k,224,95.070,4.930,98.950,1.050,21.97,0.900,bicubic +resnet152s.gluon_in1k,224,95.060,4.940,98.930,1.070,60.32,0.875,bicubic +tnt_s_patch16_224,224,95.060,4.940,98.840,1.160,23.76,0.900,bicubic +tiny_vit_5m_224.dist_in22k_ft_in1k,224,95.050,4.950,98.980,1.020,5.39,0.950,bicubic +seresnet33ts.ra2_in1k,288,95.040,4.960,98.900,1.100,19.78,1.000,bicubic +gc_efficientnetv2_rw_t.agc_in1k,224,95.040,4.960,98.840,1.160,13.68,1.000,bicubic +resnetv2_50x1_bit.goog_in21k_ft_in1k,448,95.030,4.970,99.060,0.940,25.55,1.000,bilinear +seresnext50_32x4d.racm_in1k,224,95.030,4.970,98.890,1.110,27.56,0.875,bicubic +levit_256.fb_dist_in1k,224,95.030,4.970,98.870,1.130,18.89,0.900,bicubic +levit_conv_256.fb_dist_in1k,224,95.030,4.970,98.870,1.130,18.89,0.900,bicubic +fbnetv3_g.ra2_in1k,240,95.030,4.970,98.800,1.200,16.62,0.950,bilinear +vit_base_patch32_224.augreg_in21k_ft_in1k,224,95.010,4.990,99.020,0.980,88.22,0.900,bicubic +resnet50d.ra2_in1k,288,95.010,4.990,98.970,1.030,25.58,0.950,bicubic +tf_efficientnet_b3.aa_in1k,300,95.010,4.990,98.910,1.090,12.23,0.904,bicubic +mobilenetv4_conv_medium.e500_r256_in1k,320,95.000,5.000,98.900,1.100,9.72,1.000,bicubic +coat_mini.in1k,224,95.000,5.000,98.780,1.220,10.34,0.900,bicubic +deit3_small_patch16_224.fb_in1k,224,95.000,5.000,98.460,1.540,22.06,0.900,bicubic +tresnet_m.miil_in1k_448,448,94.980,5.020,98.980,1.020,31.39,0.875,bilinear +efficientnetv2_rw_t.ra2_in1k,224,94.980,5.020,98.750,1.250,13.65,1.000,bicubic +rexnet_200.nav_in1k,224,94.970,5.030,98.990,1.010,16.37,0.875,bicubic +resnetv2_50d_evos.ah_in1k,224,94.970,5.030,98.750,1.250,25.59,0.950,bicubic +resnest50d_4s2x40d.in1k,224,94.960,5.040,99.070,0.930,30.42,0.875,bicubic +hgnet_tiny.paddle_in1k,288,94.960,5.040,99.060,0.940,14.74,1.000,bicubic +vit_base_patch16_384.augreg_in1k,384,94.960,5.040,98.910,1.090,86.86,1.000,bicubic +eva02_tiny_patch14_336.mim_in22k_ft_in1k,336,94.960,5.040,98.870,1.130,5.76,1.000,bicubic +mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k,256,94.960,5.040,98.820,1.180,8.46,0.950,bicubic +resnet50.d_in1k,288,94.960,5.040,98.820,1.180,25.56,1.000,bicubic +mobilenet_edgetpu_v2_m,256,94.960,5.040,98.810,1.190,8.46,0.950,bicubic +edgenext_small.usi_in1k,256,94.950,5.050,98.960,1.040,5.59,0.950,bicubic +resnet152.a2_in1k,224,94.950,5.050,98.570,1.430,60.19,0.950,bicubic +seresnext101_64x4d.gluon_in1k,224,94.940,5.060,98.800,1.200,88.23,0.875,bicubic +senet154.gluon_in1k,224,94.930,5.070,98.770,1.230,115.09,0.875,bicubic +convnext_nano.d1h_in1k,224,94.930,5.070,98.670,1.330,15.59,0.950,bicubic +gcresnet33ts.ra2_in1k,288,94.920,5.080,98.880,1.120,19.88,1.000,bicubic +seresnext101_32x4d.gluon_in1k,224,94.920,5.080,98.780,1.220,48.96,0.875,bicubic +tf_efficientnet_lite4.in1k,380,94.890,5.110,99.020,0.980,13.01,0.920,bilinear +repvit_m1_1.dist_450e_in1k,224,94.890,5.110,98.920,1.080,8.80,0.950,bicubic +ese_vovnet39b.ra_in1k,288,94.890,5.110,98.910,1.090,24.57,0.950,bicubic +fastvit_sa12.apple_in1k,256,94.890,5.110,98.860,1.140,11.58,0.900,bicubic +resnet50.c2_in1k,288,94.890,5.110,98.810,1.190,25.56,1.000,bicubic +resmlp_36_224.fb_distilled_in1k,224,94.880,5.120,98.870,1.130,44.69,0.875,bicubic +wide_resnet50_2.tv2_in1k,224,94.870,5.130,98.960,1.040,68.88,0.965,bilinear +cs3sedarknet_l.c2ns_in1k,256,94.870,5.130,98.900,1.100,21.91,0.887,bicubic +mobilevitv2_175.cvnets_in1k,256,94.870,5.130,98.870,1.130,14.25,0.888,bicubic +mobilenetv4_conv_blur_medium.e500_r224_in1k,256,94.870,5.130,98.810,1.190,9.72,1.000,bicubic +fastvit_s12.apple_dist_in1k,256,94.870,5.130,98.770,1.230,9.47,0.900,bicubic +convnext_tiny.fb_in22k_ft_in1k,288,94.870,5.130,98.550,1.450,28.59,1.000,bicubic +gcresnext50ts.ch_in1k,288,94.860,5.140,98.850,1.150,15.67,1.000,bicubic +seresnet33ts.ra2_in1k,256,94.860,5.140,98.810,1.190,19.78,0.900,bicubic +resnet50.b1k_in1k,288,94.860,5.140,98.800,1.200,25.56,1.000,bicubic +gcresnet50t.ra2_in1k,256,94.860,5.140,98.790,1.210,25.90,0.900,bicubic +mobilevitv2_200.cvnets_in1k,256,94.860,5.140,98.720,1.280,18.45,0.888,bicubic +mobilenetv4_hybrid_medium.e500_r224_in1k,224,94.860,5.140,98.680,1.320,11.07,0.950,bicubic +crossvit_small_240.in1k,240,94.850,5.150,99.020,0.980,26.86,0.875,bicubic +resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,224,94.850,5.150,98.880,1.120,25.03,0.875,bilinear +res2net101d.in1k,224,94.840,5.160,98.780,1.220,45.23,0.875,bilinear +efficientnet_b1.ra4_e3600_r240_in1k,240,94.840,5.160,98.710,1.290,7.79,0.900,bicubic +resnext50_32x4d.a1_in1k,288,94.840,5.160,98.620,1.380,25.03,1.000,bicubic +lambda_resnet50ts.a1h_in1k,256,94.840,5.160,98.470,1.530,21.54,0.950,bicubic +ecaresnet50t.ra2_in1k,256,94.830,5.170,98.910,1.090,25.57,0.875,bicubic +resnest50d.in1k,224,94.830,5.170,98.870,1.130,27.48,0.875,bilinear +resnetv2_50.a1h_in1k,288,94.830,5.170,98.870,1.130,25.55,1.000,bicubic +cspresnext50.ra_in1k,256,94.830,5.170,98.730,1.270,20.57,0.887,bilinear +resnet152d.gluon_in1k,224,94.820,5.180,98.760,1.240,60.21,0.875,bicubic +resnet50.a1_in1k,288,94.800,5.200,98.750,1.250,25.56,1.000,bicubic +vit_wee_patch16_reg1_gap_256.sbb_in1k,256,94.790,5.210,98.940,1.060,13.42,0.950,bicubic +resnet50.c1_in1k,288,94.790,5.210,98.920,1.080,25.56,1.000,bicubic +sehalonet33ts.ra2_in1k,256,94.790,5.210,98.570,1.430,13.69,0.940,bicubic +resnet50.a1h_in1k,224,94.770,5.230,98.680,1.320,25.56,1.000,bicubic +resnest50d_1s4x24d.in1k,224,94.760,5.240,98.980,1.020,25.68,0.875,bicubic +repvit_m1_1.dist_300e_in1k,224,94.760,5.240,98.930,1.070,8.80,0.950,bicubic +mobilenetv4_conv_medium.e500_r224_in1k,256,94.760,5.240,98.860,1.140,9.72,1.000,bicubic +ecaresnetlight.miil_in1k,224,94.760,5.240,98.800,1.200,30.16,0.875,bicubic +convnext_pico.d1_in1k,288,94.750,5.250,98.700,1.300,9.05,0.950,bicubic +resnetv2_50d_gn.ah_in1k,224,94.750,5.250,98.680,1.320,25.57,0.950,bicubic +repvit_m2.dist_in1k,224,94.750,5.250,98.670,1.330,8.80,0.950,bicubic +resnet50d.a1_in1k,288,94.750,5.250,98.470,1.530,25.58,1.000,bicubic +resnet101s.gluon_in1k,224,94.740,5.260,98.820,1.180,44.67,0.875,bicubic +haloregnetz_b.ra3_in1k,224,94.740,5.260,98.680,1.320,11.68,0.940,bicubic +convnext_nano_ols.d1h_in1k,224,94.740,5.260,98.620,1.380,15.65,0.950,bicubic +resnet152.a3_in1k,224,94.720,5.280,98.690,1.310,60.19,0.950,bicubic +deit_small_distilled_patch16_224.fb_in1k,224,94.710,5.290,99.020,0.980,22.44,0.900,bicubic +resnet50.b2k_in1k,288,94.710,5.290,98.820,1.180,25.56,1.000,bicubic +resnext50_32x4d.ra_in1k,288,94.700,5.300,98.760,1.240,25.03,0.950,bicubic +resnetrs101.tf_in1k,192,94.700,5.300,98.710,1.290,63.62,0.940,bicubic +resnet101.a2_in1k,224,94.700,5.300,98.640,1.360,44.55,0.950,bicubic +maxvit_rmlp_pico_rw_256.sw_in1k,256,94.690,5.310,98.800,1.200,7.52,0.950,bicubic +xcit_tiny_12_p8_224.fb_in1k,224,94.680,5.320,98.840,1.160,6.71,1.000,bicubic +edgenext_small_rw.sw_in1k,320,94.680,5.320,98.820,1.180,7.83,1.000,bicubic +seresnet50.a2_in1k,288,94.680,5.320,98.780,1.220,28.09,1.000,bicubic +ecaresnet50t.a1_in1k,224,94.680,5.320,98.580,1.420,25.57,0.950,bicubic +cspdarknet53.ra_in1k,256,94.670,5.330,98.800,1.200,27.64,0.887,bilinear +seresnet50.a1_in1k,288,94.670,5.330,98.720,1.280,28.09,1.000,bicubic +resnext101_64x4d.gluon_in1k,224,94.670,5.330,98.650,1.350,83.46,0.875,bicubic +regnetx_032.tv2_in1k,224,94.660,5.340,98.840,1.160,15.30,0.965,bicubic +cs3darknet_focus_l.c2ns_in1k,256,94.660,5.340,98.810,1.190,21.15,0.887,bicubic +resnet50.tv2_in1k,224,94.660,5.340,98.780,1.220,25.56,0.965,bilinear +pit_s_224.in1k,224,94.660,5.340,98.720,1.280,23.46,0.900,bicubic +eca_resnet33ts.ra2_in1k,288,94.650,5.350,98.890,1.110,19.68,1.000,bicubic +gernet_m.idstcv_in1k,224,94.650,5.350,98.860,1.140,21.14,0.875,bilinear +poolformerv2_s24.sail_in1k,224,94.650,5.350,98.830,1.170,21.34,1.000,bicubic +resnet50.a2_in1k,288,94.650,5.350,98.660,1.340,25.56,1.000,bicubic +resmlp_big_24_224.fb_in1k,224,94.650,5.350,98.510,1.490,129.14,0.875,bicubic +sebotnet33ts_256.a1h_in1k,256,94.650,5.350,98.490,1.510,13.70,0.940,bicubic +vit_pwee_patch16_reg1_gap_256.sbb_in1k,256,94.640,5.360,98.780,1.220,15.25,0.950,bicubic +convnext_pico_ols.d1_in1k,288,94.640,5.360,98.770,1.230,9.06,1.000,bicubic +tf_efficientnet_b3.in1k,300,94.640,5.360,98.770,1.230,12.23,0.904,bicubic +ecaresnet50d.miil_in1k,224,94.630,5.370,98.880,1.120,25.58,0.875,bicubic +resnet50d.ra4_e3600_r224_in1k,224,94.630,5.370,98.700,1.300,25.58,0.950,bicubic +resnetaa50.a1h_in1k,224,94.630,5.370,98.580,1.420,25.56,0.950,bicubic +darknet53.c2ns_in1k,288,94.620,5.380,98.900,1.100,41.61,1.000,bicubic +nf_resnet50.ra2_in1k,288,94.620,5.380,98.810,1.190,25.56,0.940,bicubic +fastvit_t12.apple_dist_in1k,256,94.620,5.380,98.800,1.200,7.55,0.900,bicubic +resnext50_32x4d.tv2_in1k,224,94.620,5.380,98.780,1.220,25.03,0.965,bilinear +efficientnet_b3_pruned.in1k,300,94.620,5.380,98.750,1.250,9.86,0.904,bicubic +efficientnet_b2.ra_in1k,288,94.620,5.380,98.710,1.290,9.11,1.000,bicubic +tresnet_m.miil_in1k,224,94.620,5.380,98.560,1.440,31.39,0.875,bilinear +regnety_320.pycls_in1k,224,94.590,5.410,98.850,1.150,145.05,0.875,bicubic +resnet50d.a2_in1k,288,94.590,5.410,98.670,1.330,25.58,1.000,bicubic +poolformer_s24.sail_in1k,224,94.580,5.420,98.900,1.100,21.39,0.900,bicubic 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+xcit_tiny_24_p16_224.fb_dist_in1k,224,94.530,5.470,98.800,1.200,12.12,1.000,bicubic +mobilevitv2_150.cvnets_in1k,256,94.530,5.470,98.720,1.280,10.59,0.888,bicubic +gcresnext50ts.ch_in1k,256,94.530,5.470,98.700,1.300,15.67,0.900,bicubic +resnext101_32x4d.gluon_in1k,224,94.530,5.470,98.630,1.370,44.18,0.875,bicubic +resnet101.a1_in1k,224,94.530,5.470,98.520,1.480,44.55,0.950,bicubic +repvgg_b3g4.rvgg_in1k,224,94.520,5.480,98.960,1.040,83.83,0.875,bilinear +regnety_120.pycls_in1k,224,94.520,5.480,98.820,1.180,51.82,0.875,bicubic +darknetaa53.c2ns_in1k,288,94.520,5.480,98.760,1.240,36.02,1.000,bilinear +resnet50.ram_in1k,288,94.520,5.480,98.670,1.330,25.56,0.950,bicubic +convmixer_768_32.in1k,224,94.500,5.500,98.850,1.150,21.11,0.960,bicubic +efficientformer_l1.snap_dist_in1k,224,94.500,5.500,98.820,1.180,12.29,0.950,bicubic +gcresnet33ts.ra2_in1k,256,94.500,5.500,98.780,1.220,19.88,0.900,bicubic +ecaresnet50t.a2_in1k,224,94.500,5.500,98.640,1.360,25.57,0.950,bicubic +tf_efficientnet_b2.ap_in1k,260,94.500,5.500,98.640,1.360,9.11,0.890,bicubic +resnet50.fb_ssl_yfcc100m_ft_in1k,224,94.490,5.510,98.920,1.080,25.56,0.875,bilinear +rexnet_150.nav_in1k,224,94.480,5.520,98.790,1.210,9.73,0.875,bicubic +efficientvit_b1.r288_in1k,288,94.480,5.520,98.550,1.450,9.10,1.000,bicubic +resnetblur50.bt_in1k,288,94.470,5.530,98.780,1.220,25.56,0.950,bicubic +wide_resnet101_2.tv2_in1k,176,94.470,5.530,98.680,1.320,126.89,0.875,bilinear +convnextv2_pico.fcmae_ft_in1k,224,94.460,5.540,98.620,1.380,9.07,0.875,bicubic +resmlp_24_224.fb_distilled_in1k,224,94.450,5.550,98.770,1.230,30.02,0.875,bicubic +resnetv2_50.a1h_in1k,224,94.450,5.550,98.710,1.290,25.55,0.950,bicubic +regnetx_320.pycls_in1k,224,94.440,5.560,98.730,1.270,107.81,0.875,bicubic +deit_small_patch16_224.fb_in1k,224,94.430,5.570,98.700,1.300,22.05,0.900,bicubic +tf_efficientnetv2_b2.in1k,260,94.430,5.570,98.590,1.410,10.10,0.890,bicubic +resnet50.a1_in1k,224,94.410,5.590,98.430,1.570,25.56,0.950,bicubic +tf_efficientnet_el.in1k,300,94.400,5.600,98.710,1.290,10.59,0.904,bicubic +ecaresnet50t.a3_in1k,224,94.400,5.600,98.670,1.330,25.57,0.950,bicubic +tf_efficientnet_b2.aa_in1k,260,94.400,5.600,98.600,1.400,9.11,0.890,bicubic +gcvit_xxtiny.in1k,224,94.380,5.620,98.890,1.110,12.00,0.875,bicubic +regnety_160.pycls_in1k,224,94.380,5.620,98.830,1.170,83.59,0.875,bicubic +efficientnet_el_pruned.in1k,300,94.380,5.620,98.750,1.250,10.59,0.904,bicubic +inception_v4.tf_in1k,299,94.380,5.620,98.560,1.440,42.68,0.875,bicubic +legacy_seresnext101_32x4d.in1k,224,94.370,5.630,98.640,1.360,48.96,0.875,bilinear +dpn107.mx_in1k,224,94.370,5.630,98.480,1.520,86.92,0.875,bicubic +seresnext50_32x4d.gluon_in1k,224,94.360,5.640,98.620,1.380,27.56,0.875,bicubic +repvit_m1_0.dist_300e_in1k,224,94.330,5.670,98.850,1.150,7.30,0.950,bicubic +resnet50_gn.a1h_in1k,224,94.330,5.670,98.700,1.300,25.56,0.940,bicubic +xception71.tf_in1k,299,94.330,5.670,98.640,1.360,42.34,0.903,bicubic 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+dpn98.mx_in1k,224,94.160,5.840,98.590,1.410,61.57,0.875,bicubic +gmlp_s16_224.ra3_in1k,224,94.160,5.840,98.500,1.500,19.42,0.875,bicubic +resnet152.gluon_in1k,224,94.160,5.840,98.460,1.540,60.19,0.875,bicubic +nf_resnet50.ra2_in1k,256,94.150,5.850,98.740,1.260,25.56,0.940,bicubic +vit_base_patch16_224.sam_in1k,224,94.150,5.850,98.690,1.310,86.57,0.900,bicubic +nf_regnet_b1.ra2_in1k,288,94.150,5.850,98.630,1.370,10.22,0.900,bicubic +efficientnet_b2_pruned.in1k,260,94.140,5.860,98.510,1.490,8.31,0.890,bicubic +tf_efficientnet_b2.in1k,260,94.130,5.870,98.450,1.550,9.11,0.890,bicubic +resnext50_32x4d.ra_in1k,224,94.110,5.890,98.330,1.670,25.03,0.875,bicubic +efficientformerv2_s1.snap_dist_in1k,224,94.100,5.900,98.680,1.320,6.19,0.950,bicubic +ese_vovnet39b.ra_in1k,224,94.100,5.900,98.670,1.330,24.57,0.875,bicubic +resnet50d.a2_in1k,224,94.100,5.900,98.480,1.520,25.58,0.950,bicubic +resnet152.tv2_in1k,176,94.090,5.910,98.540,1.460,60.19,0.875,bilinear 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+spnasnet_100.rmsp_in1k,224,90.320,9.680,97.190,2.810,4.42,0.875,bilinear +resnet18d.ra2_in1k,288,90.310,9.690,97.560,2.440,11.71,0.950,bicubic +resnet18.fb_ssl_yfcc100m_ft_in1k,224,90.250,9.750,97.560,2.440,11.69,0.875,bilinear +crossvit_tiny_240.in1k,240,90.230,9.770,97.590,2.410,7.01,0.875,bicubic +ghostnet_100.in1k,224,90.200,9.800,97.250,2.750,5.18,0.875,bicubic +vgg19_bn.tv_in1k,224,90.120,9.880,97.580,2.420,143.68,0.875,bilinear +semnasnet_075.rmsp_in1k,224,90.090,9.910,97.410,2.590,2.91,0.875,bicubic +vgg16_bn.tv_in1k,224,90.090,9.910,97.370,2.630,138.37,0.875,bilinear +mobilenetv4_conv_small.e2400_r224_in1k,224,90.070,9.930,97.280,2.720,3.77,0.875,bicubic +pit_ti_224.in1k,224,89.950,10.050,97.440,2.560,4.85,0.900,bicubic +resnet34.tv_in1k,224,89.930,10.070,97.340,2.660,21.80,0.875,bilinear +mobilenetv4_conv_small.e1200_r224_in1k,224,89.920,10.080,97.240,2.760,3.77,0.875,bicubic +resnet34.a3_in1k,224,89.920,10.080,97.170,2.830,21.80,0.950,bicubic 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+vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,224,89.230,10.770,97.220,2.780,6.34,0.900,bicubic +vgg19.tv_in1k,224,89.060,10.940,96.900,3.100,143.67,0.875,bilinear +hrnet_w18_small.ms_in1k,224,89.050,10.950,97.080,2.920,13.19,0.875,bilinear +resnet14t.c3_in1k,224,89.000,11.000,96.700,3.300,10.08,0.950,bicubic +repghostnet_080.in1k,224,88.970,11.030,96.770,3.230,3.28,0.875,bicubic +tf_mobilenetv3_large_minimal_100.in1k,224,88.940,11.060,96.880,3.120,3.92,0.875,bilinear +regnetx_004.pycls_in1k,224,88.920,11.080,97.110,2.890,5.16,0.875,bicubic +legacy_seresnet18.in1k,224,88.890,11.110,96.970,3.030,11.78,0.875,bicubic +edgenext_xx_small.in1k,288,88.870,11.130,96.680,3.320,1.33,1.000,bicubic +vgg13_bn.tv_in1k,224,88.780,11.220,96.970,3.030,133.05,0.875,bilinear +pvt_v2_b0.in1k,224,88.780,11.220,96.870,3.130,3.67,0.900,bicubic +lcnet_100.ra2_in1k,224,88.760,11.240,96.710,3.290,2.95,0.875,bicubic +xcit_nano_12_p16_224.fb_in1k,224,88.600,11.400,96.830,3.170,3.05,1.000,bicubic +vgg16.tv_in1k,224,88.550,11.450,96.790,3.210,138.36,0.875,bilinear +efficientvit_m2.r224_in1k,224,88.460,11.540,96.910,3.090,4.19,0.875,bicubic +resnet18.a1_in1k,224,88.440,11.560,96.650,3.350,11.69,0.950,bicubic +resnet18.gluon_in1k,224,88.370,11.630,96.700,3.300,11.69,0.875,bicubic +edgenext_xx_small.in1k,256,88.370,11.630,96.530,3.470,1.33,0.900,bicubic +mobileone_s0.apple_in1k,224,88.320,11.680,96.430,3.570,5.29,0.875,bilinear +resnet14t.c3_in1k,176,88.290,11.710,96.370,3.630,10.08,0.875,bicubic +mobilevitv2_050.cvnets_in1k,256,88.250,11.750,96.970,3.030,1.37,0.888,bicubic +resnet18.a2_in1k,224,88.030,11.970,96.420,3.580,11.69,0.950,bicubic +efficientvit_b0.r224_in1k,224,87.980,12.020,96.080,3.920,3.41,0.950,bicubic +resnet34.a3_in1k,160,87.900,12.100,96.470,3.530,21.80,0.950,bicubic +tinynet_c.in1k,184,87.790,12.210,96.370,3.630,2.46,0.875,bicubic +vgg11_bn.tv_in1k,224,87.500,12.500,96.810,3.190,132.87,0.875,bilinear +resnet18.tv_in1k,224,87.380,12.620,96.260,3.740,11.69,0.875,bilinear +regnety_002.pycls_in1k,224,87.350,12.650,96.600,3.400,3.16,0.875,bicubic +mixer_l16_224.goog_in21k_ft_in1k,224,87.180,12.820,93.520,6.480,208.20,0.875,bicubic +mobilevit_xxs.cvnets_in1k,256,87.160,12.840,96.100,3.900,1.27,0.900,bicubic +vgg13.tv_in1k,224,87.020,12.980,96.310,3.690,133.05,0.875,bilinear +efficientvit_m1.r224_in1k,224,86.810,13.190,96.000,4.000,2.98,0.875,bicubic +vgg11.tv_in1k,224,86.580,13.420,96.310,3.690,132.86,0.875,bilinear +repghostnet_058.in1k,224,86.510,13.490,95.900,4.100,2.55,0.875,bicubic +resnet18.a3_in1k,224,86.430,13.570,95.900,4.100,11.69,0.950,bicubic +dla60x_c.in1k,224,86.330,13.670,96.090,3.910,1.32,0.875,bilinear +resnet10t.c3_in1k,224,86.250,13.750,95.720,4.280,5.44,0.950,bicubic +regnetx_002.pycls_in1k,224,86.200,13.800,95.970,4.030,2.68,0.875,bicubic +lcnet_075.ra2_in1k,224,86.040,13.960,95.680,4.320,2.36,0.875,bicubic +tf_mobilenetv3_small_100.in1k,224,85.220,14.780,95.770,4.230,2.54,0.875,bilinear +mobilenetv3_small_100.lamb_in1k,224,85.190,14.810,95.620,4.380,2.54,0.875,bicubic +repghostnet_050.in1k,224,85.040,14.960,95.220,4.780,2.31,0.875,bicubic +tinynet_d.in1k,152,84.830,15.170,95.220,4.780,2.34,0.875,bicubic +resnet10t.c3_in1k,176,84.780,15.220,95.010,4.990,5.44,0.875,bicubic +mnasnet_small.lamb_in1k,224,84.370,15.630,95.170,4.830,2.03,0.875,bicubic +dla46x_c.in1k,224,84.350,15.650,95.240,4.760,1.07,0.875,bilinear +resnet18.a3_in1k,160,84.270,15.730,94.630,5.370,11.69,0.950,bicubic +mobilenetv2_050.lamb_in1k,224,83.940,16.060,94.590,5.410,1.97,0.875,bicubic +dla46_c.in1k,224,83.620,16.380,94.920,5.080,1.30,0.875,bilinear +tf_mobilenetv3_small_075.in1k,224,83.520,16.480,94.860,5.140,2.04,0.875,bilinear +mobilenetv3_small_075.lamb_in1k,224,83.110,16.890,94.150,5.850,2.04,0.875,bicubic +efficientvit_m0.r224_in1k,224,82.400,17.600,94.420,5.580,2.35,0.875,bicubic +lcnet_050.ra2_in1k,224,81.720,18.280,93.730,6.270,1.88,0.875,bicubic +tf_mobilenetv3_small_minimal_100.in1k,224,81.320,18.680,93.700,6.300,2.04,0.875,bilinear +tinynet_e.in1k,106,78.990,21.010,92.560,7.440,2.04,0.875,bicubic +mobilenetv3_small_050.lamb_in1k,224,76.980,23.020,91.310,8.690,1.59,0.875,bicubic +test_efficientnet.r160_in1k,160,67.110,32.890,86.100,13.900,0.36,0.875,bicubic +test_byobnet.r160_in1k,160,65.870,34.130,86.270,13.730,0.46,0.875,bicubic +test_vit.r160_in1k,160,62.460,37.540,84.540,15.460,0.37,0.875,bicubic diff --git a/pytorch-image-models/results/results-imagenet-a.csv b/pytorch-image-models/results/results-imagenet-a.csv new file mode 100644 index 0000000000000000000000000000000000000000..2ce27ae8ffaa9ea0d3718753c00078fa105a5b68 --- /dev/null +++ b/pytorch-image-models/results/results-imagenet-a.csv @@ -0,0 +1,1446 @@ +model,img_size,top1,top1_err,top5,top5_err,param_count,crop_pct,interpolation,top1_diff,top5_diff,rank_diff +eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,448,88.640,11.360,97.360,2.640,305.08,1.000,bicubic,-10.190,-2.520,+2 +eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,448,88.413,11.587,97.040,2.960,305.08,1.000,bicubic,-10.527,-2.870,-1 +eva_giant_patch14_560.m30m_ft_in22k_in1k,560,87.320,12.680,96.800,3.200,"1,014.45",1.000,bicubic,-11.500,-3.100,+1 +eva02_large_patch14_448.mim_m38m_ft_in1k,448,87.040,12.960,96.360,3.640,305.08,1.000,bicubic,-11.690,-3.430,+4 +eva02_large_patch14_448.mim_in22k_ft_in1k,448,86.227,13.773,95.880,4.120,305.08,1.000,bicubic,-12.623,-3.960,-3 +eva_giant_patch14_336.clip_ft_in1k,336,85.120,14.880,95.667,4.333,"1,013.01",1.000,bicubic,-13.690,-4.233,0 +eva_giant_patch14_336.m30m_ft_in22k_in1k,336,85.013,14.987,96.253,3.747,"1,013.01",1.000,bicubic,-13.797,-3.567,-2 +tf_efficientnet_l2.ns_jft_in1k,800,84.680,15.320,96.040,3.960,480.31,0.960,bicubic,-13.860,-3.780,+10 +regnety_1280.swag_ft_in1k,384,83.760,16.240,96.133,3.867,644.81,1.000,bicubic,-14.690,-3.747,+18 +eva_large_patch14_336.in22k_ft_in22k_in1k,336,83.533,16.467,95.187,4.813,304.53,1.000,bicubic,-15.217,-4.623,-3 +tf_efficientnet_l2.ns_jft_in1k_475,475,83.373,16.627,95.467,4.533,480.31,0.936,bicubic,-15.127,-4.353,+9 +convnextv2_huge.fcmae_ft_in22k_in1k_512,512,83.360,16.640,95.907,4.093,660.29,1.000,bicubic,-15.230,-3.963,+3 +maxvit_xlarge_tf_512.in21k_ft_in1k,512,83.147,16.853,95.347,4.653,475.77,1.000,bicubic,-15.473,-4.453,0 +beit_large_patch16_512.in22k_ft_in22k_in1k,512,82.960,17.040,95.720,4.280,305.67,1.000,bicubic,-15.600,-4.110,+2 +eva_large_patch14_336.in22k_ft_in1k,336,82.667,17.333,95.547,4.453,304.53,1.000,bicubic,-16.053,-4.323,-6 +maxvit_large_tf_512.in21k_ft_in1k,512,81.693,18.307,94.987,5.013,212.33,1.000,bicubic,-16.917,-4.803,-2 +eva_giant_patch14_224.clip_ft_in1k,224,81.080,18.920,94.187,5.813,"1,012.56",0.900,bicubic,-17.380,-5.563,+9 +maxvit_base_tf_512.in21k_ft_in1k,512,81.053,18.947,94.320,5.680,119.88,1.000,bicubic,-17.587,-5.480,-7 +maxvit_xlarge_tf_384.in21k_ft_in1k,384,80.933,19.067,94.440,5.560,475.32,1.000,bicubic,-17.567,-5.400,+3 +beit_large_patch16_384.in22k_ft_in22k_in1k,384,80.173,19.827,94.733,5.267,305.00,1.000,bicubic,-18.327,-5.047,+1 +caformer_b36.sail_in22k_ft_in1k_384,384,79.587,20.413,94.320,5.680,98.75,1.000,bicubic,-18.853,-5.500,+7 +convnextv2_huge.fcmae_ft_in22k_in1k_384,384,79.373,20.627,94.547,5.453,660.29,1.000,bicubic,-19.297,-5.313,-12 +deit3_large_patch16_384.fb_in22k_ft_in1k,384,79.320,20.680,93.613,6.387,304.76,1.000,bicubic,-19.140,-6.137,+2 +maxvit_large_tf_384.in21k_ft_in1k,384,78.093,21.907,93.200,6.800,212.03,1.000,bicubic,-20.397,-6.550,-1 +eva02_base_patch14_448.mim_in22k_ft_in1k,448,77.813,22.187,93.213,6.787,87.12,1.000,bicubic,-20.627,-6.607,+5 +eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,448,77.587,22.413,93.080,6.920,87.12,1.000,bicubic,-21.043,-6.720,-14 +vit_large_patch14_clip_336.openai_ft_in12k_in1k,336,77.147,22.853,93.640,6.360,304.53,1.000,bicubic,-21.123,-6.130,+13 +convnext_xxlarge.clip_laion2b_soup_ft_in1k,256,76.867,23.133,94.213,5.787,846.47,1.000,bicubic,-21.573,-5.607,+1 +maxvit_base_tf_384.in21k_ft_in1k,384,76.707,23.293,92.387,7.613,119.65,1.000,bicubic,-21.813,-7.363,-10 +beitv2_large_patch16_224.in1k_ft_in22k_in1k,224,76.600,23.400,93.307,6.693,304.43,0.950,bicubic,-21.950,-6.453,-13 +eva_large_patch14_196.in22k_ft_in22k_in1k,196,75.400,24.600,91.627,8.373,304.14,1.000,bicubic,-23.030,-8.143,+1 +maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,384,74.867,25.133,91.440,8.560,116.14,1.000,bicubic,-23.313,-8.330,+18 +regnety_1280.swag_lc_in1k,224,74.547,25.453,91.627,8.373,644.81,0.965,bicubic,-23.113,-7.973,+93 +swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,384,74.347,25.653,92.360,7.640,196.74,1.000,bicubic,-23.813,-7.350,+22 +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,336,74.227,25.773,92.307,7.693,632.46,1.000,bicubic,-24.203,-7.513,-4 +regnety_320.swag_ft_in1k,384,73.987,26.013,92.867,7.133,145.05,1.000,bicubic,-24.073,-6.993,+30 +caformer_m36.sail_in22k_ft_in1k_384,384,73.920,26.080,91.347,8.653,56.20,1.000,bicubic,-24.230,-8.403,+21 +convformer_b36.sail_in22k_ft_in1k_384,384,73.653,26.347,91.640,8.360,99.88,1.000,bicubic,-24.607,-8.130,+3 +eva_large_patch14_196.in22k_ft_in1k,196,73.040,26.960,91.347,8.653,304.14,1.000,bicubic,-25.320,-8.433,-4 +vit_large_patch14_clip_224.openai_ft_in12k_in1k,224,72.173,27.827,90.813,9.187,304.20,1.000,bicubic,-26.057,-8.907,+6 +swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,384,71.907,28.093,91.840,8.160,87.92,1.000,bicubic,-26.233,-7.940,+18 +vit_large_patch14_clip_224.openai_ft_in1k,224,71.827,28.173,91.360,8.640,304.20,1.000,bicubic,-26.333,-8.370,+15 +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,336,71.427,28.573,89.827,10.173,304.53,1.000,bicubic,-26.903,-9.933,-6 +deit3_base_patch16_384.fb_in22k_ft_in1k,384,71.293,28.707,89.880,10.120,86.88,1.000,bicubic,-26.547,-9.780,+52 +convnext_xlarge.fb_in22k_ft_in1k_384,384,71.187,28.813,90.787,9.213,350.20,1.000,bicubic,-27.233,-9.023,-12 +coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,384,71.187,28.813,89.760,10.240,73.88,1.000,bicubic,-26.883,-9.930,+19 +vit_large_patch16_384.augreg_in21k_ft_in1k,384,71.067,28.933,89.640,10.360,304.72,1.000,bicubic,-27.183,-10.160,-2 +convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,384,70.920,29.080,90.867,9.133,200.13,1.000,bicubic,-27.560,-8.883,-24 +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,224,70.893,29.107,90.547,9.453,632.05,1.000,bicubic,-27.397,-9.233,-11 +swin_large_patch4_window12_384.ms_in22k_ft_in1k,384,70.800,29.200,90.573,9.427,196.74,1.000,bicubic,-27.220,-9.117,+21 +caformer_s36.sail_in22k_ft_in1k_384,384,70.747,29.253,90.427,9.573,39.30,1.000,bicubic,-27.253,-9.293,+22 +vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k,384,70.627,29.373,90.613,9.387,64.27,1.000,bicubic,-27.533,-9.057,+3 +convnextv2_large.fcmae_ft_in22k_in1k_384,384,70.213,29.787,89.600,10.400,197.96,1.000,bicubic,-28.167,-10.160,-19 +deit3_huge_patch14_224.fb_in22k_ft_in1k,224,70.187,29.813,90.800,9.200,632.13,1.000,bicubic,-27.983,-8.950,0 +maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,384,70.027,29.973,90.440,9.560,116.09,1.000,bicubic,-28.233,-9.390,-13 +convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,320,69.827,30.173,90.747,9.253,200.13,1.000,bicubic,-28.453,-9.023,-17 +volo_d5_512.sail_in1k,512,69.453,30.547,90.560,9.440,296.09,1.150,bicubic,-28.337,-9.110,+52 +caformer_b36.sail_in22k_ft_in1k,224,69.453,30.547,90.213,9.787,98.75,1.000,bicubic,-28.717,-9.557,-7 +beit_large_patch16_224.in22k_ft_in22k_in1k,224,69.053,30.947,89.973,10.027,304.43,0.900,bicubic,-29.117,-9.787,-7 +seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,384,68.627,31.373,88.560,11.440,149.39,1.000,bicubic,-29.573,-11.220,-12 +deit3_large_patch16_224.fb_in22k_ft_in1k,224,68.573,31.427,90.000,10.000,304.37,1.000,bicubic,-29.597,-9.730,-8 +convformer_m36.sail_in22k_ft_in1k_384,384,68.547,31.453,89.160,10.840,57.05,1.000,bicubic,-29.473,-10.530,+8 +convnext_xlarge.fb_in22k_ft_in1k,288,68.227,31.773,90.133,9.867,350.20,1.000,bicubic,-29.893,-9.637,-2 +regnety_160.swag_ft_in1k,384,68.093,31.907,90.680,9.320,83.59,1.000,bicubic,-29.697,-9.080,+43 +maxvit_base_tf_512.in1k,512,68.093,31.907,88.547,11.453,119.88,1.000,bicubic,-29.707,-11.063,+43 +volo_d5_448.sail_in1k,448,68.000,32.000,89.773,10.227,295.91,1.150,bicubic,-29.750,-9.857,+52 +maxvit_large_tf_512.in1k,512,67.667,32.333,87.693,12.307,212.33,1.000,bicubic,-30.163,-12.007,+35 +beitv2_large_patch16_224.in1k_ft_in1k,224,67.627,32.373,88.733,11.267,304.43,0.950,bicubic,-30.293,-10.947,+15 +convnext_large.fb_in22k_ft_in1k_384,384,67.413,32.587,88.893,11.107,197.77,1.000,bicubic,-30.847,-10.867,-25 +swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,256,67.373,32.627,88.507,11.493,196.74,0.900,bicubic,-30.467,-11.173,+28 +convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,384,67.267,32.733,89.960,10.040,200.13,1.000,bicubic,-30.993,-9.790,-28 +tf_efficientnet_b7.ns_jft_in1k,600,67.160,32.840,88.507,11.493,66.35,0.949,bicubic,-30.730,-11.213,+17 +tf_efficientnetv2_xl.in21k_ft_in1k,512,67.147,32.853,87.160,12.840,208.12,1.000,bicubic,-30.753,-12.410,+15 +vit_large_patch14_clip_336.laion2b_ft_in1k,336,67.067,32.933,88.920,11.080,304.53,1.000,bicubic,-31.163,-10.800,-27 +beit_base_patch16_384.in22k_ft_in22k_in1k,384,66.987,33.013,89.093,10.907,86.74,1.000,bicubic,-30.843,-10.577,+25 +convnextv2_large.fcmae_ft_in22k_in1k,288,66.880,33.120,88.880,11.120,197.96,1.000,bicubic,-31.230,-10.900,-14 +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,224,66.840,33.160,88.160,11.840,304.20,1.000,bicubic,-31.240,-11.600,-13 +convnext_large.fb_in22k_ft_in1k,288,66.333,33.667,88.987,11.013,197.77,1.000,bicubic,-31.797,-10.763,-18 +volo_d4_448.sail_in1k,448,66.293,33.707,88.987,11.013,193.41,1.150,bicubic,-31.387,-10.733,+45 +hiera_huge_224.mae_in1k_ft_in1k,224,66.240,33.760,86.547,13.453,672.78,0.900,bicubic,-31.710,-13.213,-2 +seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,320,66.013,33.987,87.813,12.187,93.59,1.000,bicubic,-31.947,-11.887,-6 +convnextv2_large.fcmae_ft_in22k_in1k,224,65.933,34.067,87.280,12.720,197.96,0.875,bicubic,-31.997,-12.490,-2 +convnextv2_base.fcmae_ft_in22k_in1k_384,384,65.813,34.187,87.840,12.160,88.72,1.000,bicubic,-32.547,-11.990,-47 +vit_huge_patch14_clip_224.laion2b_ft_in1k,224,65.667,34.333,87.840,12.160,632.05,1.000,bicubic,-32.363,-11.890,-15 +regnety_320.swag_lc_in1k,224,65.547,34.453,87.987,12.013,145.05,0.965,bicubic,-31.613,-11.553,+152 +volo_d3_448.sail_in1k,448,65.520,34.480,87.373,12.627,86.63,1.000,bicubic,-32.030,-12.287,+63 +nextvit_large.bd_ssld_6m_in1k_384,384,65.373,34.627,88.587,11.413,57.87,1.000,bicubic,-32.567,-11.203,-8 +vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k,384,65.360,34.640,88.333,11.667,60.60,1.000,bicubic,-32.410,-11.267,+26 +swin_base_patch4_window12_384.ms_in22k_ft_in1k,384,65.280,34.720,88.587,11.413,87.90,1.000,bicubic,-32.590,-11.173,+2 +efficientvit_l3.r384_in1k,384,64.813,35.187,87.053,12.947,246.04,1.000,bicubic,-32.777,-12.567,+55 +convnextv2_huge.fcmae_ft_in1k,288,64.533,35.467,87.253,12.747,660.29,1.000,bicubic,-33.367,-12.377,-5 +seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,288,64.520,35.480,87.080,12.920,93.59,0.950,bicubic,-33.430,-12.540,-16 +maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,224,64.520,35.480,85.867,14.133,116.14,0.950,bicubic,-33.260,-13.793,+19 +tf_efficientnetv2_l.in21k_ft_in1k,480,64.507,35.493,87.653,12.347,118.52,1.000,bicubic,-33.323,-12.107,+5 +convnext_xlarge.fb_in22k_ft_in1k,224,63.787,36.213,87.387,12.613,350.20,0.875,bicubic,-34.133,-12.273,-13 +swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,256,63.467,36.533,87.667,12.333,87.92,0.900,bicubic,-34.213,-11.943,+27 +maxvit_large_tf_384.in1k,384,63.400,36.600,85.187,14.813,212.03,1.000,bicubic,-34.190,-14.333,+49 +convformer_b36.sail_in22k_ft_in1k,224,63.373,36.627,86.480,13.520,99.88,1.000,bicubic,-34.577,-13.250,-22 +convnext_base.fb_in22k_ft_in1k_384,384,63.307,36.693,86.827,13.173,88.59,1.000,bicubic,-34.793,-12.823,-36 +convformer_s36.sail_in22k_ft_in1k_384,384,63.027,36.973,86.773,13.227,40.01,1.000,bicubic,-34.823,-12.917,-6 +vit_base_patch16_384.augreg_in21k_ft_in1k,384,63.027,36.973,86.080,13.920,86.86,1.000,bicubic,-34.803,-13.480,+1 +maxvit_small_tf_512.in1k,512,62.840,37.160,86.467,13.533,69.13,1.000,bicubic,-34.920,-13.083,+13 +nextvit_base.bd_ssld_6m_in1k_384,384,62.813,37.187,87.960,12.040,44.82,1.000,bicubic,-35.057,-11.750,-13 +maxvit_base_tf_384.in1k,384,62.467,37.533,85.133,14.867,119.65,1.000,bicubic,-35.123,-14.537,+40 +vit_base_patch8_224.augreg2_in21k_ft_in1k,224,62.240,37.760,85.893,14.107,86.58,0.900,bicubic,-35.500,-13.747,+14 +caformer_b36.sail_in1k_384,384,62.093,37.907,84.480,15.520,98.75,1.000,bicubic,-35.417,-15.100,+49 +cait_m48_448.fb_dist_in1k,448,62.080,37.920,86.480,13.520,356.46,1.000,bicubic,-35.410,-13.140,+54 +convnextv2_base.fcmae_ft_in22k_in1k,288,62.067,37.933,87.053,12.947,88.72,1.000,bicubic,-35.993,-12.697,-41 +tf_efficientnet_b6.ns_jft_in1k,528,61.880,38.120,85.027,14.973,43.04,0.942,bicubic,-35.740,-14.573,+27 +convnextv2_huge.fcmae_ft_in1k,224,61.867,38.133,84.680,15.320,660.29,0.875,bicubic,-35.783,-14.890,+19 +beitv2_base_patch16_224.in1k_ft_in22k_in1k,224,61.480,38.520,85.573,14.427,86.53,0.900,bicubic,-36.220,-14.107,+11 +swin_large_patch4_window7_224.ms_in22k_ft_in1k,224,61.333,38.667,86.600,13.400,196.53,0.900,bicubic,-36.317,-13.090,+18 +convnext_base.fb_in22k_ft_in1k,288,61.240,38.760,86.467,13.533,88.59,1.000,bicubic,-36.610,-13.173,-20 +vit_large_r50_s32_384.augreg_in21k_ft_in1k,384,61.187,38.813,83.787,16.213,329.09,1.000,bicubic,-36.663,-15.883,-20 +seresnextaa101d_32x8d.sw_in12k_ft_in1k,288,61.147,38.853,85.840,14.160,93.59,1.000,bicubic,-36.783,-13.840,-34 +caformer_m36.sail_in22k_ft_in1k,224,60.973,39.027,84.960,15.040,56.20,1.000,bicubic,-36.867,-14.710,-19 +vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k,256,60.867,39.133,85.240,14.760,64.11,0.950,bicubic,-36.843,-14.370,+3 +convnext_large_mlp.clip_laion2b_augreg_ft_in1k,256,60.733,39.267,86.693,13.307,200.13,1.000,bicubic,-37.247,-13.007,-44 +coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,224,60.693,39.307,84.413,15.587,73.88,0.950,bicubic,-36.947,-15.157,+13 +regnety_160.swag_lc_in1k,224,60.520,39.480,85.880,14.120,83.59,0.965,bicubic,-36.310,-13.520,+190 +convnextv2_base.fcmae_ft_in22k_in1k,224,60.520,39.480,85.053,14.947,88.72,0.875,bicubic,-37.390,-14.637,-36 +convnext_small.in12k_ft_in1k_384,384,60.507,39.493,84.813,15.187,50.22,1.000,bicubic,-37.293,-14.877,-16 +convnext_large.fb_in22k_ft_in1k,224,60.480,39.520,85.147,14.853,197.77,0.875,bicubic,-37.370,-14.533,-31 +caformer_m36.sail_in1k_384,384,60.347,39.653,84.813,15.187,56.20,1.000,bicubic,-37.103,-14.727,+43 +deit3_large_patch16_384.fb_in1k,384,60.227,39.773,85.547,14.453,304.76,1.000,bicubic,-37.213,-14.043,+46 +coatnet_2_rw_224.sw_in12k_ft_in1k,224,60.160,39.840,84.613,15.387,73.87,0.950,bicubic,-37.370,-14.987,+27 +vit_large_patch14_clip_224.laion2b_ft_in1k,224,60.040,39.960,85.840,14.160,304.20,1.000,bicubic,-37.870,-13.810,-42 +tf_efficientnet_b5.ns_jft_in1k,456,59.933,40.067,84.280,15.720,30.39,0.934,bicubic,-37.567,-15.330,+29 +xcit_large_24_p8_384.fb_dist_in1k,384,59.880,40.120,85.387,14.613,188.93,1.000,bicubic,-37.650,-14.153,+25 +maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,224,59.867,40.133,84.973,15.027,116.09,0.950,bicubic,-37.883,-14.727,-14 +efficientvit_l3.r320_in1k,320,59.853,40.147,84.907,15.093,246.04,1.000,bicubic,-37.557,-14.483,+45 +vit_base_patch16_clip_384.openai_ft_in12k_in1k,384,59.627,40.373,84.200,15.800,86.86,0.950,bicubic,-38.563,-15.460,-83 +resnetv2_152x4_bit.goog_in21k_ft_in1k,480,59.560,40.440,83.173,16.827,936.53,1.000,bilinear,-37.920,-16.387,+29 +tf_efficientnetv2_m.in21k_ft_in1k,480,59.120,40.880,84.493,15.507,54.14,1.000,bicubic,-38.690,-15.197,-30 +convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,384,58.853,41.147,85.787,14.213,88.59,1.000,bicubic,-39.197,-13.963,-67 +maxvit_tiny_tf_512.in1k,512,58.667,41.333,84.627,15.373,31.05,1.000,bicubic,-38.913,-14.933,+11 +vit_base_patch8_224.augreg_in21k_ft_in1k,224,58.467,41.533,82.680,17.320,86.58,0.900,bicubic,-39.123,-16.900,+5 +volo_d2_384.sail_in1k,384,58.427,41.573,84.280,15.720,58.87,1.000,bicubic,-38.893,-15.310,+59 +caformer_s18.sail_in22k_ft_in1k_384,384,58.413,41.587,85.440,14.560,26.34,1.000,bicubic,-39.027,-14.020,+33 +tf_efficientnetv2_xl.in21k_ft_in1k,384,58.187,41.813,80.853,19.147,208.12,1.000,bicubic,-39.203,-18.527,+43 +nextvit_small.bd_ssld_6m_in1k_384,384,58.067,41.933,85.360,14.640,31.76,1.000,bicubic,-39.703,-14.350,-28 +vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k,256,58.027,41.973,83.200,16.800,64.11,0.950,bicubic,-39.603,-16.420,-9 +hiera_large_224.mae_in1k_ft_in1k,224,58.000,42.000,81.680,18.320,213.74,0.900,bicubic,-39.440,-17.950,+30 +resnext101_32x32d.fb_wsl_ig1b_ft_in1k,224,57.907,42.093,80.653,19.347,468.53,0.875,bilinear,-39.473,-18.917,+40 +tiny_vit_21m_512.dist_in22k_ft_in1k,512,57.787,42.213,83.147,16.853,21.27,1.000,bicubic,-40.113,-16.543,-58 +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,384,57.773,42.227,81.867,18.133,86.86,1.000,bicubic,-40.237,-17.793,-74 +efficientvit_l2.r384_in1k,384,57.747,42.253,83.747,16.253,63.71,1.000,bicubic,-39.653,-15.763,+34 +seresnextaa101d_32x8d.sw_in12k_ft_in1k,224,57.680,42.320,82.867,17.133,93.59,0.875,bicubic,-39.970,-16.753,-20 +cait_m36_384.fb_dist_in1k,384,57.667,42.333,84.680,15.320,271.22,1.000,bicubic,-39.733,-14.740,+29 +hgnetv2_b6.ssld_stage2_ft_in1k,288,57.547,42.453,84.173,15.827,75.26,1.000,bicubic,-40.253,-15.477,-45 +tiny_vit_21m_384.dist_in22k_ft_in1k,384,57.480,42.520,83.107,16.893,21.23,1.000,bicubic,-40.130,-16.483,-12 +tf_efficientnetv2_l.in21k_ft_in1k,384,57.387,42.613,82.293,17.707,118.52,1.000,bicubic,-40.073,-17.347,+13 +dm_nfnet_f5.dm_in1k,544,57.320,42.680,82.040,17.960,377.21,0.954,bicubic,-40.460,-17.600,-41 +convnext_base.clip_laiona_augreg_ft_in1k_384,384,57.173,42.827,84.720,15.280,88.59,1.000,bicubic,-40.437,-14.830,-14 +deit3_base_patch16_224.fb_in22k_ft_in1k,224,57.147,42.853,83.507,16.493,86.59,1.000,bicubic,-40.343,-16.093,+5 +deit3_small_patch16_384.fb_in22k_ft_in1k,384,57.133,42.867,83.013,16.987,22.21,1.000,bicubic,-39.997,-16.427,+85 +caformer_s36.sail_in1k_384,384,57.107,42.893,82.600,17.400,39.30,1.000,bicubic,-40.293,-16.900,+20 +hgnetv2_b6.ssld_stage1_in22k_in1k,288,57.093,42.907,84.240,15.760,75.26,1.000,bicubic,-40.717,-15.370,-55 +convnextv2_large.fcmae_ft_in1k,288,57.080,42.920,83.587,16.413,197.96,1.000,bicubic,-40.580,-16.053,-32 +volo_d5_224.sail_in1k,224,56.827,43.173,82.760,17.240,295.46,0.960,bicubic,-40.553,-16.920,+26 +dm_nfnet_f6.dm_in1k,576,56.747,43.253,81.520,18.480,438.36,0.956,bicubic,-41.033,-18.070,-51 +rdnet_large.nv_in1k_ft_in1k_384,384,56.720,43.280,82.987,17.013,186.27,1.000,bicubic,-40.950,-16.563,-37 +maxvit_small_tf_384.in1k,384,56.720,43.280,82.280,17.720,69.02,1.000,bicubic,-40.680,-17.190,+16 +regnety_160.lion_in12k_ft_in1k,288,56.693,43.307,83.907,16.093,83.59,1.000,bicubic,-40.757,-15.693,+4 +xcit_medium_24_p8_384.fb_dist_in1k,384,56.693,43.307,83.600,16.400,84.32,1.000,bicubic,-40.607,-15.910,+37 +convformer_m36.sail_in22k_ft_in1k,224,56.253,43.747,82.227,17.773,57.05,1.000,bicubic,-41.337,-17.293,-23 +dm_nfnet_f4.dm_in1k,512,56.240,43.760,81.307,18.693,316.07,0.951,bicubic,-41.390,-18.233,-33 +regnety_160.sw_in12k_ft_in1k,288,56.187,43.813,82.800,17.200,83.59,1.000,bicubic,-41.313,-16.840,-9 +efficientvit_l3.r256_in1k,256,56.147,43.853,81.360,18.640,246.04,1.000,bicubic,-40.883,-17.870,+99 +caformer_s36.sail_in22k_ft_in1k,224,55.547,44.453,81.880,18.120,39.30,1.000,bicubic,-42.063,-17.720,-33 +convformer_b36.sail_in1k_384,384,55.467,44.533,81.253,18.747,99.88,1.000,bicubic,-42.073,-18.257,-21 +vit_large_patch16_224.augreg_in21k_ft_in1k,224,55.293,44.707,79.680,20.320,304.33,0.900,bicubic,-42.347,-19.910,-41 +convnext_base.fb_in22k_ft_in1k,224,54.787,45.213,82.573,17.427,88.59,0.875,bicubic,-42.713,-17.027,-15 +convnextv2_large.fcmae_ft_in1k,224,54.680,45.320,81.160,18.840,197.96,0.875,bicubic,-42.670,-18.370,+15 +vit_base_patch16_clip_384.openai_ft_in1k,384,54.453,45.547,82.320,17.680,86.86,1.000,bicubic,-43.097,-17.230,-27 +xcit_small_24_p8_384.fb_dist_in1k,384,54.387,45.613,81.707,18.293,47.63,1.000,bicubic,-42.893,-17.853,+26 +vit_base_r50_s16_384.orig_in21k_ft_in1k,384,54.347,45.653,80.813,19.187,98.95,1.000,bicubic,-42.833,-18.617,+54 +cait_s36_384.fb_dist_in1k,384,54.213,45.787,81.253,18.747,68.37,1.000,bicubic,-43.137,-18.297,+12 +deit3_huge_patch14_224.fb_in1k,224,54.147,45.853,81.827,18.173,632.13,0.900,bicubic,-42.743,-17.613,+122 +volo_d1_384.sail_in1k,384,53.907,46.093,80.880,19.120,26.78,1.000,bicubic,-43.023,-18.500,+112 +vit_medium_patch16_gap_384.sw_in12k_ft_in1k,384,53.840,46.160,81.520,18.480,39.03,0.950,bicubic,-43.610,-18.080,-15 +beitv2_base_patch16_224.in1k_ft_in1k,224,53.640,46.360,81.827,18.173,86.53,0.900,bicubic,-43.530,-17.693,+53 +convformer_m36.sail_in1k_384,384,53.613,46.387,80.867,19.133,57.05,1.000,bicubic,-43.787,-18.673,-3 +dm_nfnet_f3.dm_in1k,416,53.560,46.440,79.293,20.707,254.92,0.940,bicubic,-43.920,-20.347,-21 +vit_base_patch16_clip_384.laion2b_ft_in1k,384,53.373,46.627,80.413,19.587,86.86,1.000,bicubic,-44.377,-19.207,-68 +deit3_base_patch16_384.fb_in1k,384,53.293,46.707,80.507,19.493,86.88,1.000,bicubic,-43.717,-19.003,+87 +convnext_small.fb_in22k_ft_in1k_384,384,53.160,46.840,81.467,18.533,50.22,1.000,bicubic,-44.460,-18.123,-52 +resnext101_32x16d.fb_wsl_ig1b_ft_in1k,224,53.013,46.987,76.893,23.107,194.03,0.875,bilinear,-43.907,-22.697,+107 +convnext_small.in12k_ft_in1k,288,52.987,47.013,81.307,18.693,50.22,1.000,bicubic,-44.353,-18.273,+4 +volo_d4_224.sail_in1k,224,52.867,47.133,80.547,19.453,192.96,0.960,bicubic,-44.433,-18.983,+10 +xcit_large_24_p16_384.fb_dist_in1k,384,52.827,47.173,81.733,18.267,189.10,1.000,bicubic,-44.713,-17.787,-39 +resnetv2_152x2_bit.goog_in21k_ft_in1k,448,52.800,47.200,81.133,18.867,236.34,1.000,bilinear,-44.220,-18.457,+77 +convnext_base.clip_laion2b_augreg_ft_in12k_in1k,256,52.507,47.493,82.760,17.240,88.59,1.000,bicubic,-45.093,-16.960,-52 +resnetv2_101x3_bit.goog_in21k_ft_in1k,448,52.467,47.533,79.827,20.173,387.93,1.000,bilinear,-44.543,-19.483,+78 +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,448,52.320,47.680,80.000,20.000,88.34,1.000,bicubic,-45.010,-19.480,0 +maxvit_tiny_tf_384.in1k,384,52.040,47.960,79.893,20.107,30.98,1.000,bicubic,-45.270,-19.567,+2 +vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k,256,52.000,48.000,80.467,19.533,60.40,0.950,bicubic,-45.230,-19.063,+21 +dm_nfnet_f5.dm_in1k,416,51.987,48.013,77.493,22.507,377.21,0.954,bicubic,-45.483,-22.027,-34 +swin_base_patch4_window7_224.ms_in22k_ft_in1k,224,51.813,48.187,80.413,19.587,87.77,0.900,bicubic,-45.467,-19.017,+6 +regnety_120.sw_in12k_ft_in1k,288,51.800,48.200,80.653,19.347,51.82,1.000,bicubic,-45.470,-18.867,+10 +efficientvit_l3.r224_in1k,224,51.720,48.280,79.080,20.920,246.04,1.000,bicubic,-45.290,-20.160,+74 +convnext_small.fb_in22k_ft_in1k,288,51.547,48.453,81.333,18.667,50.22,1.000,bicubic,-45.803,-18.197,-11 +dm_nfnet_f6.dm_in1k,448,51.533,48.467,76.533,23.467,438.36,0.956,bicubic,-46.177,-23.107,-82 +hgnetv2_b6.ssld_stage1_in22k_in1k,224,51.387,48.613,79.920,20.080,75.26,0.965,bicubic,-46.223,-19.690,-66 +efficientvit_l2.r288_in1k,288,51.347,48.653,79.973,20.027,63.71,1.000,bicubic,-45.713,-19.417,+58 +resnext101_32x8d.fb_swsl_ig1b_ft_in1k,224,51.280,48.720,78.200,21.800,88.79,0.875,bilinear,-45.940,-21.380,+15 +eva02_small_patch14_336.mim_in22k_ft_in1k,336,51.093,48.907,78.987,21.013,22.13,1.000,bicubic,-46.057,-20.463,+32 +tf_efficientnet_b4.ns_jft_in1k,380,51.013,48.987,79.053,20.947,19.34,0.922,bicubic,-45.957,-20.357,+76 +efficientnet_b5.sw_in12k_ft_in1k,448,50.987,49.013,78.667,21.333,30.39,1.000,bicubic,-46.403,-20.883,-27 +hgnetv2_b6.ssld_stage2_ft_in1k,224,50.920,49.080,79.280,20.720,75.26,0.965,bicubic,-46.530,-20.360,-42 +tf_efficientnetv2_m.in21k_ft_in1k,384,50.907,49.093,78.467,21.533,54.14,1.000,bicubic,-46.303,-21.033,+16 +flexivit_large.1200ep_in1k,240,50.840,49.160,80.547,19.453,304.36,0.950,bicubic,-46.580,-19.053,-37 +beit_base_patch16_224.in22k_ft_in22k_in1k,224,50.747,49.253,79.893,20.107,86.53,0.900,bicubic,-46.313,-19.477,+48 +mvitv2_large.fb_in1k,224,50.640,49.360,78.067,21.933,217.99,0.900,bicubic,-46.300,-21.323,+74 +xcit_small_12_p8_384.fb_dist_in1k,384,50.627,49.373,79.800,20.200,26.21,1.000,bicubic,-46.603,-19.790,+5 +dm_nfnet_f4.dm_in1k,384,50.307,49.693,76.053,23.947,316.07,0.951,bicubic,-47.043,-23.427,-24 +vit_base_patch16_clip_224.openai_ft_in12k_in1k,224,50.253,49.747,77.440,22.560,86.57,0.950,bicubic,-47.287,-22.040,-66 +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,224,50.240,49.760,78.200,21.800,86.57,0.950,bicubic,-47.210,-21.400,-48 +convformer_s18.sail_in22k_ft_in1k_384,384,50.160,49.840,81.053,18.947,26.77,1.000,bicubic,-47.120,-18.467,-15 +convnext_tiny.in12k_ft_in1k_384,384,50.120,49.880,79.733,20.267,28.59,1.000,bicubic,-47.210,-19.747,-26 +tf_efficientnetv2_l.in1k,480,50.120,49.880,77.520,22.480,118.52,1.000,bicubic,-47.390,-22.030,-65 +resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,384,50.093,49.907,77.640,22.360,236.34,1.000,bicubic,-46.737,-21.890,+91 +convformer_s36.sail_in1k_384,384,50.080,49.920,78.920,21.080,40.01,1.000,bicubic,-47.200,-20.670,-17 +vit_base_patch16_384.orig_in21k_ft_in1k,384,50.080,49.920,77.587,22.413,86.86,1.000,bicubic,-46.630,-21.863,+119 +volo_d3_224.sail_in1k,224,50.000,50.000,78.320,21.680,86.33,0.960,bicubic,-47.110,-21.080,+24 +flexivit_large.600ep_in1k,240,49.973,50.027,79.960,20.040,304.36,0.950,bicubic,-47.307,-19.640,-23 +mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k,544,49.853,50.147,78.560,21.440,32.59,1.000,bicubic,-47.317,-21.020,+6 +vit_base_patch16_224.augreg2_in21k_ft_in1k,224,49.733,50.267,78.733,21.267,86.57,0.900,bicubic,-47.427,-20.937,+10 +vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k,256,49.680,50.320,78.493,21.507,60.40,0.950,bicubic,-47.650,-21.107,-33 +nextvit_large.bd_ssld_6m_in1k,224,49.547,50.453,80.813,19.187,57.87,0.950,bicubic,-47.833,-18.857,-45 +inception_next_base.sail_in1k_384,384,49.493,50.507,78.560,21.440,86.67,1.000,bicubic,-47.777,-21.100,-20 +regnety_160.lion_in12k_ft_in1k,224,49.453,50.547,78.467,21.533,83.59,0.950,bicubic,-47.797,-21.073,-19 +cait_s24_384.fb_dist_in1k,384,49.427,50.573,78.653,21.347,47.06,1.000,bicubic,-47.643,-20.767,+27 +nextvit_large.bd_in1k_384,384,49.320,50.680,78.413,21.587,57.87,1.000,bicubic,-47.690,-20.947,+37 +xcit_medium_24_p16_384.fb_dist_in1k,384,49.253,50.747,79.747,20.253,84.40,1.000,bicubic,-48.057,-19.753,-36 +hiera_base_plus_224.mae_in1k_ft_in1k,224,49.173,50.827,76.507,23.493,69.90,0.900,bicubic,-47.767,-22.893,+54 +regnety_160.sw_in12k_ft_in1k,224,49.107,50.893,77.813,22.187,83.59,0.950,bicubic,-48.113,-21.777,-15 +deit_base_distilled_patch16_384.fb_in1k,384,49.027,50.973,78.960,21.040,87.63,1.000,bicubic,-47.973,-20.570,+40 +caformer_s18.sail_in1k_384,384,48.907,51.093,78.720,21.280,26.34,1.000,bicubic,-48.163,-20.700,+20 +coat_lite_medium_384.in1k,384,48.840,51.160,78.520,21.480,44.57,1.000,bicubic,-48.310,-20.950,0 +convnext_base.clip_laion2b_augreg_ft_in1k,256,48.693,51.307,79.933,20.067,88.59,1.000,bicubic,-48.577,-19.727,-32 +caformer_b36.sail_in1k,224,48.627,51.373,75.707,24.293,98.75,1.000,bicubic,-48.373,-23.813,+37 +convnextv2_base.fcmae_ft_in1k,288,48.613,51.387,78.880,21.120,88.72,1.000,bicubic,-48.627,-20.660,-28 +efficientvit_l2.r256_in1k,256,48.587,51.413,77.907,22.093,63.71,1.000,bicubic,-48.263,-21.393,+64 +deit3_large_patch16_224.fb_in1k,224,48.573,51.427,78.027,21.973,304.37,0.900,bicubic,-48.407,-21.243,+37 +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,384,48.493,51.507,77.400,22.600,88.30,1.000,bicubic,-48.877,-22.120,-58 +flexivit_large.300ep_in1k,240,48.453,51.547,78.627,21.373,304.36,0.950,bicubic,-48.797,-20.863,-33 +deit3_medium_patch16_224.fb_in22k_ft_in1k,224,48.200,51.800,77.080,22.920,38.85,1.000,bicubic,-48.790,-22.350,+32 +tf_efficientnetv2_s.in21k_ft_in1k,384,48.067,51.933,77.653,22.347,21.46,1.000,bicubic,-48.683,-21.637,+79 +tf_efficientnet_b8.ra_in1k,672,48.013,51.987,76.467,23.533,87.41,0.954,bicubic,-49.197,-23.073,-24 +xcit_large_24_p8_224.fb_dist_in1k,224,47.907,52.093,79.147,20.853,188.93,1.000,bicubic,-49.143,-20.273,+12 +convnext_small.in12k_ft_in1k,224,47.907,52.093,77.253,22.747,50.22,0.950,bicubic,-49.303,-22.247,-28 +regnety_2560.seer_ft_in1k,384,47.787,52.213,76.333,23.667,"1,282.60",1.000,bicubic,-49.453,-23.217,-36 +hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k,256,47.667,52.333,76.840,23.160,34.36,0.950,bicubic,-49.513,-22.720,-22 +resnest269e.in1k,416,47.640,52.360,74.080,25.920,110.93,0.928,bicubic,-48.880,-25.250,+144 +convnext_tiny.in12k_ft_in1k,288,47.533,52.467,78.973,21.027,28.59,1.000,bicubic,-49.567,-20.547,-5 +vit_base_patch16_clip_224.openai_ft_in1k,224,47.453,52.547,78.147,21.853,86.57,0.900,bicubic,-49.637,-21.313,-4 +regnetz_e8.ra3_in1k,320,47.347,52.653,75.760,24.240,57.70,1.000,bicubic,-49.863,-23.840,-33 +convformer_s36.sail_in22k_ft_in1k,224,47.307,52.693,77.307,22.693,40.01,1.000,bicubic,-49.773,-22.243,-2 +nextvit_base.bd_ssld_6m_in1k,224,47.280,52.720,78.560,21.440,44.82,0.950,bicubic,-49.990,-21.000,-52 +xcit_large_24_p8_224.fb_in1k,224,46.867,53.133,74.400,25.600,188.93,1.000,bicubic,-49.533,-24.750,+170 +xcit_small_24_p16_384.fb_dist_in1k,384,46.773,53.227,76.987,23.013,47.67,1.000,bicubic,-50.357,-22.513,-20 +mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k,480,46.720,53.280,77.387,22.613,32.59,1.000,bicubic,-50.460,-22.173,-33 +convnext_large.fb_in1k,288,46.667,53.333,76.733,23.267,197.77,1.000,bicubic,-50.423,-22.717,-9 +tf_efficientnet_b8.ap_in1k,672,46.493,53.507,76.187,23.813,87.41,0.954,bicubic,-50.627,-23.423,-22 +hgnet_base.ssld_in1k,288,46.480,53.520,76.213,23.787,71.58,1.000,bicubic,-50.950,-23.407,-92 +nextvit_base.bd_in1k_384,384,46.400,53.600,76.800,23.200,44.82,1.000,bicubic,-50.510,-22.600,+30 +resnetv2_50x3_bit.goog_in21k_ft_in1k,448,46.347,53.653,76.640,23.360,217.32,1.000,bilinear,-50.383,-22.700,+66 +efficientnetv2_rw_m.agc_in1k,416,46.333,53.667,75.733,24.267,53.24,1.000,bicubic,-50.667,-23.617,+7 +vit_medium_patch16_reg4_gap_256.sbb_in12k_ft_in1k,256,46.133,53.867,77.000,23.000,38.88,0.950,bicubic,-51.037,-22.390,-34 +dm_nfnet_f2.dm_in1k,352,46.120,53.880,74.613,25.387,193.78,0.920,bicubic,-50.990,-24.847,-24 +hgnetv2_b5.ssld_stage2_ft_in1k,288,45.947,54.053,77.773,22.227,39.57,1.000,bicubic,-51.423,-21.897,-85 +volo_d2_224.sail_in1k,224,45.893,54.107,75.147,24.853,58.68,0.960,bicubic,-51.097,-24.243,+8 +swinv2_base_window16_256.ms_in1k,256,45.893,54.107,75.107,24.893,87.92,0.900,bicubic,-50.877,-24.233,+51 +resnext101_32x16d.fb_swsl_ig1b_ft_in1k,224,45.867,54.133,72.093,27.907,194.03,0.875,bilinear,-50.733,-27.407,+100 +caformer_m36.sail_in1k,224,45.773,54.227,74.533,25.467,56.20,1.000,bicubic,-51.117,-24.937,+27 +ecaresnet269d.ra2_in1k,352,45.747,54.253,74.947,25.053,102.09,1.000,bicubic,-51.343,-24.533,-23 +convnextv2_base.fcmae_ft_in1k,224,45.733,54.267,75.960,24.040,88.72,0.875,bicubic,-51.167,-23.460,+22 +dm_nfnet_f3.dm_in1k,320,45.440,54.560,72.360,27.640,254.92,0.940,bicubic,-51.750,-27.180,-50 +swin_base_patch4_window12_384.ms_in1k,384,45.413,54.587,74.613,25.387,87.90,1.000,bicubic,-51.167,-24.707,+104 +vit_small_patch16_384.augreg_in21k_ft_in1k,384,45.360,54.640,76.160,23.840,22.20,1.000,bicubic,-51.340,-23.080,+66 +tf_efficientnet_b7.ap_in1k,600,45.267,54.733,74.027,25.973,66.35,0.949,bicubic,-51.943,-25.443,-58 +resnext101_32x8d.fb_wsl_ig1b_ft_in1k,224,45.240,54.760,70.853,29.147,88.79,0.875,bilinear,-51.060,-28.377,+186 +xcit_medium_24_p8_224.fb_dist_in1k,224,45.227,54.773,76.760,23.240,84.32,1.000,bicubic,-51.703,-22.490,+9 +vit_small_r26_s32_384.augreg_in21k_ft_in1k,384,45.227,54.773,75.467,24.533,36.47,1.000,bicubic,-51.453,-23.903,+70 +convnext_small.fb_in22k_ft_in1k,224,45.147,54.853,77.493,22.507,50.22,0.875,bicubic,-51.823,-22.087,-1 +coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,224,45.133,54.867,73.960,26.040,41.72,0.950,bicubic,-51.537,-25.370,+75 +convnextv2_tiny.fcmae_ft_in22k_in1k_384,384,45.120,54.880,76.587,23.413,28.64,1.000,bicubic,-52.120,-22.923,-72 +mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k,448,44.880,55.120,74.573,25.427,32.59,0.950,bicubic,-52.130,-24.927,-15 +eca_nfnet_l2.ra3_in1k,384,44.760,55.240,75.680,24.320,56.72,1.000,bicubic,-52.320,-23.930,-32 +hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k,256,44.720,55.280,74.467,25.533,34.36,0.950,bicubic,-51.980,-24.883,+59 +maxxvit_rmlp_small_rw_256.sw_in1k,256,44.493,55.507,75.133,24.867,66.01,0.950,bicubic,-52.297,-24.197,+29 +regnety_120.sw_in12k_ft_in1k,224,44.467,55.533,74.653,25.347,51.82,0.950,bicubic,-52.703,-24.807,-59 +ecaresnet269d.ra2_in1k,320,44.347,55.653,73.947,26.053,102.09,0.950,bicubic,-52.763,-25.553,-44 +tiny_vit_21m_224.dist_in22k_ft_in1k,224,44.307,55.693,74.947,25.053,21.20,0.950,bicubic,-52.893,-24.543,-67 +tf_efficientnetv2_l.in1k,384,44.067,55.933,72.027,27.973,118.52,1.000,bicubic,-53.203,-27.473,-84 +rexnetr_300.sw_in12k_ft_in1k,288,44.053,55.947,76.693,23.307,34.81,1.000,bicubic,-52.797,-22.827,+10 +resnest200e.in1k,320,43.987,56.013,73.400,26.600,70.20,0.909,bicubic,-52.623,-25.900,+73 +crossvit_18_dagger_408.in1k,408,43.840,56.160,73.613,26.387,44.61,1.000,bicubic,-52.690,-25.647,+98 +cait_xs24_384.fb_dist_in1k,384,43.640,56.360,74.840,25.160,26.67,1.000,bicubic,-52.930,-24.400,+87 +seresnextaa101d_32x8d.ah_in1k,288,43.613,56.387,73.093,26.907,93.59,1.000,bicubic,-53.327,-26.247,-12 +resnetrs200.tf_in1k,320,43.493,56.507,72.533,27.467,93.21,1.000,bicubic,-53.227,-26.827,+40 +swin_small_patch4_window7_224.ms_in22k_ft_in1k,224,43.453,56.547,76.267,23.733,49.61,0.900,bicubic,-53.097,-23.013,+89 +caformer_s18.sail_in22k_ft_in1k,224,43.333,56.667,74.920,25.080,26.34,1.000,bicubic,-53.377,-24.560,+39 +hiera_base_224.mae_in1k_ft_in1k,224,43.333,56.667,73.160,26.840,51.52,0.900,bicubic,-53.257,-26.000,+77 +hgnetv2_b5.ssld_stage1_in22k_in1k,288,43.293,56.707,75.267,24.733,39.57,1.000,bicubic,-53.977,-24.273,-100 +mvitv2_base.fb_in1k,224,43.293,56.707,73.920,26.080,51.47,0.900,bicubic,-53.477,-25.330,+20 +xcit_small_12_p16_384.fb_dist_in1k,384,43.227,56.773,74.000,26.000,26.25,1.000,bicubic,-53.723,-25.400,-21 +tresnet_xl.miil_in1k_448,448,43.213,56.787,72.147,27.853,78.44,0.875,bilinear,-52.757,-26.973,+268 +vit_base_patch16_224.augreg_in21k_ft_in1k,224,43.120,56.880,72.573,27.427,86.57,0.900,bicubic,-53.760,-26.817,-6 +regnetz_e8.ra3_in1k,256,43.093,56.907,72.200,27.800,57.70,0.940,bicubic,-53.597,-27.170,+40 +xcit_medium_24_p8_224.fb_in1k,224,43.093,56.907,70.080,29.920,84.32,1.000,bicubic,-53.027,-28.810,+217 +rdnet_large.nv_in1k,224,42.787,57.213,73.613,26.387,186.27,0.900,bicubic,-54.123,-25.847,-15 +edgenext_base.in21k_ft_in1k,320,42.773,57.227,75.427,24.573,18.51,1.000,bicubic,-53.967,-23.993,+19 +hrnet_w48_ssld.paddle_in1k,288,42.693,57.307,72.067,27.933,77.47,1.000,bilinear,-54.347,-27.303,-50 +efficientvit_l2.r224_in1k,224,42.653,57.347,74.320,25.680,63.71,1.000,bicubic,-54.187,-24.930,-6 +vit_base_patch32_clip_384.openai_ft_in12k_in1k,384,42.573,57.427,72.640,27.360,88.30,0.950,bicubic,-54.527,-26.880,-65 +coatnet_rmlp_2_rw_224.sw_in1k,224,42.547,57.453,71.040,28.960,73.88,0.950,bicubic,-53.983,-28.040,+80 +nextvit_small.bd_ssld_6m_in1k,224,42.533,57.467,75.467,24.533,31.76,0.950,bicubic,-54.427,-24.013,-33 +gcvit_base.in1k,224,42.520,57.480,73.440,26.560,90.32,0.875,bicubic,-54.040,-25.790,+70 +resnetrs420.tf_in1k,416,42.427,57.573,70.160,29.840,191.89,1.000,bicubic,-54.483,-29.070,-25 +vit_medium_patch16_gap_256.sw_in12k_ft_in1k,256,42.413,57.587,74.240,25.760,38.86,0.950,bicubic,-54.257,-24.920,+37 +tf_efficientnetv2_m.in1k,480,42.400,57.600,72.213,27.787,54.14,1.000,bicubic,-54.830,-27.257,-104 +maxvit_large_tf_224.in1k,224,42.360,57.640,69.213,30.787,211.79,0.950,bicubic,-54.660,-30.027,-54 +dm_nfnet_f1.dm_in1k,320,42.293,57.707,71.293,28.707,132.63,0.910,bicubic,-54.767,-28.317,-63 +xcit_tiny_24_p8_384.fb_dist_in1k,384,42.267,57.733,72.773,27.227,12.11,1.000,bicubic,-54.313,-26.467,+56 +swinv2_small_window16_256.ms_in1k,256,42.267,57.733,72.747,27.253,49.73,0.900,bicubic,-54.203,-26.653,+84 +tf_efficientnet_b7.ra_in1k,600,42.253,57.747,72.547,27.453,66.35,0.949,bicubic,-54.747,-26.943,-51 +maxvit_rmlp_small_rw_224.sw_in1k,224,42.200,57.800,72.373,27.627,64.90,0.900,bicubic,-54.360,-26.757,+62 +convformer_s18.sail_in1k_384,384,42.187,57.813,74.013,25.987,26.77,1.000,bicubic,-54.853,-25.627,-64 +maxvit_base_tf_224.in1k,224,42.027,57.973,70.053,29.947,119.47,0.950,bicubic,-54.953,-29.277,-48 +convnext_tiny.fb_in22k_ft_in1k_384,384,42.013,57.987,73.533,26.467,28.59,1.000,bicubic,-55.097,-25.977,-84 +vit_base_patch16_clip_224.laion2b_ft_in1k,224,41.960,58.040,73.987,26.013,86.57,1.000,bicubic,-55.160,-25.673,-87 +convnext_base.fb_in1k,288,41.907,58.093,73.840,26.160,88.59,1.000,bicubic,-54.913,-25.580,-19 +eca_nfnet_l2.ra3_in1k,320,41.907,58.093,72.933,27.067,56.72,0.900,bicubic,-54.683,-26.477,+45 +crossvit_15_dagger_408.in1k,408,41.787,58.213,71.947,28.053,28.50,1.000,bicubic,-54.633,-27.303,+90 +nextvit_small.bd_in1k_384,384,41.747,58.253,72.747,27.253,31.76,1.000,bicubic,-55.073,-26.623,-20 +convnextv2_tiny.fcmae_ft_in22k_in1k,288,41.733,58.267,75.133,24.867,28.64,1.000,bicubic,-55.107,-24.317,-29 +xcit_small_24_p8_224.fb_in1k,224,41.653,58.347,70.867,29.133,47.63,1.000,bicubic,-54.777,-28.303,+84 +vit_large_r50_s32_224.augreg_in21k_ft_in1k,224,41.613,58.387,70.280,29.720,328.99,0.900,bicubic,-55.177,-29.090,-18 +resnext101_32x4d.fb_swsl_ig1b_ft_in1k,224,41.600,58.400,71.853,28.147,44.18,0.875,bilinear,-54.840,-27.277,+76 +xcit_small_24_p8_224.fb_dist_in1k,224,41.533,58.467,73.667,26.333,47.63,1.000,bicubic,-55.357,-25.813,-40 +resnetaa101d.sw_in12k_ft_in1k,288,41.533,58.467,72.507,27.493,44.57,1.000,bicubic,-55.137,-26.993,+17 +vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k,256,41.427,58.573,71.627,28.373,63.95,0.950,bicubic,-55.193,-27.653,+26 +convnext_large.fb_in1k,224,41.213,58.787,73.280,26.720,197.77,0.875,bicubic,-55.537,-26.090,-15 +swinv2_base_window8_256.ms_in1k,256,41.160,58.840,72.560,27.440,87.92,0.900,bicubic,-55.390,-26.850,+47 +deit3_small_patch16_224.fb_in22k_ft_in1k,224,41.080,58.920,72.000,28.000,22.06,1.000,bicubic,-55.590,-27.360,+13 +caformer_s36.sail_in1k,224,41.080,58.920,70.920,29.080,39.30,1.000,bicubic,-55.650,-28.620,-12 +seresnext101d_32x8d.ah_in1k,288,40.933,59.067,70.720,29.280,93.59,1.000,bicubic,-55.797,-28.580,-13 +davit_base.msft_in1k,224,40.787,59.213,72.587,27.413,87.95,0.950,bicubic,-56.143,-26.933,-56 +maxvit_rmlp_tiny_rw_256.sw_in1k,256,40.733,59.267,70.880,29.120,29.15,0.950,bicubic,-55.687,-28.280,+73 +hgnet_base.ssld_in1k,224,40.693,59.307,70.667,29.333,71.58,0.965,bicubic,-56.387,-28.843,-96 +regnety_1280.seer_ft_in1k,384,40.667,59.333,70.587,29.413,644.81,1.000,bicubic,-56.213,-28.943,-48 +flexivit_base.1200ep_in1k,240,40.587,59.413,72.187,27.813,86.59,0.950,bicubic,-56.173,-27.173,-27 +tf_efficientnet_b6.ap_in1k,528,40.373,59.627,71.387,28.613,43.04,0.942,bicubic,-56.747,-28.073,-110 +resmlp_big_24_224.fb_in22k_ft_in1k,224,40.333,59.667,74.560,25.440,129.14,0.875,bicubic,-56.267,-24.980,+20 +convformer_b36.sail_in1k,224,40.280,59.720,69.107,30.893,99.88,1.000,bicubic,-56.630,-30.293,-58 +deit3_small_patch16_384.fb_in1k,384,40.227,59.773,70.000,30.000,22.21,1.000,bicubic,-55.993,-29.210,+134 +mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k,384,40.133,59.867,72.453,27.547,32.59,0.950,bicubic,-56.737,-26.907,-53 +tresnet_l.miil_in1k_448,448,40.133,59.867,69.720,30.280,55.99,0.875,bilinear,-55.757,-29.400,+233 +hgnetv2_b5.ssld_stage2_ft_in1k,224,39.907,60.093,72.320,27.680,39.57,0.965,bicubic,-57.213,-27.280,-117 +deit_base_patch16_384.fb_in1k,384,39.880,60.120,70.373,29.627,86.86,1.000,bicubic,-56.290,-28.827,+151 +flexivit_base.600ep_in1k,240,39.800,60.200,71.680,28.320,86.59,0.950,bicubic,-56.850,-27.650,0 +convnextv2_tiny.fcmae_ft_in22k_in1k,224,39.720,60.280,72.587,27.413,28.64,0.875,bicubic,-56.750,-26.623,+43 +mobilenetv4_hybrid_large.ix_e600_r384_in1k,448,39.707,60.293,72.480,27.520,37.76,1.000,bicubic,-56.893,-26.900,+13 +regnetz_040_h.ra3_in1k,320,39.707,60.293,70.960,29.040,28.94,1.000,bicubic,-57.013,-28.540,-26 +vit_little_patch16_reg1_gap_256.sbb_in12k_ft_in1k,256,39.693,60.307,71.133,28.867,22.52,0.950,bicubic,-56.647,-28.127,+90 +swin_s3_base_224.ms_in1k,224,39.627,60.373,70.613,29.387,71.13,0.900,bicubic,-56.643,-28.727,+107 +vit_base_patch16_rope_reg1_gap_256.sbb_in1k,256,39.613,60.387,70.467,29.533,86.43,0.950,bicubic,-56.827,-29.013,+50 +mobilenetv4_hybrid_large.e600_r384_in1k,448,39.600,60.400,72.880,27.120,37.76,1.000,bicubic,-57.100,-26.600,-22 +regnetz_d8.ra3_in1k,320,39.480,60.520,71.227,28.773,23.37,1.000,bicubic,-57.150,-28.193,-5 +resnetrs350.tf_in1k,384,39.400,60.600,68.293,31.707,163.96,1.000,bicubic,-57.370,-31.037,-48 +flexivit_base.300ep_in1k,240,39.320,60.680,70.733,29.267,86.59,0.950,bicubic,-57.290,-28.607,+1 +gcvit_small.in1k,224,39.253,60.747,70.520,29.480,51.09,0.875,bicubic,-57.017,-28.640,+102 +seresnext101_32x8d.ah_in1k,288,39.040,60.960,68.987,31.013,93.57,1.000,bicubic,-57.740,-30.363,-52 +deit3_base_patch16_224.fb_in1k,224,38.920,61.080,70.747,29.253,86.59,0.900,bicubic,-57.370,-28.443,+95 +mobilenetv4_conv_aa_large.e600_r384_in1k,480,38.893,61.107,70.707,29.293,32.59,1.000,bicubic,-57.567,-28.593,+36 +regnetz_d8_evos.ch_in1k,320,38.840,61.160,71.333,28.667,23.46,1.000,bicubic,-57.730,-28.127,+8 +vit_large_patch32_384.orig_in21k_ft_in1k,384,38.840,61.160,68.627,31.373,306.63,1.000,bicubic,-57.000,-30.533,+227 +volo_d1_224.sail_in1k,224,38.693,61.307,70.120,29.880,26.63,0.960,bicubic,-57.627,-29.120,+84 +mvitv2_small.fb_in1k,224,38.560,61.440,69.960,30.040,34.87,0.900,bicubic,-57.790,-29.430,+71 +coat_lite_medium.in1k,224,38.440,61.560,71.093,28.907,44.57,0.900,bicubic,-58.030,-28.267,+29 +efficientnetv2_rw_m.agc_in1k,320,38.427,61.573,70.133,29.867,53.24,1.000,bicubic,-58.163,-29.177,-5 +regnetz_040.ra3_in1k,320,38.427,61.573,70.067,29.933,27.12,1.000,bicubic,-58.283,-29.443,-38 +resnetv2_101x1_bit.goog_in21k_ft_in1k,448,38.253,61.747,70.227,29.773,44.54,1.000,bilinear,-57.847,-29.043,+146 +xcit_small_12_p8_224.fb_dist_in1k,224,38.147,61.853,71.333,28.667,26.21,1.000,bicubic,-58.603,-28.087,-57 +davit_small.msft_in1k,224,38.107,61.893,70.613,29.387,49.75,0.950,bicubic,-58.533,-28.747,-23 +convnext_tiny.in12k_ft_in1k,224,38.080,61.920,71.840,28.160,28.59,0.950,bicubic,-58.660,-27.620,-56 +rdnet_base.nv_in1k,224,38.013,61.987,70.000,30.000,87.45,0.900,bicubic,-58.677,-29.240,-36 +convformer_m36.sail_in1k,224,37.920,62.080,66.933,33.067,57.05,1.000,bicubic,-58.770,-32.417,-36 +resnet200d.ra2_in1k,320,37.893,62.107,68.333,31.667,64.69,1.000,bicubic,-58.837,-31.037,-54 +tf_efficientnet_b7.aa_in1k,600,37.760,62.240,69.427,30.573,66.35,0.949,bicubic,-58.780,-29.883,+4 +focalnet_base_srf.ms_in1k,224,37.747,62.253,69.707,30.293,88.15,0.900,bicubic,-58.843,-29.723,-11 +maxvit_small_tf_224.in1k,224,37.693,62.307,68.373,31.627,68.93,0.950,bicubic,-58.997,-30.697,-43 +swinv2_small_window8_256.ms_in1k,256,37.667,62.333,69.960,30.040,49.73,0.900,bicubic,-58.653,-29.340,+72 +vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k,256,37.627,62.373,68.867,31.133,60.23,0.950,bicubic,-58.733,-30.513,+48 +tf_efficientnetv2_s.in21k_ft_in1k,300,37.613,62.387,69.667,30.333,21.46,1.000,bicubic,-58.707,-29.663,+69 +fastvit_ma36.apple_dist_in1k,256,37.533,62.467,70.973,29.027,44.07,0.950,bicubic,-59.267,-28.357,-79 +focalnet_base_lrf.ms_in1k,224,37.533,62.467,68.600,31.400,88.75,0.900,bicubic,-58.947,-30.530,+8 +hgnetv2_b5.ssld_stage1_in22k_in1k,224,37.480,62.520,69.827,30.173,39.57,0.965,bicubic,-59.550,-29.743,-134 +seresnet152d.ra2_in1k,320,37.480,62.520,69.080,30.920,66.84,1.000,bicubic,-59.320,-30.330,-82 +efficientvit_b3.r288_in1k,288,37.347,62.653,69.653,30.347,48.65,1.000,bicubic,-59.283,-29.807,-35 +twins_svt_large.in1k,224,37.347,62.653,69.227,30.773,99.27,0.900,bicubic,-58.913,-29.943,+75 +xcit_large_24_p16_224.fb_dist_in1k,224,37.307,62.693,71.440,28.560,189.10,1.000,bicubic,-59.513,-27.860,-89 +xcit_small_12_p8_224.fb_in1k,224,37.307,62.693,68.133,31.867,26.21,1.000,bicubic,-58.803,-31.137,+126 +swin_s3_small_224.ms_in1k,224,37.053,62.947,68.147,31.853,49.74,0.900,bicubic,-59.187,-31.193,+81 +eca_nfnet_l1.ra2_in1k,320,37.027,62.973,70.573,29.427,41.41,1.000,bicubic,-59.723,-28.727,-76 +vit_medium_patch16_rope_reg1_gap_256.sbb_in1k,256,36.947,63.053,68.893,31.107,38.74,0.950,bicubic,-59.473,-30.497,+17 +regnetz_d32.ra3_in1k,320,36.813,63.187,70.080,29.920,27.58,0.950,bicubic,-59.787,-29.210,-32 +convnext_small.fb_in1k,288,36.787,63.213,70.947,29.053,50.22,1.000,bicubic,-59.773,-28.393,-21 +seresnextaa101d_32x8d.ah_in1k,224,36.760,63.240,66.347,33.653,93.59,0.950,bicubic,-59.570,-32.883,+48 +pvt_v2_b4.in1k,224,36.747,63.253,68.680,31.320,62.56,0.900,bicubic,-59.633,-30.500,+26 +convnext_base.fb_in1k,224,36.693,63.307,69.947,30.053,88.59,0.875,bicubic,-59.737,-29.443,+8 +pit_b_distilled_224.in1k,224,36.640,63.360,67.840,32.160,74.79,0.900,bicubic,-59.610,-31.290,+67 +regnety_064.ra3_in1k,288,36.640,63.360,67.560,32.440,30.58,1.000,bicubic,-59.720,-31.770,+29 +convnext_tiny.fb_in22k_ft_in1k,224,36.600,63.400,69.987,30.013,28.59,0.875,bicubic,-59.640,-29.183,+67 +efficientnetv2_rw_s.ra2_in1k,384,36.587,63.413,68.040,31.960,23.94,1.000,bicubic,-59.953,-31.320,-22 +vit_betwixt_patch16_reg4_gap_256.sbb_in1k,256,36.560,63.440,68.040,31.960,60.40,0.950,bicubic,-59.530,-30.830,+121 +vit_base_patch32_384.augreg_in21k_ft_in1k,384,36.520,63.480,69.147,30.853,88.30,1.000,bicubic,-59.980,-30.263,-17 +regnety_160.deit_in1k,288,36.480,63.520,68.813,31.187,83.59,1.000,bicubic,-59.880,-30.357,+21 +fastvit_sa36.apple_dist_in1k,256,36.413,63.587,69.573,30.427,31.53,0.900,bicubic,-59.937,-29.647,+28 +hiera_small_224.mae_in1k_ft_in1k,224,36.413,63.587,68.000,32.000,35.01,0.900,bicubic,-59.917,-31.170,+40 +resnext101_64x4d.c1_in1k,288,36.347,63.653,65.960,34.040,83.46,1.000,bicubic,-59.783,-33.270,+103 +mobilenetv4_hybrid_large.ix_e600_r384_in1k,384,36.333,63.667,69.040,30.960,37.76,0.950,bicubic,-60.127,-30.160,-9 +hgnet_small.ssld_in1k,288,36.307,63.693,70.800,29.200,24.36,1.000,bicubic,-60.623,-28.760,-134 +rexnetr_300.sw_in12k_ft_in1k,224,36.293,63.707,69.880,30.120,34.81,0.950,bicubic,-60.347,-29.490,-61 +pvt_v2_b5.in1k,224,36.280,63.720,68.400,31.600,81.96,0.900,bicubic,-60.070,-30.940,+28 +cait_xxs36_384.fb_dist_in1k,384,36.093,63.907,67.413,32.587,17.37,1.000,bicubic,-59.777,-31.727,+174 +efficientvit_l1.r224_in1k,224,35.960,64.040,68.000,32.000,52.65,1.000,bicubic,-60.370,-31.050,+33 +nest_base_jx.goog_in1k,224,35.947,64.053,66.600,33.400,67.72,0.875,bicubic,-60.303,-32.630,+51 +swin_base_patch4_window7_224.ms_in1k,224,35.773,64.227,68.213,31.787,87.77,0.900,bicubic,-60.317,-30.937,+109 +regnety_640.seer_ft_in1k,384,35.760,64.240,68.000,32.000,281.38,1.000,bicubic,-61.070,-31.660,-118 +repvgg_d2se.rvgg_in1k,320,35.720,64.280,66.667,33.333,133.33,1.000,bilinear,-60.960,-32.623,-77 +maxvit_tiny_rw_224.sw_in1k,224,35.720,64.280,65.747,34.253,29.06,0.950,bicubic,-60.520,-33.323,+52 +coatnet_1_rw_224.sw_in1k,224,35.693,64.307,66.987,33.013,41.72,0.950,bicubic,-60.337,-32.173,+122 +tf_efficientnet_b3.ns_jft_in1k,300,35.680,64.320,67.787,32.213,12.23,0.904,bicubic,-60.700,-31.603,+1 +mobilenetv4_hybrid_large.e600_r384_in1k,384,35.667,64.333,69.440,30.560,37.76,0.950,bicubic,-60.763,-29.790,-15 +edgenext_base.in21k_ft_in1k,256,35.640,64.360,69.440,30.560,18.51,0.950,bicubic,-60.720,-29.780,+2 +vit_betwixt_patch16_reg1_gap_256.sbb_in1k,256,35.640,64.360,67.040,32.960,60.40,0.950,bicubic,-60.590,-32.380,+51 +cs3se_edgenet_x.c2ns_in1k,320,35.547,64.453,67.640,32.360,50.72,1.000,bicubic,-60.893,-31.770,-22 +resnetrs200.tf_in1k,256,35.440,64.560,64.827,35.173,93.21,1.000,bicubic,-60.760,-34.453,+61 +hgnetv2_b4.ssld_stage2_ft_in1k,288,35.400,64.600,69.973,30.027,19.80,1.000,bicubic,-61.430,-29.477,-130 +tf_efficientnetv2_m.in1k,384,35.240,64.760,65.453,34.547,54.14,1.000,bicubic,-61.560,-33.997,-124 +resnetrs420.tf_in1k,320,35.160,64.840,63.627,36.373,191.89,1.000,bicubic,-61.450,-35.643,-71 +sequencer2d_l.in1k,224,35.093,64.907,67.093,32.907,54.30,0.875,bicubic,-61.047,-32.157,+79 +regnety_080.ra3_in1k,288,35.080,64.920,66.733,33.267,39.18,1.000,bicubic,-61.440,-32.637,-46 +tf_efficientnet_b6.aa_in1k,528,34.987,65.013,67.360,32.640,43.04,0.942,bicubic,-61.693,-32.230,-90 +mobilenetv4_hybrid_medium.ix_e550_r384_in1k,448,34.947,65.053,69.187,30.813,11.07,1.000,bicubic,-61.373,-30.123,+15 +gcvit_tiny.in1k,224,34.920,65.080,66.307,33.693,28.22,0.875,bicubic,-61.270,-32.693,+57 +inception_next_base.sail_in1k,224,34.853,65.147,66.227,33.773,86.67,0.950,bicubic,-61.697,-32.863,-56 +vit_medium_patch16_reg1_gap_256.sbb_in1k,256,34.573,65.427,67.653,32.347,38.88,0.950,bicubic,-61.777,-31.567,+1 +dm_nfnet_f2.dm_in1k,256,34.573,65.427,63.920,36.080,193.78,0.920,bicubic,-62.157,-35.440,-113 +fastvit_ma36.apple_in1k,256,34.533,65.467,67.120,32.880,44.07,0.950,bicubic,-61.957,-32.180,-50 +resnetrs270.tf_in1k,352,34.520,65.480,65.120,34.880,129.86,1.000,bicubic,-62.180,-34.270,-105 +tf_efficientnet_b5.ap_in1k,456,34.387,65.613,67.173,32.827,30.39,0.934,bicubic,-62.323,-32.307,-109 +xcit_tiny_12_p8_384.fb_dist_in1k,384,34.347,65.653,66.320,33.680,6.71,1.000,bicubic,-61.703,-32.920,+93 +mobilenetv4_conv_large.e600_r384_in1k,448,34.333,65.667,67.747,32.253,32.59,1.000,bicubic,-61.907,-31.373,+27 +deit3_medium_patch16_224.fb_in1k,224,34.160,65.840,65.880,34.120,38.85,0.900,bicubic,-61.930,-33.430,+79 +coat_small.in1k,224,34.147,65.853,66.013,33.987,21.69,0.900,bicubic,-61.773,-33.147,+127 +vit_base_patch16_224_miil.in21k_ft_in1k,224,34.120,65.880,64.547,35.453,86.54,0.875,bilinear,-62.350,-34.613,-51 +vit_medium_patch16_reg4_gap_256.sbb_in1k,256,34.040,65.960,67.280,32.720,38.88,0.950,bicubic,-62.350,-32.050,-26 +xcit_medium_24_p16_224.fb_dist_in1k,224,34.000,66.000,67.640,32.360,84.40,1.000,bicubic,-62.620,-31.580,-94 +seresnext101d_32x8d.ah_in1k,224,34.000,66.000,64.320,35.680,93.59,0.950,bicubic,-62.460,-34.870,-48 +resnet152d.ra2_in1k,320,33.813,66.187,65.427,34.573,60.21,1.000,bicubic,-62.567,-33.923,-28 +resmlp_big_24_224.fb_distilled_in1k,224,33.787,66.213,69.360,30.640,129.14,0.875,bicubic,-62.673,-29.960,-55 +regnetv_064.ra3_in1k,288,33.787,66.213,67.667,32.333,30.58,1.000,bicubic,-62.663,-31.693,-49 +tresnet_m.miil_in1k_448,448,33.787,66.213,64.320,35.680,31.39,0.875,bilinear,-61.193,-34.660,+353 +coatnet_rmlp_1_rw_224.sw_in1k,224,33.653,66.347,65.280,34.720,41.69,0.950,bicubic,-62.297,-33.880,+112 +caformer_s18.sail_in1k,224,33.613,66.387,65.200,34.800,26.34,1.000,bicubic,-62.577,-33.990,+39 +pvt_v2_b3.in1k,224,33.587,66.413,67.427,32.573,45.24,0.900,bicubic,-62.413,-31.773,+92 +xcit_tiny_24_p16_384.fb_dist_in1k,384,33.573,66.427,65.280,34.720,12.12,1.000,bicubic,-62.417,-34.030,+94 +rdnet_small.nv_in1k,224,33.547,66.453,66.867,33.133,50.44,0.900,bicubic,-62.783,-32.333,-12 +focalnet_small_srf.ms_in1k,224,33.547,66.453,65.853,34.147,49.89,0.900,bicubic,-62.543,-33.307,+65 +convformer_s18.sail_in22k_ft_in1k,224,33.440,66.560,68.240,31.760,26.77,1.000,bicubic,-63.170,-31.110,-102 +inception_next_small.sail_in1k,224,33.427,66.573,65.893,34.107,49.37,0.875,bicubic,-62.823,-33.327,+3 +regnetz_d8.ra3_in1k,256,33.400,66.600,66.573,33.427,23.37,0.940,bicubic,-63.000,-32.677,-47 +tiny_vit_11m_224.dist_in22k_ft_in1k,224,33.320,66.680,65.707,34.293,11.00,0.950,bicubic,-62.970,-33.483,-5 +resnetrs350.tf_in1k,288,33.280,66.720,62.053,37.947,163.96,1.000,bicubic,-63.400,-37.317,-120 +mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k,320,33.253,66.747,67.787,32.213,11.07,1.000,bicubic,-62.917,-31.363,+33 +convformer_s36.sail_in1k,224,33.213,66.787,63.547,36.453,40.01,1.000,bicubic,-63.367,-35.613,-95 +twins_pcpvt_large.in1k,224,33.107,66.893,67.960,32.040,60.99,0.900,bicubic,-63.053,-31.290,+36 +twins_svt_base.in1k,224,33.107,66.893,65.587,34.413,56.07,0.900,bicubic,-63.073,-33.753,+28 +resnext50_32x4d.fb_swsl_ig1b_ft_in1k,224,33.067,66.933,65.213,34.787,25.03,0.875,bilinear,-62.813,-33.877,+113 +nextvit_large.bd_in1k,224,33.027,66.973,67.000,33.000,57.87,0.950,bicubic,-63.343,-32.340,-46 +resnet152.a1h_in1k,288,33.027,66.973,63.160,36.840,60.19,1.000,bicubic,-63.193,-36.070,+11 +hrnet_w48_ssld.paddle_in1k,224,33.013,66.987,63.493,36.507,77.47,0.950,bilinear,-63.377,-35.737,-54 +fastvit_sa36.apple_in1k,256,33.000,67.000,65.960,34.040,31.53,0.900,bicubic,-63.240,-33.350,-2 +focalnet_small_lrf.ms_in1k,224,32.947,67.053,67.160,32.840,50.34,0.900,bicubic,-63.243,-32.070,+19 +tiny_vit_21m_224.in1k,224,32.880,67.120,66.933,33.067,21.20,0.950,bicubic,-63.320,-32.337,+15 +regnetz_d8_evos.ch_in1k,256,32.867,67.133,65.387,34.613,23.46,0.950,bicubic,-63.403,-34.093,-16 +regnety_320.seer_ft_in1k,384,32.787,67.213,66.013,33.987,145.05,1.000,bicubic,-63.563,-33.457,-42 +pit_b_224.in1k,224,32.773,67.227,62.253,37.747,73.76,0.900,bicubic,-62.857,-36.417,+162 +swinv2_cr_small_ns_224.sw_in1k,224,32.747,67.253,65.720,34.280,49.70,0.900,bicubic,-63.473,-33.520,+4 +xcit_large_24_p16_224.fb_in1k,224,32.733,67.267,61.773,38.227,189.10,1.000,bicubic,-62.727,-37.247,+215 +mobilevitv2_200.cvnets_in22k_ft_in1k_384,384,32.480,67.520,64.760,35.240,18.45,1.000,bicubic,-63.550,-34.340,+61 +resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,224,32.427,67.573,63.680,36.320,194.03,0.875,bilinear,-63.423,-35.490,+109 +efficientvit_b3.r256_in1k,256,32.413,67.587,65.707,34.293,48.65,1.000,bicubic,-63.937,-33.493,-41 +eca_nfnet_l1.ra2_in1k,256,32.413,67.587,65.293,34.707,41.41,0.900,bicubic,-63.837,-33.937,-18 +ecaresnet101d.miil_in1k,288,32.400,67.600,65.813,34.187,44.57,0.950,bicubic,-63.820,-33.497,-8 +swin_small_patch4_window7_224.ms_in1k,224,32.373,67.627,65.227,34.773,49.61,0.900,bicubic,-63.507,-33.883,+99 +convnextv2_nano.fcmae_ft_in22k_in1k_384,384,32.320,67.680,66.253,33.747,15.62,1.000,bicubic,-64.030,-33.147,-55 +resnetaa101d.sw_in12k_ft_in1k,224,32.307,67.693,65.173,34.827,44.57,0.950,bicubic,-64.033,-34.147,-44 +nest_small_jx.goog_in1k,224,32.307,67.693,63.813,36.187,38.35,0.875,bicubic,-63.663,-35.227,+75 +xception65.ra3_in1k,299,32.280,67.720,62.480,37.520,39.92,0.940,bicubic,-64.080,-36.760,-62 +tf_efficientnetv2_b3.in21k_ft_in1k,300,32.093,67.907,65.773,34.227,14.36,0.900,bicubic,-64.127,-33.477,-11 +mobilevitv2_175.cvnets_in22k_ft_in1k_384,384,32.093,67.907,64.187,35.813,14.25,1.000,bicubic,-64.107,-34.943,-3 +resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,224,32.027,67.973,63.573,36.427,236.34,0.875,bicubic,-64.063,-35.617,+28 +convnextv2_tiny.fcmae_ft_in1k,288,31.973,68.027,67.120,32.880,28.64,1.000,bicubic,-64.237,-32.120,-9 +regnetz_040_h.ra3_in1k,256,31.960,68.040,63.120,36.880,28.94,1.000,bicubic,-64.360,-36.080,-45 +convnext_tiny_hnf.a2h_in1k,288,31.960,68.040,62.480,37.520,28.59,1.000,bicubic,-64.050,-36.600,+51 +seresnext101_32x8d.ah_in1k,224,31.907,68.093,61.253,38.747,93.57,0.950,bicubic,-64.483,-37.957,-77 +efficientvit_b3.r224_in1k,224,31.880,68.120,64.493,35.507,48.65,0.950,bicubic,-64.140,-34.657,+47 +rexnetr_200.sw_in12k_ft_in1k,288,31.813,68.187,67.520,32.480,16.52,1.000,bicubic,-64.407,-31.610,-20 +swinv2_tiny_window16_256.ms_in1k,256,31.787,68.213,65.587,34.413,28.35,0.900,bicubic,-64.193,-33.513,+60 +resnext101_64x4d.tv_in1k,224,31.640,68.360,63.747,36.253,83.46,0.875,bilinear,-64.390,-35.343,+38 +maxvit_nano_rw_256.sw_in1k,256,31.600,68.400,63.667,36.333,15.45,0.950,bicubic,-64.330,-35.333,+68 +resnest101e.in1k,256,31.587,68.413,64.067,35.933,48.28,0.875,bilinear,-64.263,-35.143,+86 +vit_base_patch16_224.orig_in21k_ft_in1k,224,31.587,68.413,61.147,38.853,86.57,0.900,bicubic,-63.753,-37.863,+222 +regnetz_d32.ra3_in1k,256,31.507,68.493,65.013,34.987,27.58,0.950,bicubic,-64.693,-34.097,-18 +hgnetv2_b4.ssld_stage1_in22k_in1k,288,31.480,68.520,66.840,33.160,19.80,1.000,bicubic,-65.240,-32.590,-181 +mobilenetv4_hybrid_medium.ix_e550_r384_in1k,384,31.440,68.560,65.733,34.267,11.07,0.950,bicubic,-64.610,-33.487,+25 +convnext_small.fb_in1k,224,31.373,68.627,65.987,34.013,50.22,0.875,bicubic,-64.827,-33.143,-17 +maxxvit_rmlp_nano_rw_256.sw_in1k,256,31.333,68.667,64.507,35.493,16.78,0.950,bicubic,-64.697,-34.753,+29 +fastvit_sa24.apple_dist_in1k,256,31.320,68.680,64.760,35.240,21.55,0.900,bicubic,-64.840,-34.390,-9 +convnext_nano.in12k_ft_in1k,288,31.293,68.707,67.333,32.667,15.59,1.000,bicubic,-64.697,-31.857,+39 +regnety_320.tv2_in1k,224,31.267,68.733,64.493,35.507,145.05,0.965,bicubic,-64.823,-34.817,+10 +mobilenetv4_conv_aa_large.e600_r384_in1k,384,31.253,68.747,65.267,34.733,32.59,0.950,bicubic,-64.897,-34.113,-7 +nextvit_base.bd_in1k,224,31.253,68.747,65.173,34.827,44.82,0.950,bicubic,-64.997,-34.047,-48 +cait_s24_224.fb_dist_in1k,224,31.187,68.813,64.253,35.747,46.92,1.000,bicubic,-65.213,-34.727,-99 +tf_efficientnet_b5.ra_in1k,456,31.107,68.893,64.520,35.480,30.39,0.934,bicubic,-65.283,-34.890,-97 +regnetz_c16_evos.ch_in1k,320,31.080,68.920,65.933,34.067,13.49,0.950,bicubic,-65.070,-33.157,-12 +regnetv_040.ra3_in1k,288,31.027,68.973,64.107,35.893,20.64,1.000,bicubic,-65.203,-35.193,-44 +crossvit_base_240.in1k,240,31.027,68.973,60.933,39.067,105.03,0.875,bicubic,-64.473,-37.887,+158 +maxvit_rmlp_nano_rw_256.sw_in1k,256,31.000,69.000,63.093,36.907,15.50,0.950,bicubic,-65.000,-35.867,+29 +seresnet152d.ra2_in1k,256,30.907,69.093,63.173,36.827,66.84,0.950,bicubic,-65.523,-35.957,-116 +regnetz_040.ra3_in1k,256,30.907,69.093,62.627,37.373,27.12,1.000,bicubic,-65.573,-36.783,-131 +maxvit_tiny_tf_224.in1k,224,30.880,69.120,62.933,37.067,30.92,0.950,bicubic,-65.260,-36.227,-14 +swinv2_cr_small_224.sw_in1k,224,30.773,69.227,61.853,38.147,49.70,0.900,bicubic,-65.317,-37.187,+3 +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,224,30.720,69.280,62.333,37.667,88.22,0.900,bicubic,-65.400,-36.867,-12 +xcit_small_24_p16_224.fb_dist_in1k,224,30.600,69.400,64.533,35.467,47.67,1.000,bicubic,-65.620,-34.747,-44 +efficientnet_b4.ra2_in1k,384,30.573,69.427,64.347,35.653,19.34,1.000,bicubic,-65.587,-34.863,-25 +hiera_tiny_224.mae_in1k_ft_in1k,224,30.453,69.547,62.267,37.733,27.91,0.900,bicubic,-65.147,-36.603,+116 +crossvit_18_240.in1k,240,30.427,69.573,61.547,38.453,43.27,0.875,bicubic,-65.063,-37.443,+155 +sequencer2d_m.in1k,224,30.360,69.640,62.933,37.067,38.31,0.875,bicubic,-65.460,-36.177,+64 +repvit_m2_3.dist_450e_in1k,224,30.333,69.667,63.653,36.347,23.69,0.950,bicubic,-66.017,-35.567,-98 +maxxvitv2_nano_rw_256.sw_in1k,256,30.267,69.733,63.773,36.227,23.70,0.950,bicubic,-65.623,-35.337,+49 +regnety_040.ra3_in1k,288,30.200,69.800,63.373,36.627,20.65,1.000,bicubic,-65.800,-35.817,+14 +crossvit_18_dagger_240.in1k,240,30.200,69.800,61.613,38.387,44.27,0.875,bicubic,-65.420,-37.437,+107 +hgnet_small.ssld_in1k,224,30.133,69.867,64.467,35.533,24.36,0.965,bicubic,-66.497,-34.753,-182 +rexnet_300.nav_in1k,224,30.040,69.960,64.200,35.800,34.71,0.875,bicubic,-65.790,-34.930,+57 +xcit_medium_24_p16_224.fb_in1k,224,30.027,69.973,59.187,40.813,84.40,1.000,bicubic,-65.493,-39.773,+132 +dm_nfnet_f1.dm_in1k,224,30.000,70.000,57.947,42.053,132.63,0.910,bicubic,-66.350,-41.223,-101 +mvitv2_tiny.fb_in1k,224,29.867,70.133,63.747,36.253,24.17,0.900,bicubic,-66.013,-35.503,+45 +twins_pcpvt_base.in1k,224,29.840,70.160,64.587,35.413,43.83,0.900,bicubic,-65.950,-34.543,+63 +convnext_tiny.fb_in1k,288,29.787,70.213,64.920,35.080,28.59,1.000,bicubic,-65.993,-34.230,+63 +resnet50.fb_swsl_ig1b_ft_in1k,224,29.760,70.240,63.840,36.160,25.56,0.875,bilinear,-65.710,-35.170,+147 +convnextv2_nano.fcmae_ft_in22k_in1k,288,29.733,70.267,65.653,34.347,15.62,1.000,bicubic,-66.317,-33.557,-10 +cait_xxs24_384.fb_dist_in1k,384,29.720,70.280,63.733,36.267,12.03,1.000,bicubic,-65.540,-35.227,+200 +tf_efficientnet_b5.aa_in1k,456,29.720,70.280,62.733,37.267,30.39,0.934,bicubic,-66.760,-36.497,-154 +hgnetv2_b3.ssld_stage1_in22k_in1k,288,29.640,70.360,65.920,34.080,16.29,1.000,bicubic,-66.630,-33.230,-88 +resnet200d.ra2_in1k,256,29.640,70.360,61.387,38.613,64.69,0.950,bicubic,-66.670,-37.863,-92 +cs3sedarknet_x.c2ns_in1k,288,29.627,70.373,61.587,38.413,35.40,1.000,bicubic,-66.413,-37.523,-11 +mobilevitv2_150.cvnets_in22k_ft_in1k_384,384,29.573,70.427,61.400,38.600,10.59,1.000,bicubic,-66.127,-37.590,+73 +swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,224,29.547,70.453,66.493,33.507,28.29,0.900,bicubic,-65.943,-32.597,+127 +resnet152.a1_in1k,288,29.493,70.507,57.027,42.973,60.19,1.000,bicubic,-65.997,-41.923,+135 +deit_base_distilled_patch16_224.fb_in1k,224,29.480,70.520,64.093,35.907,87.34,0.900,bicubic,-66.620,-35.097,-33 +convnextv2_tiny.fcmae_ft_in1k,224,29.467,70.533,63.893,36.107,28.64,0.875,bicubic,-66.213,-35.117,+74 +convit_base.fb_in1k,224,29.387,70.613,61.493,38.507,86.54,0.875,bicubic,-66.163,-37.397,+100 +vit_relpos_base_patch16_clsgap_224.sw_in1k,224,29.360,70.640,62.187,37.813,86.43,0.900,bicubic,-66.410,-36.963,+51 +convnext_tiny.fb_in22k_ft_in1k,288,29.253,70.747,56.560,43.440,28.59,1.000,bicubic,-65.617,-42.340,+278 +mobilenetv4_conv_large.e600_r384_in1k,384,29.147,70.853,63.680,36.320,32.59,0.950,bicubic,-66.653,-35.380,+44 +cs3se_edgenet_x.c2ns_in1k,256,29.080,70.920,60.933,39.067,50.72,0.950,bicubic,-66.870,-37.957,+9 +tf_efficientnetv2_s.in1k,384,29.027,70.973,61.173,38.827,21.46,1.000,bicubic,-67.323,-37.937,-119 +fastvit_sa24.apple_in1k,256,29.000,71.000,62.320,37.680,21.55,0.900,bicubic,-66.910,-36.840,+13 +regnety_160.deit_in1k,224,29.000,71.000,61.467,38.533,83.59,0.950,bicubic,-67.050,-37.663,-27 +resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,224,28.960,71.040,60.787,39.213,88.79,0.875,bilinear,-66.530,-38.433,+117 +davit_tiny.msft_in1k,224,28.893,71.107,63.627,36.373,28.36,0.950,bicubic,-66.767,-35.603,+70 +hgnetv2_b4.ssld_stage2_ft_in1k,224,28.840,71.160,63.507,36.493,19.80,0.965,bicubic,-67.670,-35.873,-177 +resnet101d.ra2_in1k,320,28.813,71.187,61.920,38.080,44.57,1.000,bicubic,-67.487,-37.500,-109 +edgenext_base.usi_in1k,320,28.787,71.213,64.720,35.280,18.51,1.000,bicubic,-67.933,-34.800,-239 +vit_little_patch16_reg4_gap_256.sbb_in1k,256,28.787,71.213,62.413,37.587,22.52,0.950,bicubic,-66.953,-36.707,+45 +vit_relpos_medium_patch16_cls_224.sw_in1k,224,28.733,71.267,59.973,40.027,38.76,0.900,bicubic,-66.757,-38.797,+114 +regnety_160.tv2_in1k,224,28.707,71.293,61.280,38.720,83.59,0.965,bicubic,-67.273,-37.920,-10 +xcit_tiny_24_p8_224.fb_in1k,224,28.653,71.347,60.160,39.840,12.11,1.000,bicubic,-67.027,-38.990,+58 +resnetrs152.tf_in1k,320,28.653,71.347,60.013,39.987,86.62,1.000,bicubic,-67.917,-39.407,-197 +resnetv2_101.a1h_in1k,288,28.547,71.453,59.613,40.387,44.54,1.000,bicubic,-67.603,-39.547,-66 +xcit_tiny_24_p8_224.fb_dist_in1k,224,28.520,71.480,61.400,38.600,12.11,1.000,bicubic,-67.280,-37.810,+27 +vit_relpos_medium_patch16_224.sw_in1k,224,28.480,71.520,61.520,38.480,38.75,0.900,bicubic,-66.990,-37.440,+117 +crossvit_15_dagger_240.in1k,240,28.453,71.547,60.093,39.907,28.21,0.875,bicubic,-67.217,-38.967,+56 +hgnetv2_b3.ssld_stage2_ft_in1k,288,28.400,71.600,65.013,34.987,16.29,1.000,bicubic,-67.950,-34.317,-146 +efficientvit_b2.r288_in1k,288,28.400,71.600,63.907,36.093,24.33,1.000,bicubic,-67.580,-35.373,-18 +cs3edgenet_x.c2_in1k,288,28.360,71.640,61.040,38.960,47.82,1.000,bicubic,-67.700,-38.090,-47 +xcit_small_24_p16_224.fb_in1k,224,28.320,71.680,59.000,41.000,47.67,1.000,bicubic,-67.220,-40.000,+82 +xception65p.ra3_in1k,299,28.307,71.693,59.480,40.520,39.82,0.940,bicubic,-67.903,-39.940,-94 +pvt_v2_b2_li.in1k,224,28.267,71.733,61.560,38.440,22.55,0.900,bicubic,-67.323,-37.470,+66 +regnety_080.ra3_in1k,224,28.240,71.760,59.693,40.307,39.18,0.950,bicubic,-67.790,-39.417,-42 +repvit_m2_3.dist_300e_in1k,224,28.227,71.773,61.453,38.547,23.69,0.950,bicubic,-67.953,-37.597,-88 +regnety_064.ra3_in1k,224,28.227,71.773,59.507,40.493,30.58,0.950,bicubic,-67.643,-39.573,+3 +efficientnet_b4.ra2_in1k,320,28.187,71.813,61.907,38.093,19.34,0.875,bicubic,-67.573,-37.253,+22 +resnet101.a1h_in1k,288,28.120,71.880,59.640,40.360,44.55,1.000,bicubic,-67.920,-39.500,-50 +cs3sedarknet_x.c2ns_in1k,256,28.027,71.973,60.333,39.667,35.40,0.887,bicubic,-67.603,-38.647,+52 +efficientnetv2_rw_s.ra2_in1k,288,27.973,72.027,60.373,39.627,23.94,1.000,bicubic,-68.187,-38.817,-84 +rdnet_tiny.nv_in1k,224,27.760,72.240,62.000,38.000,23.86,0.900,bicubic,-67.880,-36.950,+48 +efficientformerv2_l.snap_dist_in1k,224,27.707,72.293,61.347,38.653,26.32,0.950,bicubic,-68.353,-37.853,-60 +mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k,256,27.693,72.307,62.773,37.227,11.07,0.950,bicubic,-68.127,-36.327,+4 +coat_lite_small.in1k,224,27.667,72.333,58.547,41.453,19.84,0.900,bicubic,-67.893,-40.333,+59 +efficientformer_l7.snap_dist_in1k,224,27.653,72.347,62.640,37.360,82.23,0.950,bicubic,-68.457,-36.530,-78 +flexivit_small.1200ep_in1k,240,27.587,72.413,58.240,41.760,22.06,0.950,bicubic,-67.973,-40.810,+54 +resnetaa50d.sw_in12k_ft_in1k,288,27.520,72.480,62.067,37.933,25.58,1.000,bicubic,-68.290,-37.203,+3 +regnetv_064.ra3_in1k,224,27.347,72.653,61.093,38.907,30.58,0.950,bicubic,-68.743,-38.137,-76 +pvt_v2_b2.in1k,224,27.307,72.693,60.587,39.413,25.36,0.900,bicubic,-68.193,-38.533,+76 +vit_base_patch16_384.augreg_in1k,384,27.293,72.707,56.507,43.493,86.86,1.000,bicubic,-67.667,-42.313,+213 +resnext101_32x8d.tv2_in1k,224,27.240,72.760,59.347,40.653,88.79,0.965,bilinear,-68.750,-39.883,-45 +deit_base_patch16_224.fb_in1k,224,27.173,72.827,58.640,41.360,86.57,0.900,bicubic,-68.267,-40.200,+98 +convnextv2_nano.fcmae_ft_in22k_in1k,224,27.147,72.853,62.293,37.707,15.62,0.875,bicubic,-68.383,-36.827,+60 +regnetz_c16.ra3_in1k,320,27.147,72.853,62.267,37.733,13.46,1.000,bicubic,-68.833,-36.883,-40 +regnety_080_tv.tv2_in1k,224,27.133,72.867,61.360,38.640,39.38,0.965,bicubic,-68.767,-37.790,-27 +resnetv2_50x1_bit.goog_in21k_ft_in1k,448,27.107,72.893,62.627,37.373,25.55,1.000,bilinear,-67.923,-36.263,+190 +coatnet_bn_0_rw_224.sw_in1k,224,27.013,72.987,60.840,39.160,27.44,0.950,bicubic,-68.687,-38.390,+18 +xcit_small_12_p16_224.fb_dist_in1k,224,27.000,73.000,59.813,40.187,26.25,1.000,bicubic,-69.020,-39.107,-62 +coatnet_0_rw_224.sw_in1k,224,26.973,73.027,59.107,40.893,27.44,0.950,bicubic,-68.487,-39.523,+87 +flexivit_small.600ep_in1k,240,26.827,73.173,57.013,42.987,22.06,0.950,bicubic,-68.863,-42.067,+18 +vit_small_patch16_224.augreg_in21k_ft_in1k,224,26.813,73.187,58.840,41.160,22.05,0.900,bicubic,-68.597,-40.290,+99 +vit_relpos_base_patch16_224.sw_in1k,224,26.787,73.213,60.467,39.533,86.43,0.900,bicubic,-68.803,-38.523,+36 +dm_nfnet_f0.dm_in1k,256,26.773,73.227,58.067,41.933,71.49,0.900,bicubic,-69.577,-41.183,-177 +gcvit_xtiny.in1k,224,26.720,73.280,60.587,39.413,19.98,0.875,bicubic,-68.870,-38.443,+33 +swin_s3_tiny_224.ms_in1k,224,26.680,73.320,60.293,39.707,28.33,0.900,bicubic,-68.510,-38.657,+142 +nextvit_small.bd_in1k,224,26.640,73.360,61.227,38.773,31.76,0.950,bicubic,-69.230,-37.963,-25 +mobilenetv4_conv_large.e500_r256_in1k,320,26.587,73.413,61.080,38.920,32.59,1.000,bicubic,-69.303,-37.960,-37 +sequencer2d_s.in1k,224,26.373,73.627,60.267,39.733,27.65,0.875,bicubic,-69.617,-38.823,-61 +tresnet_v2_l.miil_in21k_ft_in1k,224,26.293,73.707,59.493,40.507,46.17,0.875,bilinear,-69.867,-39.697,-117 +cs3edgenet_x.c2_in1k,256,26.267,73.733,59.067,40.933,47.82,0.887,bicubic,-69.243,-39.963,+52 +mobilevitv2_200.cvnets_in22k_ft_in1k,256,26.227,73.773,58.707,41.293,18.45,0.888,bicubic,-68.943,-40.153,+142 +hgnet_small.paddle_in1k,288,26.187,73.813,60.040,39.960,24.36,1.000,bicubic,-69.463,-39.070,+14 +swinv2_tiny_window8_256.ms_in1k,256,26.173,73.827,60.480,39.520,28.35,0.900,bicubic,-69.327,-38.510,+49 +regnetx_320.tv2_in1k,224,26.173,73.827,57.747,42.253,107.81,0.965,bicubic,-69.817,-41.303,-68 +nfnet_l0.ra2_in1k,288,26.080,73.920,61.547,38.453,35.07,1.000,bicubic,-70.050,-37.693,-113 +coatnet_rmlp_nano_rw_224.sw_in1k,224,26.080,73.920,60.400,39.600,15.15,0.900,bicubic,-69.360,-38.440,+74 +resnext101_64x4d.c1_in1k,224,26.067,73.933,56.227,43.773,83.46,0.950,bicubic,-69.643,-42.803,-5 +regnety_032.ra_in1k,288,26.040,73.960,60.867,39.133,19.44,1.000,bicubic,-69.950,-38.233,-74 +resnet152d.ra2_in1k,256,26.000,74.000,58.040,41.960,60.21,0.950,bicubic,-70.070,-41.260,-98 +deit3_small_patch16_224.fb_in1k,224,26.000,74.000,54.293,45.707,22.06,0.900,bicubic,-69.000,-44.607,+176 +tf_efficientnet_b4.aa_in1k,380,25.960,74.040,59.960,40.040,19.34,0.922,bicubic,-69.990,-39.160,-64 +fbnetv3_g.ra2_in1k,288,25.920,74.080,60.907,39.093,16.62,0.950,bilinear,-69.580,-38.073,+42 +coatnext_nano_rw_224.sw_in1k,224,25.907,74.093,59.147,40.853,14.70,0.900,bicubic,-69.503,-39.853,+79 +tf_efficientnet_b4.ap_in1k,380,25.827,74.173,59.573,40.427,19.34,0.922,bicubic,-70.343,-39.707,-135 +flexivit_small.300ep_in1k,240,25.773,74.227,56.813,43.187,22.06,0.950,bicubic,-69.747,-41.947,+35 +ecaresnet101d.miil_in1k,224,25.760,74.240,58.680,41.320,44.57,0.875,bicubic,-69.780,-40.500,+23 +coat_mini.in1k,224,25.720,74.280,57.680,42.320,10.34,0.900,bicubic,-69.280,-41.100,+168 +inception_next_tiny.sail_in1k,224,25.693,74.307,59.480,40.520,28.06,0.875,bicubic,-69.777,-39.400,+54 +ecaresnet50t.ra2_in1k,320,25.680,74.320,59.707,40.293,25.57,0.950,bicubic,-69.840,-39.303,+26 +mobilevitv2_175.cvnets_in22k_ft_in1k,256,25.680,74.320,58.027,41.973,14.25,0.888,bicubic,-69.570,-40.903,+109 +mobilenetv4_hybrid_medium.ix_e550_r256_in1k,320,25.667,74.333,60.080,39.920,11.07,1.000,bicubic,-70.103,-38.960,-34 +visformer_small.in1k,224,25.640,74.360,58.573,41.427,40.22,0.900,bicubic,-69.860,-40.417,+35 +rexnetr_200.sw_in12k_ft_in1k,224,25.507,74.493,60.427,39.573,16.52,0.950,bicubic,-70.303,-38.743,-42 +crossvit_15_240.in1k,240,25.440,74.560,57.320,42.680,27.53,0.875,bicubic,-69.690,-41.620,+129 +vit_small_patch16_384.augreg_in1k,384,25.413,74.587,57.147,42.853,22.20,1.000,bicubic,-69.877,-41.863,+95 +convformer_s18.sail_in1k,224,25.387,74.613,57.600,42.400,26.77,1.000,bicubic,-70.563,-41.570,-76 +vit_relpos_medium_patch16_rpn_224.sw_in1k,224,25.267,74.733,58.267,41.733,38.73,0.900,bicubic,-70.253,-40.853,+20 +halo2botnet50ts_256.a1h_in1k,256,25.173,74.827,56.280,43.720,22.64,0.950,bicubic,-70.247,-42.650,+60 +resnetv2_50x1_bit.goog_distilled_in1k,224,25.133,74.867,59.387,40.613,25.55,0.875,bicubic,-70.967,-39.893,-129 +xcit_small_12_p16_224.fb_in1k,224,25.040,74.960,55.707,44.293,26.25,1.000,bicubic,-70.390,-43.273,+54 +resnetrs270.tf_in1k,256,25.040,74.960,54.307,45.693,129.86,1.000,bicubic,-71.360,-44.933,-234 +edgenext_base.usi_in1k,256,25.027,74.973,60.667,39.333,18.51,0.950,bicubic,-71.373,-38.713,-237 +vit_srelpos_medium_patch16_224.sw_in1k,224,24.987,75.013,58.147,41.853,38.74,0.900,bicubic,-70.253,-40.913,+97 +hgnet_tiny.ssld_in1k,288,24.947,75.053,61.440,38.560,14.74,1.000,bicubic,-71.283,-37.670,-178 +resnet101.a1_in1k,288,24.947,75.053,54.973,45.027,44.55,1.000,bicubic,-70.543,-43.817,+30 +convit_small.fb_in1k,224,24.813,75.187,56.880,43.120,27.78,0.875,bicubic,-70.377,-42.080,+103 +resnet152.a2_in1k,288,24.800,75.200,53.893,46.107,60.19,1.000,bicubic,-70.690,-45.217,+30 +regnetz_c16_evos.ch_in1k,256,24.733,75.267,59.653,40.347,13.49,0.950,bicubic,-70.967,-39.257,-36 +vit_base_patch16_rpn_224.sw_in1k,224,24.733,75.267,57.987,42.013,86.54,0.900,bicubic,-70.657,-40.963,+57 +gc_efficientnetv2_rw_t.agc_in1k,288,24.587,75.413,57.533,42.467,13.68,1.000,bicubic,-71.153,-41.647,-45 +tnt_s_patch16_224,224,24.573,75.427,57.773,42.227,23.76,0.900,bicubic,-70.487,-41.067,+131 +efficientvit_b2.r256_in1k,256,24.533,75.467,59.120,40.880,24.33,1.000,bicubic,-71.137,-39.690,-30 +hgnetv2_b4.ssld_stage1_in22k_in1k,224,24.507,75.493,60.387,39.613,19.80,0.965,bicubic,-71.963,-38.913,-267 +eca_nfnet_l0.ra2_in1k,288,24.480,75.520,59.587,40.413,24.14,1.000,bicubic,-71.490,-39.623,-100 +regnetv_040.ra3_in1k,224,24.387,75.613,57.600,42.400,20.64,0.950,bicubic,-71.363,-41.520,-55 +swinv2_cr_tiny_ns_224.sw_in1k,224,24.280,75.720,58.347,41.653,28.33,0.900,bicubic,-71.050,-40.593,+65 +tf_efficientnet_b2.ns_jft_in1k,260,24.253,75.747,57.573,42.427,9.11,0.890,bicubic,-71.497,-41.647,-56 +efficientnetv2_rw_t.ra2_in1k,288,24.240,75.760,57.147,42.853,13.65,1.000,bicubic,-71.370,-41.923,-24 +xception41p.ra3_in1k,299,24.240,75.760,54.560,45.440,26.91,0.940,bicubic,-71.320,-44.370,-16 +tf_efficientnetv2_b3.in21k_ft_in1k,240,24.213,75.787,55.840,44.160,14.36,0.900,bicubic,-71.127,-43.060,+58 +xcit_tiny_12_p16_384.fb_dist_in1k,384,24.120,75.880,56.920,43.080,6.72,1.000,bicubic,-71.040,-41.930,+94 +convnext_tiny.fb_in1k,224,24.107,75.893,59.107,40.893,28.59,0.875,bicubic,-71.433,-39.943,-10 +convnext_nano.in12k_ft_in1k,224,24.067,75.933,60.373,39.627,15.59,0.950,bicubic,-71.643,-38.887,-52 +cs3darknet_x.c2ns_in1k,288,24.000,76.000,57.573,42.427,35.05,1.000,bicubic,-71.870,-41.517,-86 +eva02_tiny_patch14_336.mim_in22k_ft_in1k,336,23.987,76.013,55.507,44.493,5.76,1.000,bicubic,-70.973,-43.403,+139 +convnext_nano_ols.d1h_in1k,288,23.973,76.027,56.493,43.507,15.65,1.000,bicubic,-71.137,-42.407,+106 +vit_small_r26_s32_224.augreg_in21k_ft_in1k,224,23.933,76.067,55.947,44.053,36.43,0.900,bicubic,-71.707,-43.243,-39 +twins_svt_small.in1k,224,23.920,76.080,57.400,42.600,24.06,0.900,bicubic,-71.300,-41.680,+77 +vit_relpos_small_patch16_224.sw_in1k,224,23.907,76.093,57.733,42.267,21.98,0.900,bicubic,-71.283,-41.307,+79 +resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,224,23.800,76.200,57.227,42.773,44.18,0.875,bilinear,-71.680,-41.713,+9 +ecaresnet50d.miil_in1k,288,23.773,76.227,58.547,41.453,25.58,0.950,bicubic,-71.717,-40.593,+1 +cs3sedarknet_l.c2ns_in1k,288,23.747,76.253,58.280,41.720,21.91,0.950,bicubic,-71.573,-40.860,+51 +coatnet_nano_rw_224.sw_in1k,224,23.720,76.280,56.693,43.307,15.14,0.900,bicubic,-71.520,-42.217,+69 +poolformer_m48.sail_in1k,224,23.680,76.320,56.853,43.147,73.47,0.950,bicubic,-71.960,-42.177,-44 +convnext_nano.d1h_in1k,288,23.653,76.347,55.707,44.293,15.59,1.000,bicubic,-71.697,-43.193,+41 +vit_small_patch32_384.augreg_in21k_ft_in1k,384,23.560,76.440,56.907,43.093,22.92,1.000,bicubic,-71.510,-42.083,+103 +resnet152.a1h_in1k,224,23.533,76.467,53.093,46.907,60.19,0.950,bicubic,-72.357,-45.987,-105 +tiny_vit_11m_224.in1k,224,23.520,76.480,58.627,41.373,11.00,0.950,bicubic,-72.030,-40.463,-33 +mobilevitv2_150.cvnets_in22k_ft_in1k,256,23.507,76.493,55.187,44.813,10.59,0.888,bicubic,-71.643,-43.903,+80 +tf_efficientnet_b5.in1k,456,23.440,76.560,57.480,42.520,30.39,0.934,bicubic,-72.640,-41.820,-160 +crossvit_small_240.in1k,240,23.413,76.587,56.387,43.613,26.86,0.875,bicubic,-71.437,-42.633,+152 +nest_tiny_jx.goog_in1k,224,23.253,76.747,56.133,43.867,17.06,0.875,bicubic,-72.007,-42.837,+53 +seresnext50_32x4d.racm_in1k,288,23.240,76.760,57.347,42.653,27.56,0.950,bicubic,-72.480,-41.693,-77 +hrnet_w18_ssld.paddle_in1k,288,23.240,76.760,55.067,44.933,21.30,1.000,bilinear,-72.740,-44.093,-135 +focalnet_tiny_srf.ms_in1k,224,23.227,76.773,58.160,41.840,28.43,0.900,bicubic,-72.263,-40.640,-15 +efficientnet_b3.ra2_in1k,320,23.200,76.800,55.933,44.067,12.23,1.000,bicubic,-72.520,-43.257,-78 +regnety_040.ra3_in1k,224,23.187,76.813,55.333,44.667,20.65,0.950,bicubic,-72.373,-43.787,-46 +levit_384.fb_dist_in1k,224,23.173,76.827,55.747,44.253,39.13,0.900,bicubic,-72.367,-43.393,-38 +levit_conv_384.fb_dist_in1k,224,23.173,76.827,55.747,44.253,39.13,0.900,bicubic,-72.367,-43.303,-38 +convnext_tiny_hnf.a2h_in1k,224,23.160,76.840,54.933,45.067,28.59,0.950,bicubic,-72.360,-44.037,-32 +lamhalobotnet50ts_256.a1h_in1k,256,23.133,76.867,54.880,45.120,22.57,0.950,bicubic,-72.027,-44.120,+66 +regnetz_c16.ra3_in1k,256,23.107,76.893,57.733,42.267,13.46,0.940,bicubic,-72.413,-41.347,-34 +pnasnet5large.tf_in1k,331,23.107,76.893,53.293,46.707,86.06,0.911,bicubic,-72.613,-45.627,-83 +nasnetalarge.tf_in1k,331,23.080,76.920,54.533,45.467,88.75,0.911,bicubic,-72.620,-44.527,-77 +vit_base_patch32_clip_224.laion2b_ft_in1k,224,23.067,76.933,55.347,44.653,88.22,0.900,bicubic,-72.483,-43.523,-48 +efficientformer_l3.snap_dist_in1k,224,23.040,76.960,56.787,43.213,31.41,0.950,bicubic,-72.570,-42.393,-62 +pit_s_distilled_224.in1k,224,22.973,77.027,56.880,43.120,24.04,0.900,bicubic,-72.207,-42.000,+53 +wide_resnet50_2.racm_in1k,288,22.960,77.040,55.600,44.400,68.88,0.950,bicubic,-72.700,-43.430,-74 +focalnet_tiny_lrf.ms_in1k,224,22.920,77.080,58.133,41.867,28.65,0.900,bicubic,-72.560,-40.967,-19 +hgnetv2_b3.ssld_stage1_in22k_in1k,224,22.827,77.173,58.760,41.240,16.29,0.965,bicubic,-73.153,-40.530,-151 +convnextv2_nano.fcmae_ft_in1k,288,22.813,77.187,58.747,41.253,15.62,1.000,bicubic,-73.087,-40.363,-134 +regnetx_160.tv2_in1k,224,22.787,77.213,56.200,43.800,54.28,0.965,bicubic,-73.113,-42.910,-133 +resnet61q.ra2_in1k,288,22.773,77.227,55.480,44.520,36.85,1.000,bicubic,-73.027,-43.520,-109 +vit_base_patch32_clip_224.openai_ft_in1k,224,22.747,77.253,55.840,44.160,88.22,0.900,bicubic,-72.373,-43.150,+61 +halonet50ts.a1h_in1k,256,22.747,77.253,54.360,45.640,22.73,0.940,bicubic,-72.393,-44.630,+57 +tiny_vit_5m_224.dist_in22k_ft_in1k,224,22.720,77.280,55.893,44.107,5.39,0.950,bicubic,-72.330,-43.087,+76 +resnetv2_50d_evos.ah_in1k,288,22.720,77.280,55.013,44.987,25.59,1.000,bicubic,-72.930,-44.137,-79 +efficientnet_b3.ra2_in1k,288,22.693,77.307,55.133,44.867,12.23,0.875,bicubic,-72.497,-43.797,+38 +tf_efficientnet_b4.in1k,380,22.653,77.347,56.267,43.733,19.34,0.922,bicubic,-73.167,-42.793,-121 +twins_pcpvt_small.in1k,224,22.627,77.373,56.600,43.400,24.11,0.900,bicubic,-72.603,-42.280,+32 +resmlp_big_24_224.fb_in1k,224,22.627,77.373,54.013,45.987,129.14,0.875,bicubic,-72.023,-44.647,+172 +hgnetv2_b2.ssld_stage2_ft_in1k,288,22.560,77.440,59.227,40.773,11.22,1.000,bicubic,-73.650,-39.953,-236 +poolformerv2_m48.sail_in1k,224,22.520,77.480,55.533,44.467,73.35,1.000,bicubic,-73.230,-43.447,-111 +repvit_m1_5.dist_450e_in1k,224,22.373,77.627,55.493,44.507,14.64,0.950,bicubic,-73.557,-43.667,-153 +vit_srelpos_small_patch16_224.sw_in1k,224,22.360,77.640,55.293,44.707,21.97,0.900,bicubic,-72.710,-43.657,+65 +ecaresnet101d_pruned.miil_in1k,288,22.293,77.707,56.427,43.573,24.88,0.950,bicubic,-73.447,-42.593,-113 +poolformer_m36.sail_in1k,224,22.267,77.733,55.147,44.853,56.17,0.950,bicubic,-73.143,-43.873,-14 +hgnetv2_b3.ssld_stage2_ft_in1k,224,22.253,77.747,57.893,42.107,16.29,0.965,bicubic,-73.667,-41.387,-155 +vit_base_patch32_224.augreg_in21k_ft_in1k,224,22.240,77.760,53.707,46.293,88.22,0.900,bicubic,-72.770,-45.313,+72 +xcit_tiny_12_p8_224.fb_dist_in1k,224,22.080,77.920,53.880,46.120,6.71,1.000,bicubic,-73.010,-45.050,+55 +resnetv2_50d_gn.ah_in1k,288,22.040,77.960,54.880,45.120,25.57,1.000,bicubic,-73.460,-44.020,-55 +wide_resnet101_2.tv2_in1k,224,22.013,77.987,54.560,45.440,126.89,0.965,bilinear,-73.537,-44.430,-78 +ecaresnet50t.a1_in1k,288,21.840,78.160,53.560,46.440,25.57,1.000,bicubic,-73.620,-45.160,-38 +resnext50_32x4d.a1h_in1k,288,21.813,78.187,54.200,45.800,25.03,1.000,bicubic,-73.627,-44.790,-33 +tf_efficientnetv2_s.in1k,300,21.760,78.240,53.293,46.707,21.46,1.000,bicubic,-74.390,-45.817,-225 +resnet101d.ra2_in1k,256,21.693,78.307,54.360,45.640,44.57,0.950,bicubic,-74.207,-44.720,-160 +res2net101d.in1k,224,21.613,78.387,51.147,48.853,45.23,0.875,bilinear,-73.227,-47.563,+108 +resnetrs152.tf_in1k,256,21.587,78.413,51.360,48.640,86.62,1.000,bicubic,-74.443,-47.690,-195 +efficientvit_b2.r224_in1k,224,21.573,78.427,55.253,44.747,24.33,0.950,bicubic,-73.737,-43.537,-3 +convnextv2_nano.fcmae_ft_in1k,224,21.547,78.453,55.427,44.573,15.62,0.875,bicubic,-73.883,-43.453,-37 +efficientformerv2_s2.snap_dist_in1k,224,21.547,78.453,54.040,45.960,12.71,0.950,bicubic,-73.833,-44.910,-25 +cs3sedarknet_l.c2ns_in1k,256,21.427,78.573,55.653,44.347,21.91,0.887,bicubic,-73.443,-43.157,+90 +vit_wee_patch16_reg1_gap_256.sbb_in1k,256,21.413,78.587,54.507,45.493,13.42,0.950,bicubic,-73.377,-44.063,+112 +tresnet_m.miil_in21k_ft_in1k,224,21.387,78.613,53.520,46.480,31.39,0.875,bilinear,-74.343,-45.510,-128 +fastvit_sa12.apple_dist_in1k,256,21.307,78.693,54.600,45.400,11.58,0.900,bicubic,-73.823,-44.210,+29 +ecaresnet50t.ra2_in1k,256,21.253,78.747,52.960,47.040,25.57,0.875,bicubic,-73.577,-45.770,+103 +resnet50_gn.a1h_in1k,288,21.160,78.840,54.200,45.800,25.56,0.950,bicubic,-74.120,-44.710,-6 +maxvit_rmlp_pico_rw_256.sw_in1k,256,21.160,78.840,51.787,48.213,7.52,0.950,bicubic,-73.530,-47.013,+129 +swin_tiny_patch4_window7_224.ms_in1k,224,21.120,78.880,55.640,44.360,28.29,0.900,bicubic,-74.050,-43.280,+15 +repvit_m1_5.dist_300e_in1k,224,21.107,78.893,54.307,45.693,14.64,0.950,bicubic,-74.593,-44.843,-124 +pit_s_224.in1k,224,21.080,78.920,53.547,46.453,23.46,0.900,bicubic,-73.580,-45.173,+136 +convmixer_1536_20.in1k,224,20.973,79.027,55.307,44.693,51.63,0.960,bicubic,-74.107,-43.723,+34 +cs3darknet_x.c2ns_in1k,256,20.893,79.107,53.467,46.533,35.05,0.950,bicubic,-74.517,-45.373,-42 +resnet101.a2_in1k,288,20.880,79.120,51.853,48.147,44.55,1.000,bicubic,-74.540,-47.177,-45 +xcit_tiny_12_p8_224.fb_in1k,224,20.827,79.173,51.987,48.013,6.71,1.000,bicubic,-73.853,-46.853,+122 +nfnet_l0.ra2_in1k,224,20.760,79.240,55.507,44.493,35.07,0.900,bicubic,-74.670,-43.333,-54 +deit_small_distilled_patch16_224.fb_in1k,224,20.733,79.267,54.853,45.147,22.44,0.900,bicubic,-73.977,-43.967,+114 +poolformerv2_m36.sail_in1k,224,20.667,79.333,52.987,47.013,56.08,1.000,bicubic,-74.763,-46.103,-54 +resnet61q.ra2_in1k,256,20.653,79.347,52.947,47.053,36.85,0.900,bicubic,-74.687,-46.023,-30 +resnet51q.ra2_in1k,288,20.600,79.400,55.173,44.827,35.70,1.000,bilinear,-75.280,-43.847,-175 +vit_pwee_patch16_reg1_gap_256.sbb_in1k,256,20.560,79.440,53.173,46.827,15.25,0.950,bicubic,-74.080,-45.607,+133 +regnety_032.tv2_in1k,224,20.547,79.453,54.347,45.653,19.44,0.965,bicubic,-74.753,-44.583,-25 +resnet152.a1_in1k,224,20.493,79.507,48.853,51.147,60.19,0.950,bicubic,-74.647,-49.927,+9 +resnet152.tv2_in1k,224,20.453,79.547,52.427,47.573,60.19,0.965,bilinear,-75.077,-46.533,-99 +ecaresnetlight.miil_in1k,288,20.440,79.560,53.360,46.640,30.16,0.950,bicubic,-74.850,-45.680,-26 +sebotnet33ts_256.a1h_in1k,256,20.333,79.667,48.693,51.307,13.70,0.940,bicubic,-74.317,-49.817,+127 +resnetrs101.tf_in1k,288,20.307,79.693,52.573,47.427,63.62,0.940,bicubic,-75.113,-46.447,-60 +regnety_032.ra_in1k,224,20.173,79.827,52.933,47.067,19.44,0.950,bicubic,-75.227,-46.147,-54 +resnest50d_4s2x40d.in1k,224,20.093,79.907,52.800,47.200,30.42,0.875,bicubic,-74.867,-46.020,+42 +hgnet_small.paddle_in1k,224,20.040,79.960,52.627,47.373,24.36,0.965,bicubic,-75.070,-46.303,+7 +resnetaa50d.sw_in12k_ft_in1k,224,20.027,79.973,54.720,45.280,25.58,0.950,bicubic,-75.333,-44.350,-49 +resnetaa50.a1h_in1k,288,20.013,79.987,51.733,48.267,25.56,1.000,bicubic,-75.227,-47.137,-21 +hgnet_tiny.ssld_in1k,224,19.987,80.013,54.773,45.227,14.74,0.965,bicubic,-75.723,-44.477,-153 +fbnetv3_g.ra2_in1k,240,19.947,80.053,53.720,46.280,16.62,0.950,bilinear,-75.083,-45.340,+26 +mobilenetv4_conv_large.e500_r256_in1k,256,19.920,80.080,53.493,46.507,32.59,0.950,bicubic,-75.170,-45.457,+9 +xcit_nano_12_p8_384.fb_dist_in1k,384,19.760,80.240,50.560,49.440,3.05,1.000,bicubic,-73.710,-47.950,+342 +resnext50_32x4d.a1_in1k,288,19.747,80.253,50.227,49.773,25.03,1.000,bicubic,-75.093,-48.553,+70 +hgnetv2_b2.ssld_stage1_in22k_in1k,288,19.693,80.307,55.360,44.640,11.22,1.000,bicubic,-76.117,-43.820,-180 +resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,224,19.693,80.307,53.253,46.747,25.03,0.875,bilinear,-75.157,-45.627,+66 +mobilenetv4_hybrid_medium.ix_e550_r256_in1k,256,19.600,80.400,53.227,46.773,11.07,0.950,bicubic,-75.770,-45.633,-62 +haloregnetz_b.ra3_in1k,224,19.600,80.400,49.720,50.280,11.68,0.940,bicubic,-75.140,-48.960,+88 +regnetz_b16.ra3_in1k,288,19.573,80.427,52.400,47.600,9.72,1.000,bicubic,-75.597,-46.680,-20 +resnet51q.ra2_in1k,256,19.560,80.440,53.533,46.467,35.70,0.875,bilinear,-75.930,-45.317,-100 +mobilenetv4_conv_medium.e500_r256_in1k,320,19.480,80.520,53.053,46.947,9.72,1.000,bicubic,-75.520,-45.407,+20 +resnext101_32x8d.tv2_in1k,176,19.440,80.560,51.893,48.107,88.79,0.875,bilinear,-75.920,-47.157,-64 +tresnet_xl.miil_in1k,224,19.347,80.653,52.827,47.173,78.44,0.875,bilinear,-76.093,-46.233,-88 +resnetv2_101.a1h_in1k,224,19.320,80.680,48.853,51.147,44.54,0.950,bicubic,-76.360,-50.207,-156 +resnet101.a1h_in1k,224,19.280,80.720,49.787,50.213,44.55,0.950,bicubic,-76.090,-49.023,-70 +senet154.gluon_in1k,224,19.133,80.867,47.320,52.680,115.09,0.875,bicubic,-75.797,-51.450,+32 +rexnet_200.nav_in1k,224,19.040,80.960,52.573,47.427,16.37,0.875,bicubic,-75.930,-46.177,+19 +lambda_resnet50ts.a1h_in1k,256,19.027,80.973,48.907,51.093,21.54,0.950,bicubic,-75.813,-49.563,+57 +resnet50d.a1_in1k,288,19.027,80.973,48.707,51.293,25.58,1.000,bicubic,-75.723,-49.763,+74 +seresnext101_64x4d.gluon_in1k,224,19.013,80.987,48.920,51.080,88.23,0.875,bicubic,-75.927,-49.880,+27 +levit_256.fb_dist_in1k,224,19.000,81.000,49.707,50.293,18.89,0.900,bicubic,-76.030,-49.163,+3 +levit_conv_256.fb_dist_in1k,224,19.000,81.000,49.707,50.293,18.89,0.900,bicubic,-76.030,-49.163,+5 +legacy_senet154.in1k,224,18.947,81.053,47.640,52.360,115.09,0.875,bilinear,-76.153,-51.190,-13 +tf_efficientnet_b1.ns_jft_in1k,240,18.920,81.080,51.707,48.293,7.79,0.882,bicubic,-76.230,-47.123,-29 +repvgg_b3.rvgg_in1k,224,18.867,81.133,49.840,50.160,123.09,0.875,bilinear,-75.683,-49.070,+121 +gcvit_xxtiny.in1k,224,18.800,81.200,53.107,46.893,12.00,0.875,bicubic,-75.580,-45.643,+153 +eca_nfnet_l0.ra2_in1k,224,18.760,81.240,53.320,46.680,24.14,0.900,bicubic,-76.530,-45.780,-62 +deit_small_patch16_224.fb_in1k,224,18.720,81.280,51.093,48.907,22.05,0.900,bicubic,-75.710,-47.497,+145 +mobilevitv2_200.cvnets_in1k,256,18.587,81.413,50.080,49.920,18.45,0.888,bicubic,-76.273,-48.710,+39 +edgenext_small.usi_in1k,320,18.547,81.453,53.560,46.440,5.59,1.000,bicubic,-76.873,-45.540,-98 +mixer_b16_224.miil_in21k_ft_in1k,224,18.547,81.453,51.213,48.787,59.88,0.875,bilinear,-76.813,-47.767,-80 +ecaresnet50t.a2_in1k,288,18.440,81.560,48.760,51.240,25.57,1.000,bicubic,-76.910,-50.090,-81 +regnetx_080.tv2_in1k,224,18.413,81.587,50.133,49.867,39.57,0.965,bicubic,-76.697,-48.577,-25 +seresnet50.ra2_in1k,288,18.360,81.640,51.067,48.933,28.09,0.950,bicubic,-76.970,-47.833,-77 +poolformer_s36.sail_in1k,224,18.293,81.707,52.027,47.973,30.86,0.900,bicubic,-76.817,-46.783,-28 +repvit_m3.dist_in1k,224,18.293,81.707,51.680,48.320,10.68,0.950,bicubic,-76.947,-47.300,-60 +seresnext50_32x4d.racm_in1k,224,18.240,81.760,50.893,49.107,27.56,0.875,bicubic,-76.790,-47.907,-13 +wide_resnet50_2.tv2_in1k,224,18.133,81.867,52.267,47.733,68.88,0.965,bilinear,-76.737,-46.503,+20 +ese_vovnet39b.ra_in1k,288,18.080,81.920,49.613,50.387,24.57,0.950,bicubic,-76.810,-49.297,+14 +cait_xxs36_224.fb_dist_in1k,224,18.080,81.920,49.267,50.733,17.30,1.000,bicubic,-76.160,-49.443,+168 +sehalonet33ts.ra2_in1k,256,18.040,81.960,47.613,52.387,13.69,0.940,bicubic,-76.750,-51.327,+43 +mobilenetv4_hybrid_medium.e500_r224_in1k,256,18.013,81.987,51.613,48.387,11.07,1.000,bicubic,-77.457,-47.717,-121 +ecaresnet50d.miil_in1k,224,18.000,82.000,51.507,48.493,25.58,0.875,bicubic,-76.630,-47.193,+81 +gcresnet50t.ra2_in1k,288,17.947,82.053,49.373,50.627,25.90,1.000,bicubic,-77.333,-49.627,-74 +tf_efficientnet_lite4.in1k,380,17.907,82.093,50.307,49.693,13.01,0.920,bilinear,-76.983,-48.713,+7 +cs3darknet_l.c2ns_in1k,288,17.853,82.147,51.453,48.547,21.16,0.950,bicubic,-77.287,-46.957,-49 +resnet101.a1_in1k,224,17.853,82.147,45.573,54.427,44.55,0.950,bicubic,-76.677,-53.057,+107 +resnest50d_1s4x24d.in1k,224,17.627,82.373,49.627,50.373,25.68,0.875,bicubic,-77.133,-49.173,+38 +mobilevitv2_175.cvnets_in1k,256,17.600,82.400,49.227,50.773,14.25,0.888,bicubic,-77.270,-49.323,+11 +resnet50d.ra2_in1k,288,17.547,82.453,49.880,50.120,25.58,0.950,bicubic,-77.463,-49.090,-22 +vit_tiny_patch16_384.augreg_in21k_ft_in1k,384,17.547,82.453,49.747,50.253,5.79,1.000,bicubic,-76.083,-48.483,+268 +resnetv2_50.a1h_in1k,288,17.533,82.467,49.880,50.120,25.55,1.000,bicubic,-77.297,-48.990,+26 +tiny_vit_5m_224.in1k,224,17.520,82.480,49.960,50.040,5.39,0.950,bicubic,-76.760,-48.580,+149 +hgnet_tiny.paddle_in1k,288,17.480,82.520,50.853,49.147,14.74,1.000,bicubic,-77.480,-48.207,-15 +seresnext101_32x4d.gluon_in1k,224,17.400,82.600,46.493,53.507,48.96,0.875,bicubic,-77.520,-52.387,-4 +resnest50d.in1k,224,17.347,82.653,50.547,49.453,27.48,0.875,bilinear,-77.483,-48.323,+21 +resnet152.a2_in1k,224,17.347,82.653,45.600,54.400,60.19,0.950,bicubic,-77.603,-53.360,-11 +hgnetv2_b2.ssld_stage2_ft_in1k,224,17.293,82.707,51.787,48.213,11.22,0.965,bicubic,-78.247,-46.973,-175 +resnet50.c2_in1k,288,17.293,82.707,49.387,50.613,25.56,1.000,bicubic,-77.597,-49.473,-2 +inception_v4.tf_in1k,299,17.293,82.707,45.373,54.627,42.68,0.875,bicubic,-77.087,-53.517,+123 +dm_nfnet_f0.dm_in1k,192,17.200,82.800,45.027,54.973,71.49,0.900,bicubic,-78.180,-53.893,-117 +convnext_pico.d1_in1k,288,17.133,82.867,49.707,50.293,9.05,0.950,bicubic,-77.617,-48.973,+28 +tf_efficientnet_b3.ap_in1k,300,17.120,82.880,49.560,50.440,12.23,0.904,bicubic,-78.230,-49.370,-111 +efficientnet_el.ra_in1k,300,17.093,82.907,49.893,50.107,10.59,0.904,bicubic,-78.027,-49.097,-62 +xcit_tiny_24_p16_224.fb_dist_in1k,224,17.093,82.907,47.427,52.573,12.12,1.000,bicubic,-77.437,-51.373,+85 +regnetx_032.tv2_in1k,224,17.000,83.000,48.120,51.880,15.30,0.965,bicubic,-77.660,-50.720,+45 +fastvit_s12.apple_dist_in1k,256,16.987,83.013,49.333,50.667,9.47,0.900,bicubic,-77.883,-49.537,-5 +gcresnext50ts.ch_in1k,288,16.973,83.027,47.987,52.013,15.67,1.000,bicubic,-77.887,-50.823,-4 +xception71.tf_in1k,299,16.893,83.107,45.613,54.387,42.34,0.903,bicubic,-77.437,-53.237,+119 +cs3darknet_focus_l.c2ns_in1k,288,16.853,83.147,50.053,49.947,21.15,0.950,bicubic,-78.307,-48.907,-77 +hrnet_w18_ssld.paddle_in1k,224,16.827,83.173,46.867,53.133,21.30,0.950,bilinear,-78.533,-52.003,-124 +regnety_016.tv2_in1k,224,16.787,83.213,49.840,50.160,11.20,0.965,bicubic,-77.793,-49.060,+65 +tf_efficientnet_b3.aa_in1k,300,16.787,83.213,49.187,50.813,12.23,0.904,bicubic,-78.223,-49.723,-42 +gc_efficientnetv2_rw_t.agc_in1k,224,16.733,83.267,46.653,53.347,13.68,1.000,bicubic,-78.307,-52.247,-52 +resnext101_64x4d.gluon_in1k,224,16.720,83.280,44.027,55.973,83.46,0.875,bicubic,-77.950,-54.623,+35 +resmlp_36_224.fb_distilled_in1k,224,16.707,83.293,51.093,48.907,44.69,0.875,bicubic,-78.173,-47.777,-19 +convnextv2_pico.fcmae_ft_in1k,288,16.707,83.293,50.000,50.000,9.07,0.950,bicubic,-78.543,-48.800,-104 +efficientnetv2_rw_t.ra2_in1k,224,16.707,83.293,47.067,52.933,13.65,1.000,bicubic,-78.273,-51.683,-42 +poolformerv2_s36.sail_in1k,224,16.680,83.320,49.267,50.733,30.79,1.000,bicubic,-78.650,-49.743,-120 +tf_efficientnetv2_b3.in1k,300,16.587,83.413,48.480,51.520,14.36,0.904,bicubic,-78.593,-50.350,-92 +ecaresnet50t.a1_in1k,224,16.520,83.480,47.147,52.853,25.57,0.950,bicubic,-78.160,-51.673,+26 +resnet50d.ra4_e3600_r224_in1k,288,16.453,83.547,49.013,50.987,25.58,1.000,bicubic,-78.867,-49.807,-122 +inception_resnet_v2.tf_in1k,299,16.453,83.547,44.720,55.280,55.84,0.897,bicubic,-78.117,-54.240,+59 +tresnet_l.miil_in1k,224,16.440,83.560,49.573,50.427,55.99,0.875,bilinear,-78.830,-49.437,-114 +resnet152s.gluon_in1k,224,16.440,83.560,44.347,55.653,60.32,0.875,bicubic,-78.620,-54.583,-66 +convnext_pico_ols.d1_in1k,288,16.347,83.653,49.267,50.733,9.06,1.000,bicubic,-78.293,-49.503,+36 +resnet101.tv2_in1k,224,16.333,83.667,48.333,51.667,44.55,0.965,bilinear,-78.957,-50.597,-119 +gcresnet50t.ra2_in1k,256,16.320,83.680,48.320,51.680,25.90,0.900,bicubic,-78.540,-50.530,-22 +resnet152d.gluon_in1k,224,16.320,83.680,44.080,55.920,60.21,0.875,bicubic,-78.500,-54.680,-8 +fastvit_sa12.apple_in1k,256,16.307,83.693,49.387,50.613,11.58,0.900,bicubic,-78.583,-49.423,-35 +convnext_nano.d1h_in1k,224,16.293,83.707,47.440,52.560,15.59,0.950,bicubic,-78.637,-51.230,-42 +gernet_l.idstcv_in1k,256,16.253,83.747,46.773,53.227,31.08,0.875,bilinear,-78.857,-52.257,-86 +resmlp_24_224.fb_distilled_in1k,224,16.227,83.773,49.947,50.053,30.02,0.875,bicubic,-78.223,-48.823,+77 +mobilevitv2_150.cvnets_in1k,256,16.227,83.773,47.960,52.040,10.59,0.888,bicubic,-78.303,-50.740,+57 +convnext_nano_ols.d1h_in1k,224,16.200,83.800,47.893,52.107,15.65,0.950,bicubic,-78.540,-50.727,+1 +seresnet50.a1_in1k,288,16.173,83.827,46.853,53.147,28.09,1.000,bicubic,-78.497,-51.867,+13 +wide_resnet50_2.racm_in1k,224,16.133,83.867,48.093,51.907,68.88,0.875,bicubic,-78.947,-50.877,-83 +inception_resnet_v2.tf_ens_adv_in1k,299,16.120,83.880,43.333,56.667,55.84,0.897,bicubic,-78.100,-55.267,+113 +resnetv2_50d_evos.ah_in1k,224,16.107,83.893,46.467,53.533,25.59,0.950,bicubic,-78.863,-52.523,-62 +repvgg_b3g4.rvgg_in1k,224,16.067,83.933,47.480,52.520,83.83,0.875,bilinear,-78.453,-51.340,+55 +gmlp_s16_224.ra3_in1k,224,16.067,83.933,44.707,55.293,19.42,0.875,bicubic,-78.093,-53.793,+128 +gcresnext50ts.ch_in1k,256,16.053,83.947,46.160,53.840,15.67,0.900,bicubic,-78.477,-52.560,+49 +resnetv2_50d_gn.ah_in1k,224,16.053,83.947,46.133,53.867,25.57,0.950,bicubic,-78.697,-52.537,-13 +xcit_tiny_24_p16_224.fb_in1k,224,16.053,83.947,45.267,54.733,12.12,1.000,bicubic,-78.027,-53.243,+140 +repvit_m1_1.dist_450e_in1k,224,16.040,83.960,49.120,50.880,8.80,0.950,bicubic,-78.850,-49.800,-52 +ecaresnet101d_pruned.miil_in1k,224,15.907,84.093,48.573,51.427,24.88,0.875,bicubic,-79.163,-50.407,-91 +mobilenetv4_hybrid_medium.e500_r224_in1k,224,15.893,84.107,47.973,52.027,11.07,0.950,bicubic,-78.967,-50.827,-38 +ecaresnet50d_pruned.miil_in1k,288,15.880,84.120,49.653,50.347,19.94,0.950,bicubic,-79.230,-49.257,-104 +efficientnet_b1.ra4_e3600_r240_in1k,288,15.880,84.120,48.693,51.307,7.79,1.000,bicubic,-79.440,-50.277,-148 +xception65.tf_in1k,299,15.880,84.120,43.480,56.520,39.92,0.903,bicubic,-77.890,-55.140,+193 +resnet50.a1_in1k,288,15.853,84.147,45.627,54.373,25.56,1.000,bicubic,-78.947,-53.123,-30 +resnet50.fb_ssl_yfcc100m_ft_in1k,224,15.840,84.160,49.333,50.667,25.56,0.875,bilinear,-78.650,-49.587,+52 +edgenext_small_rw.sw_in1k,320,15.800,84.200,49.520,50.480,7.83,1.000,bicubic,-78.880,-49.060,-8 +resnext50_32x4d.ra_in1k,288,15.733,84.267,46.920,53.080,25.03,0.950,bicubic,-78.967,-51.720,-14 +fastvit_t12.apple_dist_in1k,256,15.627,84.373,47.640,52.360,7.55,0.900,bicubic,-78.993,-50.920,+14 +resnet50.c1_in1k,288,15.613,84.387,47.107,52.893,25.56,1.000,bicubic,-79.177,-51.813,-33 +ecaresnet26t.ra2_in1k,320,15.520,84.480,47.947,52.053,16.01,0.950,bicubic,-78.780,-50.833,+77 +regnety_320.pycls_in1k,224,15.493,84.507,44.587,55.413,145.05,0.875,bicubic,-79.097,-54.083,+15 +vit_base_patch32_384.augreg_in1k,384,15.493,84.507,43.733,56.267,88.30,1.000,bicubic,-78.147,-54.937,+199 +coat_tiny.in1k,224,15.467,84.533,45.600,54.400,5.50,0.900,bicubic,-78.113,-52.820,+208 +mobilenetv4_conv_blur_medium.e500_r224_in1k,256,15.440,84.560,47.960,52.040,9.72,1.000,bicubic,-79.430,-51.000,-60 +convmixer_768_32.in1k,224,15.387,84.613,47.613,52.387,21.11,0.960,bicubic,-79.113,-51.237,+37 +edgenext_small.usi_in1k,256,15.320,84.680,48.360,51.640,5.59,0.950,bicubic,-79.630,-50.210,-79 +resnet50d.a2_in1k,288,15.320,84.680,44.547,55.453,25.58,1.000,bicubic,-79.270,-54.303,+10 +resnet50.d_in1k,288,15.320,84.680,44.493,55.507,25.56,1.000,bicubic,-79.640,-54.377,-80 +ecaresnetlight.miil_in1k,224,15.227,84.773,45.600,54.400,30.16,0.875,bicubic,-79.533,-53.260,-37 +cs3darknet_l.c2ns_in1k,256,15.213,84.787,48.640,51.360,21.16,0.887,bicubic,-79.337,-50.140,+18 +resnext50d_32x4d.bt_in1k,288,15.213,84.787,45.933,54.067,25.05,0.950,bicubic,-79.327,-52.757,+20 +skresnext50_32x4d.ra_in1k,224,15.187,84.813,44.560,55.440,27.48,0.875,bicubic,-79.073,-53.910,+76 +repvit_m1_0.dist_450e_in1k,224,15.027,84.973,46.653,53.347,7.30,0.950,bicubic,-79.513,-52.227,+16 +fastvit_s12.apple_in1k,256,15.027,84.973,45.000,55.000,9.47,0.900,bicubic,-79.193,-53.640,+82 +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,256,15.027,84.973,42.333,57.667,119.42,0.900,bicubic,-78.723,-55.767,+173 +seresnet33ts.ra2_in1k,288,15.013,84.987,47.067,52.933,19.78,1.000,bicubic,-80.027,-51.773,-113 +resnext50_32x4d.a2_in1k,288,15.013,84.987,45.080,54.920,25.03,1.000,bicubic,-79.547,-53.560,+11 +efficientvit_b1.r288_in1k,288,15.000,85.000,46.333,53.667,9.10,1.000,bicubic,-79.480,-52.457,+31 +eca_resnet33ts.ra2_in1k,288,14.987,85.013,48.600,51.400,19.68,1.000,bicubic,-79.663,-50.260,-21 +hgnetv2_b2.ssld_stage1_in22k_in1k,224,14.947,85.053,48.347,51.653,11.22,0.965,bicubic,-80.273,-50.533,-156 +vit_base_patch16_224.augreg_in1k,224,14.907,85.093,41.893,58.107,86.57,0.900,bicubic,-78.723,-56.347,+181 +cait_xxs24_224.fb_dist_in1k,224,14.880,85.120,44.533,55.467,11.96,1.000,bicubic,-78.700,-53.907,+188 +regnetz_b16.ra3_in1k,224,14.827,85.173,45.800,54.200,9.72,0.940,bicubic,-79.733,-53.080,+3 +levit_192.fb_dist_in1k,224,14.693,85.307,44.640,55.360,10.95,0.900,bicubic,-79.487,-53.900,+80 +levit_conv_192.fb_dist_in1k,224,14.680,85.320,44.653,55.347,10.95,0.900,bicubic,-79.500,-53.887,+80 +res2net50d.in1k,224,14.653,85.347,44.400,55.600,25.72,0.875,bilinear,-79.647,-53.960,+53 +seresnet50.a2_in1k,288,14.653,85.347,44.147,55.853,28.09,1.000,bicubic,-80.027,-54.633,-38 +poolformerv2_s24.sail_in1k,224,14.640,85.360,45.973,54.027,21.34,1.000,bicubic,-80.010,-52.857,-28 +convnextv2_pico.fcmae_ft_in1k,224,14.627,85.373,45.840,54.160,9.07,0.875,bicubic,-79.833,-52.780,+23 +gcresnet33ts.ra2_in1k,288,14.573,85.427,46.013,53.987,19.88,1.000,bicubic,-80.347,-52.767,-97 +repvit_m1_1.dist_300e_in1k,224,14.547,85.453,46.800,53.200,8.80,0.950,bicubic,-80.213,-52.180,-61 +cs3darknet_focus_l.c2ns_in1k,256,14.480,85.520,46.507,53.493,21.15,0.887,bicubic,-80.180,-52.303,-37 +darknet53.c2ns_in1k,288,14.440,85.560,46.813,53.187,41.61,1.000,bicubic,-80.180,-51.967,-23 +efficientnet_el_pruned.in1k,300,14.413,85.587,45.827,54.173,10.59,0.904,bicubic,-79.967,-52.733,+30 +coat_lite_mini.in1k,224,14.400,85.600,44.533,55.467,11.01,0.900,bicubic,-79.650,-53.707,+95 +rexnet_150.nav_in1k,224,14.373,85.627,46.440,53.560,9.73,0.875,bicubic,-80.107,-52.110,+12 +resnet50.tv2_in1k,224,14.333,85.667,46.747,53.253,25.56,0.965,bilinear,-80.327,-52.033,-41 +resnext50_32x4d.a1h_in1k,224,14.320,85.680,44.000,56.000,25.03,0.950,bicubic,-80.220,-54.600,-6 +efficientnet_b2.ra_in1k,288,14.307,85.693,45.827,54.173,9.11,1.000,bicubic,-80.313,-52.973,-24 +seresnet33ts.ra2_in1k,256,14.280,85.720,45.840,54.160,19.78,0.900,bicubic,-80.580,-52.880,-92 +darknetaa53.c2ns_in1k,288,14.120,85.880,44.880,55.120,36.02,1.000,bilinear,-80.400,-53.790,-1 +poolformer_s24.sail_in1k,224,14.107,85.893,47.040,52.960,21.39,0.900,bicubic,-80.473,-51.620,-23 +repvit_m2.dist_in1k,224,14.053,85.947,46.320,53.680,8.80,0.950,bicubic,-80.697,-52.380,-68 +seresnet50.ra2_in1k,224,14.053,85.947,45.413,54.587,28.09,0.875,bicubic,-80.517,-53.387,-19 +pvt_v2_b1.in1k,224,14.027,85.973,47.360,52.640,14.01,0.900,bicubic,-79.793,-51.300,+129 +mobilevitv2_125.cvnets_in1k,256,14.000,86.000,44.280,55.720,7.48,0.888,bicubic,-79.990,-54.300,+100 +eca_resnet33ts.ra2_in1k,256,13.987,86.013,47.027,52.973,19.68,0.900,bicubic,-80.213,-51.313,+50 +gernet_m.idstcv_in1k,224,13.973,86.027,45.893,54.107,21.14,0.875,bilinear,-80.677,-52.597,-49 +fbnetv3_d.ra2_in1k,256,13.920,86.080,46.040,53.960,10.31,0.950,bilinear,-79.990,-52.700,+108 +resnet101.a2_in1k,224,13.907,86.093,42.960,57.040,44.55,0.950,bicubic,-80.793,-55.800,-65 +legacy_seresnext101_32x4d.in1k,224,13.867,86.133,42.587,57.413,48.96,0.875,bilinear,-80.503,-55.893,+15 +resnext101_32x4d.gluon_in1k,224,13.667,86.333,41.467,58.533,44.18,0.875,bicubic,-80.863,-57.053,-16 +gcresnet33ts.ra2_in1k,256,13.640,86.360,44.813,55.187,19.88,0.900,bicubic,-80.860,-53.967,-9 +mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k,256,13.627,86.373,45.427,54.573,8.46,0.950,bicubic,-81.333,-53.643,-129 +edgenext_small_rw.sw_in1k,256,13.600,86.400,45.907,54.093,7.83,0.900,bicubic,-80.330,-52.643,+98 +mobilenetv4_conv_medium.e500_r256_in1k,256,13.600,86.400,44.893,55.107,9.72,0.950,bicubic,-80.700,-53.637,+19 +dpn68b.ra_in1k,288,13.600,86.400,39.867,60.133,12.61,1.000,bicubic,-80.380,-58.673,+92 +mobilenet_edgetpu_v2_m,256,13.587,86.413,45.427,54.573,8.46,0.950,bicubic,-81.373,-53.383,-132 +efficientnet_b2.ra_in1k,256,13.587,86.413,44.200,55.800,9.11,0.875,bicubic,-80.423,-54.280,+83 +fastvit_t12.apple_in1k,256,13.587,86.413,43.253,56.747,7.55,0.900,bicubic,-80.403,-55.307,+87 +hgnet_tiny.paddle_in1k,224,13.573,86.427,44.000,56.000,14.74,0.965,bicubic,-80.717,-54.750,+20 +pit_xs_distilled_224.in1k,224,13.507,86.493,45.107,54.893,11.00,0.900,bicubic,-80.263,-53.313,+121 +wide_resnet101_2.tv2_in1k,176,13.453,86.547,40.907,59.093,126.89,0.875,bilinear,-81.017,-57.873,-12 +darknet53.c2ns_in1k,256,13.440,86.560,44.720,55.280,41.61,0.887,bicubic,-80.870,-54.140,+9 +seresnext50_32x4d.gluon_in1k,224,13.440,86.560,43.440,56.560,27.56,0.875,bicubic,-80.920,-55.180,+3 +resmlp_36_224.fb_in1k,224,13.427,86.573,46.253,53.747,44.69,0.875,bicubic,-80.773,-52.407,+33 +ecaresnet50t.a2_in1k,224,13.400,86.600,42.493,57.507,25.57,0.950,bicubic,-81.100,-56.147,-22 +mobilenetv4_conv_medium.e500_r224_in1k,256,13.373,86.627,45.067,54.933,9.72,1.000,bicubic,-81.387,-53.863,-97 +eca_botnext26ts_256.c1_in1k,256,13.360,86.640,42.267,57.733,10.59,0.950,bicubic,-80.450,-56.233,+108 +resnet50_gn.a1h_in1k,224,13.333,86.667,42.773,57.227,25.56,0.940,bicubic,-80.997,-55.927,0 +repvgg_b2g4.rvgg_in1k,224,13.240,86.760,43.440,56.560,61.76,0.875,bilinear,-80.630,-54.950,+93 +resnet152.a3_in1k,224,13.240,86.760,42.773,57.227,60.19,0.950,bicubic,-81.480,-55.917,-92 +regnetx_320.pycls_in1k,224,13.173,86.827,40.280,59.720,107.81,0.875,bicubic,-81.267,-58.450,-18 +resnet50.b1k_in1k,288,13.147,86.853,43.773,56.227,25.56,1.000,bicubic,-81.713,-54.907,-125 +ese_vovnet39b.ra_in1k,224,13.133,86.867,43.733,56.267,24.57,0.875,bicubic,-80.967,-54.947,+46 +visformer_tiny.in1k,224,13.133,86.867,43.573,56.427,10.32,0.900,bicubic,-80.457,-55.127,+133 +resnet50d.a1_in1k,224,13.133,86.867,41.227,58.773,25.58,0.950,bicubic,-81.167,-57.513,+3 +vit_small_patch16_224.augreg_in1k,224,13.067,86.933,40.987,59.013,22.05,0.900,bicubic,-80.833,-57.643,+83 +cspresnext50.ra_in1k,256,13.040,86.960,45.053,54.947,20.57,0.887,bilinear,-81.790,-53.857,-117 +efficientnet_b3_pruned.in1k,300,13.027,86.973,44.893,55.107,9.86,0.904,bicubic,-81.593,-54.007,-66 +mobilevit_s.cvnets_in1k,256,13.013,86.987,40.827,59.173,5.58,0.900,bicubic,-80.197,-57.643,+181 +mixnet_xl.ra_in1k,224,12.973,87.027,43.133,56.867,11.90,0.875,bicubic,-81.227,-55.507,+20 +efficientformerv2_s1.snap_dist_in1k,224,12.960,87.040,42.400,57.600,6.19,0.950,bicubic,-81.140,-56.270,+37 +nf_regnet_b1.ra2_in1k,288,12.907,87.093,43.907,56.093,10.22,0.900,bicubic,-81.243,-54.833,+32 +efficientformer_l1.snap_dist_in1k,224,12.893,87.107,45.147,54.853,12.29,0.950,bicubic,-81.607,-53.673,-42 +eca_halonext26ts.c1_in1k,256,12.893,87.107,42.200,57.800,10.76,0.940,bicubic,-81.157,-56.300,+45 +resnext50_32x4d.a1_in1k,224,12.893,87.107,40.813,59.187,25.03,0.950,bicubic,-81.177,-57.387,+39 +resnet101d.gluon_in1k,224,12.773,87.227,41.333,58.667,44.57,0.875,bicubic,-81.477,-57.217,+1 +regnetx_016.tv2_in1k,224,12.720,87.280,45.173,54.827,9.19,0.965,bicubic,-81.450,-53.567,+17 +repvit_m1_0.dist_300e_in1k,224,12.720,87.280,44.187,55.813,7.30,0.950,bicubic,-81.610,-54.453,-21 +pit_xs_224.in1k,224,12.707,87.293,42.600,57.400,10.62,0.900,bicubic,-80.413,-55.710,+184 +hgnetv2_b1.ssld_stage2_ft_in1k,288,12.600,87.400,46.733,53.267,6.34,1.000,bicubic,-81.970,-52.027,-69 +resnetblur50.bt_in1k,288,12.573,87.427,44.280,55.720,25.56,0.950,bicubic,-81.897,-54.400,-43 +resnet50.b2k_in1k,288,12.560,87.440,43.760,56.240,25.56,1.000,bicubic,-82.150,-55.260,-113 +tf_efficientnet_b3.in1k,300,12.560,87.440,43.213,56.787,12.23,0.904,bicubic,-82.080,-55.557,-88 +crossvit_9_dagger_240.in1k,240,12.560,87.440,41.387,58.613,8.78,0.875,bicubic,-80.380,-56.823,+201 +resnext50_32x4d.tv2_in1k,224,12.533,87.467,42.853,57.147,25.03,0.965,bilinear,-82.087,-55.857,-84 +inception_v3.gluon_in1k,299,12.533,87.467,40.213,59.787,23.83,0.875,bicubic,-80.957,-58.327,+128 +resmlp_24_224.fb_in1k,224,12.467,87.533,43.413,56.587,30.02,0.875,bicubic,-81.553,-55.197,+41 +tresnet_m.miil_in1k,224,12.467,87.533,41.707,58.293,31.39,0.875,bilinear,-82.153,-57.043,-83 +wide_resnet50_2.tv2_in1k,176,12.453,87.547,43.467,56.533,68.88,0.875,bilinear,-81.747,-55.173,-3 +efficientnet_b1.ra4_e3600_r240_in1k,240,12.427,87.573,43.933,56.067,7.79,0.900,bicubic,-82.413,-54.687,-147 +efficientvit_b1.r256_in1k,256,12.427,87.573,41.840,58.160,9.10,1.000,bicubic,-81.613,-56.700,+33 +darknetaa53.c2ns_in1k,256,12.413,87.587,42.760,57.240,36.02,0.887,bilinear,-81.487,-55.870,+55 +regnety_120.pycls_in1k,224,12.387,87.613,41.840,58.160,51.82,0.875,bicubic,-82.133,-57.120,-66 +ecaresnet26t.ra2_in1k,256,12.253,87.747,42.640,57.360,16.01,0.875,bicubic,-81.587,-56.010,+64 +coat_lite_tiny.in1k,224,12.253,87.747,41.080,58.920,5.72,0.900,bicubic,-81.007,-57.310,+153 +resnet50.a1h_in1k,224,12.240,87.760,43.707,56.293,25.56,1.000,bicubic,-82.530,-54.973,-140 +convnext_femto_ols.d1_in1k,288,12.240,87.760,43.507,56.493,5.23,0.950,bicubic,-81.660,-54.953,+49 +mobilenetv4_conv_blur_medium.e500_r224_in1k,224,12.227,87.773,42.987,57.013,9.72,0.950,bicubic,-82.063,-55.813,-28 +resnetaa50.a1h_in1k,224,12.213,87.787,42.360,57.640,25.56,0.950,bicubic,-82.417,-56.520,-102 +regnety_160.pycls_in1k,224,12.187,87.813,41.133,58.867,83.59,0.875,bicubic,-82.193,-57.697,-49 +resnet50.a2_in1k,288,12.053,87.947,40.093,59.907,25.56,1.000,bicubic,-82.597,-58.797,-112 +efficientnet_em.ra2_in1k,240,11.987,88.013,43.640,56.360,6.90,0.882,bicubic,-81.853,-55.170,+54 +resnet101s.gluon_in1k,224,11.987,88.013,40.800,59.200,44.67,0.875,bicubic,-82.753,-58.020,-137 +hgnetv2_b1.ssld_stage1_in22k_in1k,288,11.960,88.040,45.333,54.667,6.34,1.000,bicubic,-82.620,-53.547,-96 +convnext_pico.d1_in1k,224,11.947,88.053,43.453,56.547,9.05,0.875,bicubic,-82.113,-55.037,+10 +hrnet_w64.ms_in1k,224,11.920,88.080,40.773,59.227,128.06,0.875,bilinear,-82.100,-57.557,+22 +resnetrs101.tf_in1k,192,11.920,88.080,38.707,61.293,63.62,0.940,bicubic,-82.780,-60.003,-135 +resnet101.a3_in1k,224,11.787,88.213,40.280,59.720,44.55,0.950,bicubic,-82.263,-58.190,+9 +xcit_tiny_12_p16_224.fb_dist_in1k,224,11.747,88.253,39.853,60.147,6.72,1.000,bicubic,-81.653,-58.357,+117 +resnet50d.ra2_in1k,224,11.733,88.267,42.200,57.800,25.58,0.875,bicubic,-82.567,-56.520,-46 +xception41.tf_in1k,299,11.707,88.293,38.707,61.293,26.97,0.903,bicubic,-81.713,-59.703,+112 +nf_resnet50.ra2_in1k,288,11.693,88.307,45.347,54.653,25.56,0.940,bicubic,-82.927,-53.463,-114 +cspdarknet53.ra_in1k,256,11.693,88.307,43.240,56.760,27.64,0.887,bilinear,-82.977,-55.560,-135 +ecaresnet50t.a3_in1k,224,11.693,88.307,40.907,59.093,25.57,0.950,bicubic,-82.707,-57.763,-64 +efficientnet_b0.ra4_e3600_r224_in1k,256,11.640,88.360,40.907,59.093,5.29,1.000,bicubic,-82.230,-57.693,+39 +gmixer_24_224.ra3_in1k,224,11.627,88.373,37.267,62.733,24.72,0.875,bicubic,-81.243,-60.623,+175 +convnextv2_femto.fcmae_ft_in1k,288,11.600,88.400,40.600,59.400,5.23,0.950,bicubic,-82.600,-58.170,-29 +vit_small_patch32_224.augreg_in21k_ft_in1k,224,11.587,88.413,39.533,60.467,22.88,0.900,bicubic,-80.493,-58.717,+251 +fbnetv3_b.ra2_in1k,256,11.560,88.440,44.067,55.933,8.60,0.950,bilinear,-82.440,-54.563,+13 +botnet26t_256.c1_in1k,256,11.507,88.493,39.760,60.240,12.49,0.950,bicubic,-82.013,-58.670,+86 +dla102x2.in1k,224,11.453,88.547,41.133,58.867,41.28,0.875,bilinear,-82.557,-57.327,+9 +resnet50.ra_in1k,288,11.440,88.560,41.147,58.853,25.56,0.950,bicubic,-82.780,-57.423,-42 +seresnet50.a1_in1k,224,11.400,88.600,39.107,60.893,28.09,0.950,bicubic,-82.630,-59.313,+4 +xcit_nano_12_p16_384.fb_dist_in1k,384,11.387,88.613,39.573,60.427,3.05,1.000,bicubic,-80.513,-58.467,+261 +regnety_080.pycls_in1k,224,11.373,88.627,40.733,59.267,39.18,0.875,bicubic,-82.807,-57.947,-35 +efficientvit_b1.r224_in1k,224,11.373,88.627,39.973,60.027,9.10,0.950,bicubic,-82.147,-58.357,+80 +levit_128.fb_dist_in1k,224,11.360,88.640,39.747,60.253,9.21,0.900,bicubic,-82.030,-58.863,+104 +mobilenet_edgetpu_v2_m,224,11.347,88.653,42.013,57.987,8.46,0.900,bicubic,-82.953,-56.577,-62 +mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k,224,11.320,88.680,42.120,57.880,8.46,0.900,bicubic,-83.000,-56.460,-69 +resnet152.tv2_in1k,176,11.320,88.680,38.067,61.933,60.19,0.875,bilinear,-82.770,-60.473,-19 +fbnetv3_d.ra2_in1k,224,11.307,88.693,41.053,58.947,10.31,0.950,bilinear,-82.133,-57.577,+87 +levit_conv_128.fb_dist_in1k,224,11.293,88.707,39.720,60.280,9.21,0.900,bicubic,-82.077,-58.660,+100 +efficientnet_b2_pruned.in1k,260,11.280,88.720,41.840,58.160,8.31,0.890,bicubic,-82.860,-56.670,-29 +lambda_resnet26t.c1_in1k,256,11.280,88.720,39.973,60.027,10.96,0.940,bicubic,-82.590,-58.637,+20 +tf_efficientnet_el.in1k,300,11.253,88.747,41.440,58.560,10.59,0.904,bicubic,-83.147,-57.160,-88 +seresnext26t_32x4d.bt_in1k,288,11.187,88.813,40.747,59.253,16.81,0.950,bicubic,-82.383,-57.653,+65 +tf_efficientnetv2_b3.in1k,240,11.187,88.813,39.160,60.840,14.36,0.904,bicubic,-83.393,-59.680,-127 +dpn92.mx_in1k,224,11.120,88.880,39.440,60.560,37.67,0.875,bicubic,-83.160,-59.310,-66 +convnext_femto.d1_in1k,288,11.107,88.893,42.560,57.440,5.22,0.950,bicubic,-82.813,-55.710,+3 +tf_efficientnet_b0.ns_jft_in1k,224,11.093,88.907,39.907,60.093,5.29,0.875,bicubic,-82.597,-58.723,+41 +mobilenetv4_conv_medium.e500_r224_in1k,224,11.067,88.933,40.480,59.520,9.72,0.950,bicubic,-83.143,-58.220,-57 +resnet152c.gluon_in1k,224,11.053,88.947,36.787,63.213,60.21,0.875,bicubic,-83.107,-61.843,-45 +ecaresnet50d_pruned.miil_in1k,224,11.027,88.973,42.053,57.947,19.94,0.875,bicubic,-83.233,-56.647,-69 +mobilevitv2_100.cvnets_in1k,256,11.027,88.973,40.133,59.867,4.90,0.888,bicubic,-82.263,-58.117,+101 +xcit_tiny_12_p16_224.fb_in1k,224,10.947,89.053,36.893,63.107,6.72,1.000,bicubic,-81.633,-61.357,+181 +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,384,10.933,89.067,39.427,60.573,6.36,1.000,bicubic,-81.117,-58.863,+224 +inception_v3.tf_adv_in1k,299,10.933,89.067,36.760,63.240,23.83,0.875,bicubic,-81.967,-61.450,+143 +resnet50d.ra4_e3600_r224_in1k,224,10.893,89.107,40.387,59.613,25.58,0.950,bicubic,-83.737,-58.193,-152 +hrnet_w48.ms_in1k,224,10.893,89.107,39.973,60.027,77.47,0.875,bilinear,-83.047,-58.627,-9 +tf_efficientnetv2_b2.in1k,260,10.840,89.160,39.413,60.587,10.10,0.890,bicubic,-83.590,-59.287,-105 +halonet26t.a1h_in1k,256,10.840,89.160,38.627,61.373,12.48,0.950,bicubic,-83.210,-59.903,-31 +regnety_008_tv.tv2_in1k,224,10.827,89.173,40.613,59.387,6.43,0.965,bicubic,-82.863,-57.877,+29 +inception_v3.tf_in1k,299,10.827,89.173,36.507,63.493,23.83,0.875,bicubic,-82.523,-62.043,+83 +nf_regnet_b1.ra2_in1k,256,10.813,89.187,40.413,59.587,10.22,0.900,bicubic,-82.607,-57.807,+68 +mobileone_s4.apple_in1k,224,10.787,89.213,38.453,61.547,14.95,0.900,bilinear,-82.983,-59.797,+18 +dpn107.mx_in1k,224,10.747,89.253,38.040,61.960,86.92,0.875,bicubic,-83.623,-60.600,-101 +resnet50.c2_in1k,224,10.733,89.267,39.000,61.000,25.56,0.950,bicubic,-83.547,-59.660,-83 +resnetv2_50.a1h_in1k,224,10.720,89.280,39.267,60.733,25.55,0.950,bicubic,-83.730,-59.443,-116 +resnet34d.ra2_in1k,288,10.707,89.293,38.987,61.013,21.82,0.950,bicubic,-82.943,-59.523,+26 +convnext_pico_ols.d1_in1k,224,10.693,89.307,40.560,59.440,9.06,0.950,bicubic,-83.347,-57.810,-38 +resnext50_32x4d.ra_in1k,224,10.667,89.333,40.227,59.773,25.03,0.875,bicubic,-83.443,-58.103,-54 +xcit_nano_12_p8_224.fb_dist_in1k,224,10.640,89.360,37.987,62.013,3.05,1.000,bicubic,-81.480,-60.183,+202 +seresnext26d_32x4d.bt_in1k,288,10.627,89.373,40.960,59.040,16.81,0.950,bicubic,-82.813,-57.480,+57 +resnext50d_32x4d.bt_in1k,224,10.533,89.467,39.373,60.627,25.05,0.875,bicubic,-83.687,-59.217,-83 +tf_efficientnet_b2.ap_in1k,260,10.427,89.573,39.720,60.280,9.11,0.890,bicubic,-84.073,-58.920,-131 +repvit_m0_9.dist_450e_in1k,224,10.347,89.653,40.027,59.973,5.49,0.950,bicubic,-83.263,-58.483,+27 +dpn131.mx_in1k,224,10.347,89.653,36.653,63.347,79.25,0.875,bicubic,-83.683,-61.867,-42 +hrnet_w44.ms_in1k,224,10.320,89.680,39.320,60.680,67.06,0.875,bilinear,-83.270,-59.380,+28 +densenetblur121d.ra_in1k,288,10.307,89.693,39.520,60.480,8.00,0.950,bicubic,-82.323,-58.740,+152 +rexnet_130.nav_in1k,224,10.293,89.707,41.467,58.533,7.56,0.875,bicubic,-83.627,-56.933,-27 +convnextv2_femto.fcmae_ft_in1k,224,10.293,89.707,38.533,61.467,5.23,0.875,bicubic,-83.097,-59.957,+59 +xcit_nano_12_p8_224.fb_in1k,224,10.213,89.787,37.173,62.827,3.05,1.000,bicubic,-80.747,-60.587,+254 +resnext101_32x8d.tv_in1k,224,10.133,89.867,37.733,62.267,88.79,0.875,bilinear,-83.677,-60.667,-9 +seresnet50.a2_in1k,224,10.107,89.893,38.413,61.587,28.09,0.950,bicubic,-83.853,-60.217,-35 +resnet101.tv2_in1k,176,10.093,89.907,37.253,62.747,44.55,0.875,bilinear,-83.697,-61.197,-6 +resnetrs50.tf_in1k,224,10.080,89.920,37.267,62.733,35.69,0.910,bicubic,-84.210,-61.373,-105 +regnetx_160.pycls_in1k,224,10.040,89.960,37.773,62.227,54.28,0.875,bicubic,-84.120,-60.967,-82 +regnety_064.pycls_in1k,224,10.027,89.973,38.960,61.040,30.58,0.875,bicubic,-84.133,-59.630,-82 +lambda_resnet26rpt_256.c1_in1k,256,10.027,89.973,37.560,62.440,10.99,0.940,bicubic,-83.703,-60.930,-1 +dpn98.mx_in1k,224,9.987,90.013,35.973,64.027,61.57,0.875,bicubic,-84.173,-62.747,-81 +resnet50.a1_in1k,224,9.973,90.027,38.000,62.000,25.56,0.950,bicubic,-84.437,-60.430,-135 +resnet33ts.ra2_in1k,288,9.960,90.040,39.293,60.707,19.68,1.000,bicubic,-84.200,-59.167,-84 +efficientnet_b1.ft_in1k,256,9.947,90.053,37.307,62.693,7.79,1.000,bicubic,-83.333,-60.933,+63 +legacy_xception.tf_in1k,299,9.933,90.067,37.880,62.120,22.86,0.897,bicubic,-83.537,-60.650,+31 +resnext50_32x4d.a3_in1k,224,9.907,90.093,37.613,62.387,25.03,0.950,bicubic,-83.743,-60.957,+1 +legacy_seresnext50_32x4d.in1k,224,9.893,90.107,38.960,61.040,27.56,0.875,bilinear,-83.837,-59.630,-9 +inception_v3.tv_in1k,299,9.893,90.107,35.040,64.960,23.83,0.875,bicubic,-82.887,-62.910,+113 +efficientnet_b0.ra4_e3600_r224_in1k,224,9.880,90.120,38.120,61.880,5.29,0.900,bicubic,-83.360,-60.250,+61 +resnext50_32x4d.a2_in1k,224,9.840,90.160,37.067,62.933,25.03,0.950,bicubic,-84.210,-61.583,-68 +resnet152.gluon_in1k,224,9.747,90.253,35.920,64.080,60.19,0.875,bicubic,-84.413,-62.690,-89 +tf_efficientnet_b2.aa_in1k,260,9.680,90.320,38.880,61.120,9.11,0.890,bicubic,-84.720,-59.830,-142 +resnet50.c1_in1k,224,9.640,90.360,37.547,62.453,25.56,0.950,bicubic,-84.390,-61.163,-67 +tf_efficientnet_cc_b1_8e.in1k,240,9.613,90.387,36.960,63.040,39.72,0.882,bicubic,-84.307,-61.570,-49 +tf_efficientnet_lite3.in1k,300,9.587,90.413,39.147,60.853,8.20,0.904,bilinear,-84.613,-59.533,-105 +resnet50d.a2_in1k,224,9.560,90.440,37.680,62.320,25.58,0.950,bicubic,-84.540,-60.800,-85 +hgnetv2_b1.ssld_stage2_ft_in1k,224,9.547,90.453,39.613,60.387,6.34,0.965,bicubic,-84.333,-59.037,-46 +fbnetv3_b.ra2_in1k,224,9.493,90.507,38.840,61.160,8.60,0.950,bilinear,-83.807,-59.590,+46 +res2net101_26w_4s.in1k,224,9.427,90.573,34.427,65.573,45.21,0.875,bilinear,-84.323,-63.933,-23 +resnet50.ram_in1k,288,9.413,90.587,35.373,64.627,25.56,0.950,bicubic,-85.107,-63.387,-170 +cspresnet50.ra_in1k,256,9.360,90.640,39.667,60.333,21.62,0.887,bilinear,-84.440,-58.953,-33 +nf_resnet50.ra2_in1k,256,9.320,90.680,40.347,59.653,25.56,0.940,bicubic,-84.830,-58.343,-99 +resnet50.tv2_in1k,176,9.320,90.680,38.787,61.213,25.56,0.875,bilinear,-84.470,-59.603,-34 +resnet50.d_in1k,224,9.280,90.720,35.533,64.467,25.56,0.950,bicubic,-84.780,-62.897,-87 +legacy_seresnet152.in1k,224,9.253,90.747,37.040,62.960,66.82,0.875,bilinear,-84.187,-61.320,+15 +resnet34.a1_in1k,288,9.253,90.747,34.533,65.467,21.80,1.000,bicubic,-83.847,-63.957,+61 +hrnet_w40.ms_in1k,224,9.187,90.813,36.600,63.400,57.56,0.875,bilinear,-84.323,-61.760,+2 +repvit_m0_9.dist_300e_in1k,224,9.173,90.827,38.347,61.653,5.49,0.950,bicubic,-84.257,-60.383,+14 +regnetx_120.pycls_in1k,224,9.093,90.907,36.907,63.093,46.11,0.875,bicubic,-85.147,-61.743,-129 +dpn68b.ra_in1k,224,9.080,90.920,33.707,66.293,12.61,0.950,bicubic,-84.320,-64.793,+18 +vit_tiny_patch16_224.augreg_in21k_ft_in1k,224,9.053,90.947,34.160,65.840,5.72,0.900,bicubic,-82.717,-63.780,+182 +resnet32ts.ra2_in1k,288,9.040,90.960,38.107,61.893,17.96,1.000,bicubic,-84.790,-60.553,-53 +resnext50_32x4d.gluon_in1k,224,9.040,90.960,36.213,63.787,25.03,0.875,bicubic,-84.790,-62.197,-51 +regnety_040.pycls_in1k,224,9.013,90.987,37.147,62.853,20.65,0.875,bicubic,-84.867,-61.563,-62 +vit_base_patch16_224.sam_in1k,224,9.013,90.987,36.053,63.947,86.57,0.900,bicubic,-85.137,-62.577,-111 +resnest26d.gluon_in1k,224,9.000,91.000,37.533,62.467,17.07,0.875,bilinear,-84.390,-60.847,+14 +resnet33ts.ra2_in1k,256,8.973,91.027,38.227,61.773,19.68,0.900,bicubic,-84.637,-60.293,-23 +crossvit_tiny_240.in1k,240,8.960,91.040,34.333,65.667,7.01,0.875,bicubic,-81.270,-63.257,+234 +bat_resnext26ts.ch_in1k,256,8.920,91.080,36.000,64.000,10.73,0.900,bicubic,-84.400,-62.350,+23 +mixnet_l.ft_in1k,224,8.867,91.133,36.253,63.747,7.33,0.875,bicubic,-84.553,-62.007,+6 +seresnext26d_32x4d.bt_in1k,224,8.840,91.160,36.467,63.533,16.81,0.875,bicubic,-83.890,-61.793,+90 +gcresnext26ts.ch_in1k,288,8.813,91.187,36.733,63.267,10.48,1.000,bicubic,-84.377,-61.557,+33 +hgnetv2_b0.ssld_stage1_in22k_in1k,288,8.800,91.200,39.160,60.840,6.00,1.000,bicubic,-84.790,-59.350,-27 +efficientvit_m5.r224_in1k,224,8.800,91.200,34.440,65.560,12.47,0.875,bicubic,-83.640,-63.550,+116 +seresnext26t_32x4d.bt_in1k,224,8.787,91.213,36.560,63.440,16.81,0.875,bicubic,-84.053,-61.790,+68 +rexnet_100.nav_in1k,224,8.787,91.213,36.227,63.773,4.80,0.875,bicubic,-84.243,-62.083,+49 +hgnetv2_b1.ssld_stage1_in22k_in1k,224,8.773,91.227,38.293,61.707,6.34,0.965,bicubic,-84.867,-60.097,-39 +resnet50d.a3_in1k,224,8.773,91.227,36.600,63.400,25.58,0.950,bicubic,-84.747,-61.720,-24 +hrnet_w18.ms_aug_in1k,224,8.707,91.293,38.987,61.013,21.30,0.950,bilinear,-84.803,-59.513,-21 +convit_tiny.fb_in1k,224,8.693,91.307,34.080,65.920,5.71,0.875,bicubic,-81.927,-63.660,+213 +mobilenetv3_large_100.miil_in21k_ft_in1k,224,8.693,91.307,32.467,67.533,5.48,0.875,bilinear,-83.557,-65.183,+125 +hrnet_w30.ms_in1k,224,8.667,91.333,37.027,62.973,37.71,0.875,bilinear,-84.533,-61.383,+22 +resnet50.bt_in1k,288,8.640,91.360,38.413,61.587,25.56,0.950,bicubic,-85.670,-60.227,-170 +levit_128s.fb_dist_in1k,224,8.627,91.373,32.827,67.173,7.78,0.900,bicubic,-83.313,-65.303,+147 +levit_conv_128s.fb_dist_in1k,224,8.627,91.373,32.827,67.173,7.78,0.900,bicubic,-83.313,-65.243,+149 +resnet50.a1h_in1k,176,8.613,91.387,35.533,64.467,25.56,0.900,bicubic,-84.957,-62.977,-35 +ghostnetv2_160.in1k,224,8.587,91.413,36.533,63.467,12.39,0.875,bicubic,-84.423,-61.697,+41 +gcresnext26ts.ch_in1k,256,8.587,91.413,35.533,64.467,10.48,0.900,bicubic,-84.173,-62.697,+67 +hgnetv2_b0.ssld_stage2_ft_in1k,288,8.573,91.427,39.133,60.867,6.00,1.000,bicubic,-85.247,-59.227,-77 +repvit_m1.dist_in1k,224,8.547,91.453,36.800,63.200,5.49,0.950,bicubic,-84.773,-61.620,-1 +eca_resnext26ts.ch_in1k,288,8.547,91.453,36.427,63.573,10.30,1.000,bicubic,-84.553,-61.873,+25 +efficientnet_b1.ft_in1k,224,8.547,91.453,34.147,65.853,7.79,0.875,bicubic,-83.883,-64.133,+102 +resnet32ts.ra2_in1k,256,8.480,91.520,36.933,63.067,17.96,0.900,bicubic,-85.030,-61.667,-32 +dla169.in1k,224,8.453,91.547,35.960,64.040,53.39,0.875,bilinear,-84.877,-62.550,-7 +mixer_b16_224.goog_in21k_ft_in1k,224,8.427,91.573,29.267,70.733,59.88,0.875,bicubic,-83.433,-67.933,+144 +tf_efficientnet_b2.in1k,260,8.413,91.587,36.320,63.680,9.11,0.890,bicubic,-85.717,-62.130,-139 +convnext_atto_ols.a2_in1k,288,8.400,91.600,34.787,65.213,3.70,0.950,bicubic,-84.690,-63.503,+21 +tf_efficientnet_b1.ap_in1k,240,8.387,91.613,35.147,64.853,7.79,0.882,bicubic,-85.333,-63.213,-67 +resnetblur50.bt_in1k,224,8.360,91.640,37.440,62.560,25.56,0.875,bicubic,-85.620,-60.910,-112 +repvgg_b2.rvgg_in1k,224,8.280,91.720,36.027,63.973,89.02,0.875,bilinear,-85.220,-62.473,-37 +legacy_seresnet101.in1k,224,8.240,91.760,35.707,64.293,49.33,0.875,bilinear,-85.070,-62.773,-8 +crossvit_9_240.in1k,240,8.240,91.760,34.120,65.880,8.55,0.875,bicubic,-82.420,-63.580,+186 +ese_vovnet19b_dw.ra_in1k,288,8.213,91.787,37.160,62.840,6.54,0.950,bicubic,-84.957,-61.110,+4 +resnet50.b1k_in1k,224,8.213,91.787,35.133,64.867,25.56,0.950,bicubic,-85.817,-63.487,-126 +dla102x.in1k,224,8.173,91.827,36.760,63.240,26.31,0.875,bilinear,-85.327,-61.980,-41 +eca_resnext26ts.ch_in1k,256,8.120,91.880,35.733,64.267,10.30,0.900,bicubic,-84.510,-62.507,+65 +resmlp_12_224.fb_distilled_in1k,224,8.107,91.893,36.627,63.373,15.35,0.875,bicubic,-84.713,-61.503,+41 +cs3darknet_m.c2ns_in1k,288,7.947,92.053,36.240,63.760,9.31,0.950,bicubic,-85.403,-61.820,-24 +hrnet_w32.ms_in1k,224,7.933,92.067,37.253,62.747,41.23,0.875,bilinear,-85.587,-61.207,-54 +seresnext26ts.ch_in1k,288,7.920,92.080,35.973,64.027,10.39,1.000,bicubic,-85.060,-62.227,+20 +resnet50.b2k_in1k,224,7.907,92.093,35.320,64.680,25.56,0.950,bicubic,-86.183,-63.130,-148 +poolformerv2_s12.sail_in1k,224,7.893,92.107,34.467,65.533,11.89,1.000,bicubic,-85.067,-63.893,+22 +resnet101c.gluon_in1k,224,7.880,92.120,32.920,67.080,44.57,0.875,bicubic,-85.800,-65.520,-79 +repghostnet_200.in1k,224,7.853,92.147,36.747,63.253,9.80,0.875,bicubic,-85.657,-61.813,-54 +resnet26t.ra2_in1k,320,7.853,92.147,36.320,63.680,16.01,1.000,bicubic,-85.327,-62.180,-9 +vit_base_patch32_224.augreg_in1k,224,7.840,92.160,30.293,69.707,88.22,0.900,bicubic,-83.360,-67.087,+148 +resnet50d.gluon_in1k,224,7.813,92.187,34.760,65.240,25.58,0.875,bicubic,-85.977,-63.740,-97 +res2net50_26w_8s.in1k,224,7.813,92.187,33.053,66.947,48.40,0.875,bilinear,-85.587,-65.127,-38 +efficientformerv2_s0.snap_dist_in1k,224,7.800,92.200,32.947,67.053,3.60,0.950,bicubic,-84.170,-64.943,+108 +dla60_res2next.in1k,224,7.787,92.213,34.667,65.333,17.03,0.875,bilinear,-85.393,-63.743,-13 +mobilevitv2_075.cvnets_in1k,256,7.773,92.227,33.400,66.600,2.87,0.888,bicubic,-83.977,-64.460,+124 +convnext_femto_ols.d1_in1k,224,7.747,92.253,36.453,63.547,5.23,0.875,bicubic,-85.323,-61.827,-2 +fastvit_t8.apple_dist_in1k,256,7.747,92.253,34.427,65.573,4.03,0.900,bicubic,-84.793,-63.603,+60 +tf_efficientnetv2_b1.in1k,240,7.733,92.267,34.333,65.667,8.14,0.882,bicubic,-86.227,-64.057,-135 +deit_tiny_distilled_patch16_224.fb_in1k,224,7.720,92.280,33.507,66.493,5.91,0.900,bicubic,-83.040,-64.063,+161 +regnety_032.pycls_in1k,224,7.693,92.307,34.040,65.960,19.44,0.875,bicubic,-85.747,-64.310,-58 +mobilevit_xs.cvnets_in1k,256,7.667,92.333,32.440,67.560,2.32,0.900,bicubic,-83.183,-65.500,+155 +densenetblur121d.ra_in1k,224,7.653,92.347,34.347,65.653,8.00,0.875,bicubic,-84.277,-63.633,+106 +convnextv2_atto.fcmae_ft_in1k,288,7.640,92.360,32.680,67.320,3.71,0.950,bicubic,-85.330,-65.460,+4 +convnext_atto.d2_in1k,288,7.547,92.453,34.773,65.227,3.70,0.950,bicubic,-85.223,-63.277,+24 +tf_efficientnetv2_b2.in1k,208,7.547,92.453,32.973,67.027,10.10,0.890,bicubic,-86.273,-65.787,-117 +resnext50_32x4d.tv2_in1k,176,7.533,92.467,33.680,66.320,25.03,0.875,bilinear,-86.087,-64.620,-89 +dla60_res2net.in1k,224,7.507,92.493,34.587,65.413,20.85,0.875,bilinear,-85.643,-63.823,-24 +resnet50.a2_in1k,224,7.507,92.493,33.240,66.760,25.56,0.950,bicubic,-86.343,-65.100,-127 +resnet152.a3_in1k,160,7.400,92.600,31.573,68.427,60.19,0.950,bicubic,-86.230,-67.027,-93 +regnetx_064.pycls_in1k,224,7.387,92.613,34.320,65.680,26.21,0.875,bicubic,-86.513,-64.310,-137 +hardcorenas_e.miil_green_in1k,224,7.373,92.627,33.360,66.640,8.07,0.875,bilinear,-85.197,-64.770,+41 +resnet50.ra_in1k,224,7.320,92.680,34.867,65.133,25.56,0.875,bicubic,-86.030,-63.753,-54 +wide_resnet101_2.tv_in1k,224,7.320,92.680,33.733,66.267,126.89,0.875,bilinear,-86.440,-64.787,-115 +resnet34.a2_in1k,288,7.320,92.680,31.800,68.200,21.80,1.000,bicubic,-85.430,-66.450,+21 +efficientnet_b1_pruned.in1k,240,7.267,92.733,34.373,65.627,6.33,0.882,bicubic,-85.523,-63.677,+9 +regnetx_008.tv2_in1k,224,7.227,92.773,34.173,65.827,7.26,0.965,bicubic,-85.363,-64.007,+32 +efficientnet_b0.ra_in1k,224,7.227,92.773,33.933,66.067,5.29,0.875,bicubic,-85.443,-64.547,+26 +resnet101.gluon_in1k,224,7.213,92.787,32.720,67.280,44.55,0.875,bicubic,-86.517,-65.680,-114 +deit_tiny_patch16_224.fb_in1k,224,7.213,92.787,30.627,69.373,5.72,0.900,bicubic,-82.477,-66.873,+170 +densenet121.ra_in1k,288,7.200,92.800,35.307,64.693,7.98,0.950,bicubic,-85.350,-62.883,+34 +tf_efficientnet_cc_b0_8e.in1k,224,7.187,92.813,31.973,68.027,24.01,0.875,bicubic,-85.643,-66.277,-2 +tf_efficientnet_b1.aa_in1k,240,7.160,92.840,33.027,66.973,7.79,0.882,bicubic,-86.350,-65.503,-86 +resnet50s.gluon_in1k,224,7.120,92.880,33.133,66.867,25.68,0.875,bicubic,-86.550,-65.327,-114 +edgenext_x_small.in1k,288,7.107,92.893,30.587,69.413,2.34,1.000,bicubic,-84.633,-67.003,+96 +tf_mixnet_l.in1k,224,7.080,92.920,31.573,68.427,7.33,0.875,bicubic,-86.210,-66.457,-55 +resmlp_12_224.fb_in1k,224,7.067,92.933,33.867,66.133,15.35,0.875,bicubic,-85.143,-64.283,+56 +convmixer_1024_20_ks9_p14.in1k,224,7.013,92.987,32.720,67.280,24.38,0.960,bicubic,-85.417,-65.100,+38 +seresnext26ts.ch_in1k,256,6.987,93.013,34.693,65.307,10.39,0.900,bicubic,-85.733,-63.487,+8 +hardcorenas_f.miil_green_in1k,224,6.973,93.027,33.947,66.053,8.20,0.875,bilinear,-85.997,-64.113,-23 +cs3darknet_focus_m.c2ns_in1k,288,6.960,93.040,34.387,65.613,9.30,0.950,bicubic,-86.030,-63.973,-27 +pit_ti_distilled_224.in1k,224,6.933,93.067,30.800,69.200,5.10,0.900,bicubic,-83.837,-66.850,+128 +hgnetv2_b0.ssld_stage1_in22k_in1k,224,6.827,93.173,33.587,66.413,6.00,0.965,bicubic,-85.643,-64.833,+29 +fastvit_t8.apple_in1k,256,6.800,93.200,33.453,66.547,4.03,0.900,bicubic,-85.360,-64.567,+53 +convnext_femto.d1_in1k,224,6.787,93.213,35.093,64.907,5.22,0.875,bicubic,-86.143,-63.157,-23 +convnextv2_atto.fcmae_ft_in1k,224,6.773,93.227,30.827,69.173,3.71,0.875,bicubic,-85.357,-66.923,+53 +mixnet_m.ft_in1k,224,6.707,93.293,32.040,67.960,5.01,0.875,bicubic,-85.713,-65.830,+31 +selecsls60b.in1k,224,6.653,93.347,33.053,66.947,32.77,0.875,bicubic,-86.677,-65.327,-76 +tinynet_a.in1k,192,6.653,93.347,32.707,67.293,6.19,0.875,bicubic,-85.827,-65.333,+22 +dpn68b.mx_in1k,224,6.653,93.347,32.600,67.400,12.61,0.875,bicubic,-86.167,-65.840,-18 +ghostnetv2_130.in1k,224,6.653,93.347,32.600,67.400,8.96,0.875,bicubic,-85.667,-65.470,+36 +efficientnet_es.ra_in1k,224,6.627,93.373,34.333,65.667,5.44,0.875,bicubic,-86.573,-64.077,-64 +ese_vovnet19b_dw.ra_in1k,224,6.613,93.387,33.173,66.827,6.54,0.875,bicubic,-85.667,-64.817,+35 +mobileone_s3.apple_in1k,224,6.600,93.400,32.000,68.000,10.17,0.900,bilinear,-86.310,-66.180,-30 +poolformer_s12.sail_in1k,224,6.560,93.440,34.227,65.773,11.92,0.900,bicubic,-86.090,-63.953,-1 +res2net50_26w_6s.in1k,224,6.547,93.453,31.493,68.507,37.05,0.875,bilinear,-86.873,-67.107,-96 +hardcorenas_d.miil_green_in1k,224,6.533,93.467,32.133,67.867,7.50,0.875,bilinear,-85.927,-65.897,+16 +legacy_seresnext26_32x4d.in1k,224,6.520,93.480,33.027,66.973,16.79,0.875,bicubic,-86.120,-65.083,-3 +dla60x.in1k,224,6.453,93.547,33.973,66.027,17.35,0.875,bilinear,-86.647,-64.457,-59 +tf_efficientnet_b1.in1k,240,6.440,93.560,32.133,67.867,7.79,0.882,bicubic,-86.650,-66.337,-56 +cs3darknet_m.c2ns_in1k,256,6.427,93.573,33.467,66.533,9.31,0.887,bicubic,-86.243,-64.613,-10 +repghostnet_150.in1k,224,6.427,93.573,32.133,67.867,6.58,0.875,bicubic,-85.943,-66.177,+22 +skresnet34.ra_in1k,224,6.413,93.587,31.493,68.507,22.28,0.875,bicubic,-85.967,-66.647,+18 +regnetx_080.pycls_in1k,224,6.400,93.600,32.307,67.693,39.57,0.875,bicubic,-87.450,-66.213,-173 +resnet34d.ra2_in1k,224,6.373,93.627,31.640,68.360,21.82,0.875,bicubic,-86.357,-66.530,-20 +hgnetv2_b0.ssld_stage2_ft_in1k,224,6.360,93.640,33.253,66.747,6.00,0.965,bicubic,-86.460,-64.897,-35 +regnety_004.tv2_in1k,224,6.347,93.653,30.413,69.587,4.34,0.965,bicubic,-85.273,-67.667,+69 +resnet50.a3_in1k,224,6.333,93.667,31.547,68.453,25.56,0.950,bicubic,-86.437,-66.623,-30 +repvgg_b1.rvgg_in1k,224,6.320,93.680,33.613,66.387,57.42,0.875,bilinear,-87.010,-64.657,-96 +ecaresnet50t.a3_in1k,160,6.280,93.720,30.360,69.640,25.57,0.950,bicubic,-86.790,-68.040,-63 +resnet18.fb_swsl_ig1b_ft_in1k,224,6.227,93.773,31.280,68.720,11.69,0.875,bilinear,-84.433,-66.460,+103 +resnet101.a3_in1k,160,6.200,93.800,28.880,71.120,44.55,0.950,bicubic,-86.840,-69.270,-64 +edgenext_x_small.in1k,256,6.160,93.840,29.627,70.373,2.34,0.900,bicubic,-84.980,-67.923,+82 +resnet26d.bt_in1k,288,6.147,93.853,32.853,67.147,16.01,0.950,bicubic,-86.343,-65.367,-5 +pit_ti_224.in1k,224,6.093,93.907,30.040,69.960,4.85,0.900,bicubic,-83.857,-67.400,+118 +legacy_seresnet50.in1k,224,6.067,93.933,32.387,67.613,28.09,0.875,bilinear,-86.913,-66.023,-60 +resnet152.tv_in1k,224,6.053,93.947,31.467,68.533,60.19,0.875,bilinear,-87.277,-67.133,-103 +tf_efficientnet_cc_b0_4e.in1k,224,6.013,93.987,29.520,70.480,13.31,0.875,bicubic,-86.617,-68.570,-20 +resnet26t.ra2_in1k,256,5.960,94.040,31.720,68.280,16.01,0.940,bicubic,-86.790,-66.270,-37 +mobilenetv1_125.ra4_e3600_r224_in1k,256,5.960,94.040,30.400,69.600,6.27,1.000,bicubic,-86.720,-67.820,-28 +mixer_l16_224.goog_in21k_ft_in1k,224,5.920,94.080,18.200,81.800,208.20,0.875,bicubic,-81.260,-75.320,+156 +tf_efficientnetv2_b0.in1k,224,5.907,94.093,30.787,69.213,7.14,0.875,bicubic,-87.223,-67.573,-84 +wide_resnet50_2.tv_in1k,224,5.880,94.120,31.867,68.133,68.88,0.875,bilinear,-87.330,-66.453,-96 +dla102.in1k,224,5.853,94.147,32.733,67.267,33.27,0.875,bilinear,-87.227,-65.807,-79 +resnet50.ram_in1k,224,5.800,94.200,29.080,70.920,25.56,0.875,bicubic,-88.010,-69.500,-183 +regnetx_040.pycls_in1k,224,5.787,94.213,31.213,68.787,22.12,0.875,bicubic,-87.763,-67.357,-149 +resnetrs50.tf_in1k,160,5.787,94.213,27.653,72.347,35.69,0.910,bicubic,-87.143,-70.607,-63 +convnext_atto_ols.a2_in1k,224,5.760,94.240,29.453,70.547,3.70,0.875,bicubic,-86.170,-68.647,+32 +selecsls60.in1k,224,5.747,94.253,32.347,67.653,30.67,0.875,bicubic,-87.283,-65.843,-80 +resnet50.bt_in1k,224,5.680,94.320,31.893,68.107,25.56,0.875,bicubic,-87.580,-66.377,-107 +regnety_016.pycls_in1k,224,5.600,94.400,30.493,69.507,11.20,0.875,bicubic,-87.540,-67.847,-95 +hardcorenas_c.miil_green_in1k,224,5.600,94.400,30.133,69.867,5.52,0.875,bilinear,-86.440,-67.707,+16 +hrnet_w18_small_v2.gluon_in1k,224,5.587,94.413,31.853,68.147,15.60,0.875,bicubic,-87.183,-66.557,-56 +res2next50.in1k,224,5.533,94.467,30.640,69.360,24.67,0.875,bilinear,-87.367,-67.500,-69 +resnext50_32x4d.a3_in1k,160,5.520,94.480,27.853,72.147,25.03,0.950,bicubic,-87.170,-70.137,-46 +seresnet50.a3_in1k,224,5.507,94.493,30.213,69.787,28.09,0.950,bicubic,-86.593,-67.827,+6 +cs3darknet_focus_m.c2ns_in1k,256,5.467,94.533,31.867,68.133,9.30,0.887,bicubic,-86.913,-66.513,-17 +resnet34.a1_in1k,224,5.467,94.533,27.600,72.400,21.80,0.950,bicubic,-86.643,-70.180,+3 +ghostnetv2_100.in1k,224,5.453,94.547,28.827,71.173,6.16,0.875,bicubic,-85.407,-68.873,+67 +hrnet_w18.ms_in1k,224,5.427,94.573,30.827,69.173,21.30,0.875,bilinear,-86.943,-67.233,-18 +tf_efficientnet_lite2.in1k,260,5.373,94.627,30.987,69.013,6.09,0.890,bicubic,-87.307,-67.253,-51 +resnest14d.gluon_in1k,224,5.333,94.667,28.560,71.440,10.61,0.875,bilinear,-86.387,-69.610,+32 +tf_efficientnet_b0.ap_in1k,224,5.307,94.693,28.853,71.147,5.29,0.875,bicubic,-86.953,-69.167,-13 +resnext26ts.ra2_in1k,288,5.293,94.707,29.400,70.600,10.30,1.000,bicubic,-86.867,-68.490,-8 +efficientvit_m4.r224_in1k,224,5.280,94.720,27.773,72.227,8.80,0.875,bicubic,-85.320,-69.817,+74 +tf_efficientnetv2_b1.in1k,192,5.267,94.733,28.320,71.680,8.14,0.882,bicubic,-87.873,-70.070,-109 +gernet_s.idstcv_in1k,224,5.253,94.747,30.013,69.987,8.17,0.875,bilinear,-86.897,-68.197,-9 +tf_efficientnet_em.in1k,240,5.213,94.787,30.653,69.347,6.90,0.882,bicubic,-87.737,-67.557,-88 +resnet34.bt_in1k,288,5.200,94.800,29.307,70.693,21.80,0.950,bicubic,-87.210,-68.843,-31 +xcit_nano_12_p16_224.fb_dist_in1k,224,5.200,94.800,26.693,73.307,3.05,1.000,bicubic,-84.460,-70.397,+94 +tf_efficientnet_b0.aa_in1k,224,5.187,94.813,29.053,70.947,5.29,0.875,bicubic,-87.093,-69.047,-22 +efficientvit_m3.r224_in1k,224,5.187,94.813,27.453,72.547,6.90,0.875,bicubic,-84.693,-70.087,+85 +densenet121.ra_in1k,224,5.173,94.827,29.533,70.467,7.98,0.875,bicubic,-86.447,-68.377,+22 +repvgg_b1g4.rvgg_in1k,224,5.147,94.853,30.467,69.533,39.97,0.875,bilinear,-87.863,-67.943,-102 +convnext_atto.d2_in1k,224,5.120,94.880,28.880,71.120,3.70,0.875,bicubic,-86.650,-69.160,+14 +mobilenetv1_100h.ra4_e3600_r224_in1k,256,5.093,94.907,28.480,71.520,5.28,0.950,bicubic,-87.137,-69.520,-24 +res2net50_26w_4s.in1k,224,5.067,94.933,29.040,70.960,25.70,0.875,bilinear,-87.463,-69.030,-50 +mobilenetv3_large_100.ra_in1k,224,5.067,94.933,28.053,71.947,5.48,0.875,bicubic,-86.273,-69.647,+28 +tf_mixnet_m.in1k,224,5.040,94.960,28.320,71.680,5.01,0.875,bicubic,-87.300,-69.560,-35 +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,224,4.973,95.027,26.813,73.187,6.34,0.900,bicubic,-84.257,-70.407,+89 +hardcorenas_a.miil_green_in1k,224,4.960,95.040,27.947,72.053,5.26,0.875,bilinear,-86.400,-69.893,+21 +mixnet_s.ft_in1k,224,4.920,95.080,28.840,71.160,4.13,0.875,bicubic,-87.030,-68.840,-8 +mobilenetv1_125.ra4_e3600_r224_in1k,224,4.920,95.080,28.053,71.947,6.27,0.900,bicubic,-87.030,-69.977,-10 +regnetx_004_tv.tv2_in1k,224,4.920,95.080,27.467,72.533,5.50,0.965,bicubic,-85.710,-70.133,+55 +res2net50_14w_8s.in1k,224,4.893,95.107,28.347,71.653,25.06,0.875,bilinear,-87.827,-69.953,-77 +regnetx_032.pycls_in1k,224,4.880,95.120,29.907,70.093,15.30,0.875,bicubic,-88.260,-68.193,-131 +repghostnet_130.in1k,224,4.840,95.160,29.480,70.520,5.48,0.875,bicubic,-87.040,-68.450,-4 +tf_efficientnetv2_b0.in1k,192,4.827,95.173,26.613,73.387,7.14,0.875,bicubic,-87.463,-71.587,-41 +mobilenetv3_rw.rmsp_in1k,224,4.813,95.187,29.520,70.480,5.48,0.875,bicubic,-86.507,-68.110,+17 +hardcorenas_b.miil_green_in1k,224,4.813,95.187,27.760,72.240,5.18,0.875,bilinear,-87.027,-70.170,-4 +resnet50c.gluon_in1k,224,4.800,95.200,27.760,72.240,25.58,0.875,bicubic,-88.220,-70.600,-120 +xcit_nano_12_p16_224.fb_in1k,224,4.800,95.200,25.253,74.747,3.05,1.000,bicubic,-83.800,-71.577,+89 +resnext26ts.ra2_in1k,256,4.693,95.307,28.800,71.200,10.30,0.900,bicubic,-87.147,-68.970,-9 +mobilenetv4_conv_small.e2400_r224_in1k,256,4.693,95.307,26.987,73.013,3.77,0.950,bicubic,-86.327,-70.683,+25 +resnext50_32x4d.tv_in1k,224,4.680,95.320,29.840,70.160,25.03,0.875,bilinear,-88.070,-68.420,-93 +selecsls42b.in1k,224,4.667,95.333,28.373,71.627,32.46,0.875,bicubic,-87.633,-69.767,-50 +densenet161.tv_in1k,224,4.627,95.373,29.453,70.547,28.68,0.875,bicubic,-87.953,-68.837,-76 +mobilenetv1_100.ra4_e3600_r224_in1k,256,4.600,95.400,27.067,72.933,4.23,0.950,bicubic,-87.490,-70.753,-34 +resnet101.tv_in1k,224,4.560,95.440,29.013,70.987,44.55,0.875,bilinear,-88.250,-69.217,-105 +mobileone_s2.apple_in1k,224,4.480,95.520,28.867,71.133,7.88,0.900,bilinear,-88.350,-69.303,-112 +tf_efficientnet_lite1.in1k,240,4.480,95.520,28.320,71.680,5.42,0.882,bicubic,-88.120,-69.720,-81 +mobilenetv2_120d.ra_in1k,224,4.467,95.533,29.107,70.893,5.83,0.875,bicubic,-88.013,-68.973,-71 +tf_efficientnet_b0.in1k,224,4.413,95.587,26.760,73.240,5.29,0.875,bicubic,-87.677,-71.160,-40 +resnet34.a2_in1k,224,4.387,95.613,24.400,75.600,21.80,0.950,bicubic,-87.343,-73.370,-12 +resnet50d.a3_in1k,160,4.373,95.627,26.160,73.840,25.58,0.950,bicubic,-88.067,-71.890,-71 +pvt_v2_b0.in1k,224,4.347,95.653,25.707,74.293,3.67,0.900,bicubic,-84.433,-71.163,+73 +vit_base_patch32_224.sam_in1k,224,4.307,95.693,24.307,75.693,88.22,0.900,bicubic,-85.443,-72.673,+51 +tinynet_b.in1k,188,4.253,95.747,26.800,73.200,3.73,0.875,bicubic,-86.657,-70.870,+16 +mobilenetv4_conv_small.e1200_r224_in1k,256,4.240,95.760,26.133,73.867,3.77,0.950,bicubic,-86.400,-71.377,+27 +mobilenetv1_100h.ra4_e3600_r224_in1k,224,4.227,95.773,25.760,74.240,5.28,0.875,bicubic,-87.153,-72.050,-9 +repghostnet_111.in1k,224,4.213,95.787,26.307,73.693,4.54,0.875,bicubic,-86.537,-71.153,+21 +fbnetc_100.rmsp_in1k,224,4.213,95.787,25.880,74.120,5.57,0.875,bilinear,-86.567,-71.340,+16 +edgenext_xx_small.in1k,288,4.187,95.813,23.880,76.120,1.33,1.000,bicubic,-84.683,-72.800,+64 +mobilenetv1_100.ra4_e3600_r224_in1k,224,4.173,95.827,25.160,74.840,4.23,0.875,bicubic,-87.007,-72.610,-3 +resnet50.am_in1k,224,4.133,95.867,28.360,71.640,25.56,0.875,bicubic,-89.857,-70.160,-284 +efficientnet_es_pruned.in1k,224,4.120,95.880,26.800,73.200,5.44,0.875,bicubic,-87.070,-70.940,-6 +resnet50.gluon_in1k,224,4.053,95.947,26.800,73.200,25.56,0.875,bicubic,-88.497,-71.410,-91 +resnet26d.bt_in1k,224,4.040,95.960,28.360,71.640,16.01,0.875,bicubic,-88.010,-69.600,-51 +densenet201.tv_in1k,224,3.987,96.013,27.267,72.733,20.01,0.875,bicubic,-88.773,-71.013,-118 +tf_mixnet_s.in1k,224,3.947,96.053,25.280,74.720,4.13,0.875,bicubic,-87.573,-72.310,-23 +semnasnet_100.rmsp_in1k,224,3.933,96.067,26.880,73.120,3.89,0.875,bicubic,-87.427,-70.660,-18 +mobilevitv2_050.cvnets_in1k,256,3.933,96.067,23.667,76.333,1.37,0.888,bicubic,-84.317,-73.303,+68 +resnet26.bt_in1k,288,3.907,96.093,28.133,71.867,16.00,0.950,bicubic,-88.123,-70.067,-55 +dpn68.mx_in1k,224,3.907,96.093,25.587,74.413,12.61,0.875,bicubic,-88.083,-72.423,-53 +repvgg_a2.rvgg_in1k,224,3.853,96.147,27.307,72.693,28.21,0.875,bilinear,-88.087,-70.763,-49 +tf_efficientnet_es.in1k,224,3.840,96.160,26.093,73.907,5.44,0.875,bicubic,-88.140,-71.767,-55 +semnasnet_075.rmsp_in1k,224,3.813,96.187,26.600,73.400,2.91,0.875,bicubic,-86.277,-70.810,+22 +resnet18.a1_in1k,288,3.813,96.187,23.000,77.000,11.69,1.000,bicubic,-85.907,-74.100,+32 +mobilevit_xxs.cvnets_in1k,256,3.800,96.200,22.133,77.867,1.27,0.900,bicubic,-83.360,-73.967,+69 +edgenext_xx_small.in1k,256,3.760,96.240,23.480,76.520,1.33,0.900,bicubic,-84.610,-73.220,+56 +regnety_008.pycls_in1k,224,3.747,96.253,26.867,73.133,6.26,0.875,bicubic,-87.973,-71.003,-40 +resnet18d.ra2_in1k,288,3.747,96.253,25.760,74.240,11.71,0.950,bicubic,-86.563,-71.800,+14 +resnet18.fb_ssl_yfcc100m_ft_in1k,224,3.747,96.253,25.293,74.707,11.69,0.875,bilinear,-86.503,-72.267,+14 +densenet169.tv_in1k,224,3.720,96.280,25.573,74.427,14.15,0.875,bicubic,-88.240,-72.517,-62 +mobilenetv4_conv_small.e1200_r224_in1k,224,3.720,96.280,24.467,75.533,3.77,0.875,bicubic,-86.200,-72.703,+21 +mobilenetv2_140.ra_in1k,224,3.653,96.347,26.467,73.533,6.11,0.875,bicubic,-88.177,-71.413,-51 +tf_mobilenetv3_large_100.in1k,224,3.653,96.347,24.987,75.013,5.48,0.875,bilinear,-87.567,-72.673,-30 +mobilenetv4_conv_small.e2400_r224_in1k,224,3.653,96.347,24.413,75.587,3.77,0.875,bicubic,-86.417,-72.867,+16 +dla60.in1k,224,3.640,96.360,28.013,71.987,22.04,0.875,bilinear,-88.560,-70.087,-85 +res2net50_48w_2s.in1k,224,3.600,96.400,26.187,73.813,25.29,0.875,bilinear,-88.960,-71.883,-116 +efficientvit_m2.r224_in1k,224,3.560,96.440,21.627,78.373,4.19,0.875,bicubic,-84.900,-75.283,+42 +efficientnet_lite0.ra_in1k,224,3.547,96.453,26.320,73.680,4.65,0.875,bicubic,-87.593,-71.320,-29 +repghostnet_100.in1k,224,3.547,96.453,24.293,75.707,4.07,0.875,bicubic,-86.773,-73.197,+1 +regnetx_016.pycls_in1k,224,3.533,96.467,26.347,73.653,9.19,0.875,bicubic,-88.647,-71.853,-92 +regnety_006.pycls_in1k,224,3.533,96.467,25.107,74.893,6.06,0.875,bicubic,-87.907,-72.653,-48 +spnasnet_100.rmsp_in1k,224,3.533,96.467,24.387,75.613,4.42,0.875,bilinear,-86.787,-72.803,0 +seresnet50.a3_in1k,160,3.533,96.467,22.627,77.373,28.09,0.950,bicubic,-87.467,-74.933,-25 +ghostnet_100.in1k,224,3.400,96.600,25.187,74.813,5.18,0.875,bicubic,-86.800,-72.063,-1 +resnet34.a3_in1k,224,3.400,96.600,23.240,76.760,21.80,0.950,bicubic,-86.520,-74.000,+8 +resnet50.a3_in1k,160,3.373,96.627,23.600,76.400,25.56,0.950,bicubic,-88.057,-74.030,-51 +legacy_seresnet34.in1k,224,3.320,96.680,23.707,76.293,21.96,0.875,bilinear,-87.600,-73.863,-28 +resnet18.a2_in1k,288,3.307,96.693,22.253,77.747,11.69,1.000,bicubic,-86.273,-74.697,+13 +resnet34.bt_in1k,224,3.253,96.747,24.413,75.587,21.80,0.875,bicubic,-87.917,-73.157,-40 +dla34.in1k,224,3.240,96.760,23.667,76.333,15.74,0.875,bilinear,-87.530,-73.983,-25 +efficientvit_b0.r224_in1k,224,3.227,96.773,19.427,80.573,3.41,0.950,bicubic,-84.753,-76.653,+36 +tinynet_c.in1k,184,3.213,96.787,21.413,78.587,2.46,0.875,bicubic,-84.577,-74.957,+37 +mobilenetv2_110d.ra_in1k,224,3.133,96.867,24.787,75.213,4.52,0.875,bicubic,-87.867,-72.623,-38 +mnasnet_100.rmsp_in1k,224,3.133,96.867,24.307,75.693,4.38,0.875,bicubic,-87.517,-73.203,-23 +regnety_004.pycls_in1k,224,3.080,96.920,22.573,77.427,4.34,0.875,bicubic,-87.430,-75.007,-18 +tf_efficientnet_lite0.in1k,224,3.067,96.933,22.733,77.267,4.65,0.875,bicubic,-88.033,-74.977,-43 +mobileone_s1.apple_in1k,224,3.040,96.960,25.027,74.973,4.83,0.900,bilinear,-88.320,-72.743,-58 +skresnet18.ra_in1k,224,3.027,96.973,22.680,77.320,11.96,0.875,bicubic,-86.633,-74.550,-2 +repghostnet_080.in1k,224,3.027,96.973,22.627,77.373,3.28,0.875,bicubic,-85.943,-74.143,+11 +efficientvit_m1.r224_in1k,224,2.907,97.093,19.573,80.427,2.98,0.875,bicubic,-83.903,-76.427,+36 +tf_mobilenetv3_large_075.in1k,224,2.893,97.107,21.707,78.293,3.99,0.875,bilinear,-86.767,-75.533,-3 +vgg19_bn.tv_in1k,224,2.840,97.160,23.467,76.533,143.68,0.875,bilinear,-87.280,-74.113,-17 +tinynet_d.in1k,152,2.787,97.213,17.773,82.227,2.34,0.875,bicubic,-82.043,-77.447,+44 +regnetx_008.pycls_in1k,224,2.733,97.267,22.387,77.613,7.26,0.875,bicubic,-88.367,-75.153,-52 +resnet14t.c3_in1k,224,2.720,97.280,19.827,80.173,10.08,0.950,bicubic,-86.280,-76.873,+3 +lcnet_100.ra2_in1k,224,2.653,97.347,20.467,79.533,2.95,0.875,bicubic,-86.107,-76.243,+10 +resnet34.gluon_in1k,224,2.640,97.360,21.533,78.467,21.80,0.875,bicubic,-88.330,-76.117,-50 +hrnet_w18_small_v2.ms_in1k,224,2.627,97.373,23.533,76.467,15.60,0.875,bilinear,-88.583,-74.367,-64 +repvgg_b0.rvgg_in1k,224,2.600,97.400,23.907,76.093,15.82,0.875,bilinear,-88.810,-74.073,-74 +hrnet_w18_small.gluon_in1k,224,2.600,97.400,20.520,79.480,13.19,0.875,bicubic,-86.880,-76.530,-7 +vgg16.tv_in1k,224,2.573,97.427,19.960,80.040,138.36,0.875,bilinear,-85.977,-76.830,+7 +vgg16_bn.tv_in1k,224,2.560,97.440,23.587,76.413,138.37,0.875,bilinear,-87.530,-73.783,-25 +resnet18d.ra2_in1k,224,2.480,97.520,21.547,78.453,11.71,0.875,bicubic,-86.850,-75.633,-10 +densenet121.tv_in1k,224,2.467,97.533,22.533,77.467,7.98,0.875,bicubic,-88.433,-75.197,-55 +regnetx_006.pycls_in1k,224,2.467,97.533,20.640,79.360,6.20,0.875,bicubic,-87.903,-76.780,-36 +legacy_seresnet18.in1k,224,2.467,97.533,19.880,80.120,11.78,0.875,bicubic,-86.423,-77.090,-3 +resnet18.a1_in1k,224,2.440,97.560,18.467,81.533,11.69,0.950,bicubic,-86.000,-78.183,+3 +resnet26.bt_in1k,224,2.400,97.600,22.840,77.160,16.00,0.875,bicubic,-88.780,-74.960,-70 +lcnet_075.ra2_in1k,224,2.280,97.720,17.133,82.867,2.36,0.875,bicubic,-83.760,-78.547,+24 +efficientvit_m0.r224_in1k,224,2.267,97.733,16.453,83.547,2.35,0.875,bicubic,-80.133,-77.967,+36 +repghostnet_058.in1k,224,2.253,97.747,18.427,81.573,2.55,0.875,bicubic,-84.257,-77.473,+15 +resnet34.a3_in1k,160,2.253,97.747,18.280,81.720,21.80,0.950,bicubic,-85.647,-78.190,+7 +resnet18.a3_in1k,224,2.253,97.747,17.507,82.493,11.69,0.950,bicubic,-84.177,-78.393,+17 +mobilenetv3_small_075.lamb_in1k,224,2.227,97.773,15.707,84.293,2.04,0.875,bicubic,-80.883,-78.443,+30 +test_vit.r160_in1k,160,2.227,97.773,12.733,87.267,0.37,0.875,bicubic,-60.233,-71.807,+39 +mobilenetv2_100.ra_in1k,224,2.187,97.813,19.733,80.267,3.50,0.875,bicubic,-87.443,-77.407,-26 +test_efficientnet.r160_in1k,160,2.173,97.827,11.200,88.800,0.36,0.875,bicubic,-64.937,-74.900,+34 +repvgg_a1.rvgg_in1k,224,2.147,97.853,21.227,78.773,14.09,0.875,bilinear,-88.443,-76.413,-53 +tf_mobilenetv3_small_100.in1k,224,2.147,97.853,16.773,83.227,2.54,0.875,bilinear,-83.073,-78.997,+16 +regnety_002.pycls_in1k,224,2.133,97.867,18.640,81.360,3.16,0.875,bicubic,-85.217,-77.960,+2 +resnet14t.c3_in1k,176,2.133,97.867,16.960,83.040,10.08,0.875,bicubic,-86.157,-79.410,-7 +vgg19.tv_in1k,224,2.107,97.893,20.480,79.520,143.67,0.875,bilinear,-86.953,-76.420,-26 +mobileone_s0.apple_in1k,224,2.107,97.893,17.360,82.640,5.29,0.875,bilinear,-86.213,-79.070,-10 +vgg13_bn.tv_in1k,224,2.080,97.920,20.040,79.960,133.05,0.875,bilinear,-86.700,-76.930,-20 +test_byobnet.r160_in1k,160,2.053,97.947,11.547,88.453,0.46,0.875,bicubic,-63.817,-74.723,+27 +regnetx_004.pycls_in1k,224,1.973,98.027,19.080,80.920,5.16,0.875,bicubic,-86.947,-78.030,-25 +resnet18.a2_in1k,224,1.973,98.027,17.467,82.533,11.69,0.950,bicubic,-86.057,-78.953,-11 +tf_mobilenetv3_small_075.in1k,224,1.973,98.027,14.840,85.160,2.04,0.875,bilinear,-81.547,-80.020,+16 +mobilenetv3_small_100.lamb_in1k,224,1.933,98.067,16.893,83.107,2.54,0.875,bicubic,-83.257,-78.727,+6 +tinynet_e.in1k,106,1.933,98.067,13.973,86.027,2.04,0.875,bicubic,-77.057,-78.587,+19 +repghostnet_050.in1k,224,1.920,98.080,16.347,83.653,2.31,0.875,bicubic,-83.120,-78.873,+5 +vgg13.tv_in1k,224,1.853,98.147,17.947,82.053,133.05,0.875,bilinear,-85.167,-78.363,-7 +resnet34.tv_in1k,224,1.840,98.160,19.813,80.187,21.80,0.875,bilinear,-88.090,-77.527,-53 +lcnet_050.ra2_in1k,224,1.787,98.213,13.813,86.187,1.88,0.875,bicubic,-79.933,-79.917,+13 +mobilenetv3_small_050.lamb_in1k,224,1.773,98.227,12.547,87.453,1.59,0.875,bicubic,-75.207,-78.763,+15 +mnasnet_small.lamb_in1k,224,1.760,98.240,15.040,84.960,2.03,0.875,bicubic,-82.610,-80.130,+3 +vgg11_bn.tv_in1k,224,1.733,98.267,18.107,81.893,132.87,0.875,bilinear,-85.767,-78.703,-17 +dla46x_c.in1k,224,1.707,98.293,16.333,83.667,1.07,0.875,bilinear,-82.643,-78.907,+2 +tf_mobilenetv3_large_minimal_100.in1k,224,1.653,98.347,17.347,82.653,3.92,0.875,bilinear,-87.287,-79.533,-39 +resnet10t.c3_in1k,224,1.640,98.360,15.907,84.093,5.44,0.950,bicubic,-84.610,-79.813,-9 +mobilenetv2_050.lamb_in1k,224,1.640,98.360,14.053,85.947,1.97,0.875,bicubic,-82.300,-80.537,+1 +vgg11.tv_in1k,224,1.613,98.387,15.907,84.093,132.86,0.875,bilinear,-84.967,-80.403,-15 +resnet18.gluon_in1k,224,1.600,98.400,16.773,83.227,11.69,0.875,bicubic,-86.770,-79.757,-32 +dla60x_c.in1k,224,1.587,98.413,17.773,82.227,1.32,0.875,bilinear,-84.743,-78.317,-14 +hrnet_w18_small.ms_in1k,224,1.520,98.480,18.147,81.853,13.19,0.875,bilinear,-87.530,-78.933,-48 +repvgg_a0.rvgg_in1k,224,1.493,98.507,17.453,82.547,9.11,0.875,bilinear,-87.767,-79.477,-53 +dla46_c.in1k,224,1.493,98.507,15.120,84.880,1.30,0.875,bilinear,-82.127,-79.800,-3 +resnet10t.c3_in1k,176,1.413,98.587,14.147,85.853,5.44,0.875,bicubic,-83.367,-80.863,-10 +resnet18.a3_in1k,160,1.387,98.613,13.707,86.293,11.69,0.950,bicubic,-82.883,-80.923,-8 +regnetx_002.pycls_in1k,224,1.293,98.707,14.907,85.093,2.68,0.875,bicubic,-84.907,-81.063,-18 +tf_mobilenetv3_small_minimal_100.in1k,224,1.133,98.867,11.413,88.587,2.04,0.875,bilinear,-80.187,-82.287,-3 +resnet18.tv_in1k,224,1.120,98.880,16.200,83.800,11.69,0.875,bilinear,-86.260,-80.060,-31 +resnet50.tv_in1k,224,0.040,99.960,14.360,85.640,25.56,0.875,bilinear,-91.850,-83.690,-145 diff --git a/pytorch-image-models/results/results-imagenet-r-clean.csv b/pytorch-image-models/results/results-imagenet-r-clean.csv new file mode 100644 index 0000000000000000000000000000000000000000..e626e1d812e19c8cfff23c2135abac8b5f83b84e --- /dev/null +++ b/pytorch-image-models/results/results-imagenet-r-clean.csv @@ -0,0 +1,1446 @@ +model,img_size,top1,top1_err,top5,top5_err,param_count,crop_pct,interpolation +eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,448,98.160,1.840,99.880,0.120,305.08,1.000,bicubic +eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,448,98.030,1.970,99.890,0.110,305.08,1.000,bicubic +eva_giant_patch14_336.m30m_ft_in22k_in1k,336,98.020,1.980,99.900,0.100,"1,013.01",1.000,bicubic +eva_giant_patch14_560.m30m_ft_in22k_in1k,560,97.990,2.010,99.860,0.140,"1,014.45",1.000,bicubic +eva_large_patch14_336.in22k_ft_in22k_in1k,336,97.900,2.100,99.880,0.120,304.53,1.000,bicubic +convnextv2_huge.fcmae_ft_in22k_in1k_384,384,97.860,2.140,99.910,0.090,660.29,1.000,bicubic +eva02_large_patch14_448.mim_in22k_ft_in1k,448,97.860,2.140,99.800,0.200,305.08,1.000,bicubic +eva_giant_patch14_336.clip_ft_in1k,336,97.860,2.140,99.790,0.210,"1,013.01",1.000,bicubic +eva_large_patch14_336.in22k_ft_in1k,336,97.820,2.180,99.850,0.150,304.53,1.000,bicubic +eva02_large_patch14_448.mim_m38m_ft_in1k,448,97.820,2.180,99.830,0.170,305.08,1.000,bicubic +beit_large_patch16_384.in22k_ft_in22k_in1k,384,97.820,2.180,99.790,0.210,305.00,1.000,bicubic +convnextv2_huge.fcmae_ft_in22k_in1k_512,512,97.810,2.190,99.860,0.140,660.29,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k,800,97.770,2.230,99.890,0.110,480.31,0.960,bicubic +maxvit_base_tf_512.in21k_ft_in1k,512,97.770,2.230,99.850,0.150,119.88,1.000,bicubic +beit_large_patch16_512.in22k_ft_in22k_in1k,512,97.770,2.230,99.810,0.190,305.67,1.000,bicubic +convnext_xxlarge.clip_laion2b_soup_ft_in1k,256,97.770,2.230,99.810,0.190,846.47,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k_475,475,97.770,2.230,99.810,0.190,480.31,0.936,bicubic +regnety_1280.swag_ft_in1k,384,97.760,2.240,99.860,0.140,644.81,1.000,bicubic +maxvit_xlarge_tf_384.in21k_ft_in1k,384,97.760,2.240,99.850,0.150,475.32,1.000,bicubic +maxvit_xlarge_tf_512.in21k_ft_in1k,512,97.760,2.240,99.820,0.180,475.77,1.000,bicubic +beitv2_large_patch16_224.in1k_ft_in22k_in1k,224,97.760,2.240,99.790,0.210,304.43,0.950,bicubic +eva02_base_patch14_448.mim_in22k_ft_in1k,448,97.710,2.290,99.760,0.240,87.12,1.000,bicubic +maxvit_large_tf_384.in21k_ft_in1k,384,97.670,2.330,99.820,0.180,212.03,1.000,bicubic +maxvit_large_tf_512.in21k_ft_in1k,512,97.660,2.340,99.730,0.270,212.33,1.000,bicubic +caformer_b36.sail_in22k_ft_in1k_384,384,97.650,2.350,99.860,0.140,98.75,1.000,bicubic +convnextv2_large.fcmae_ft_in22k_in1k_384,384,97.650,2.350,99.800,0.200,197.96,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in12k_in1k,224,97.640,2.360,99.730,0.270,304.20,1.000,bicubic +eva_large_patch14_196.in22k_ft_in22k_in1k,196,97.620,2.380,99.810,0.190,304.14,1.000,bicubic +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,336,97.620,2.380,99.760,0.240,632.46,1.000,bicubic +vit_large_patch14_clip_336.openai_ft_in12k_in1k,336,97.620,2.380,99.730,0.270,304.53,1.000,bicubic +eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,448,97.600,2.400,99.820,0.180,87.12,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k_384,384,97.580,2.420,99.780,0.220,350.20,1.000,bicubic +deit3_large_patch16_384.fb_in22k_ft_in1k,384,97.580,2.420,99.710,0.290,304.76,1.000,bicubic +maxvit_base_tf_384.in21k_ft_in1k,384,97.570,2.430,99.770,0.230,119.65,1.000,bicubic +eva_giant_patch14_224.clip_ft_in1k,224,97.570,2.430,99.710,0.290,"1,012.56",0.900,bicubic +eva_large_patch14_196.in22k_ft_in1k,196,97.530,2.470,99.790,0.210,304.14,1.000,bicubic +beit_large_patch16_224.in22k_ft_in22k_in1k,224,97.500,2.500,99.680,0.320,304.43,0.900,bicubic +convnext_xlarge.fb_in22k_ft_in1k,288,97.480,2.520,99.820,0.180,350.20,1.000,bicubic +convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,384,97.470,2.530,99.760,0.240,200.13,1.000,bicubic +maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,384,97.470,2.530,99.760,0.240,116.09,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,336,97.460,2.540,99.780,0.220,304.53,1.000,bicubic +convformer_b36.sail_in22k_ft_in1k_384,384,97.450,2.550,99.750,0.250,99.88,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in1k,224,97.450,2.550,99.690,0.310,304.20,1.000,bicubic +vit_large_patch16_384.augreg_in21k_ft_in1k,384,97.430,2.570,99.780,0.220,304.72,1.000,bicubic +regnety_1280.swag_lc_in1k,224,97.420,2.580,99.740,0.260,644.81,0.965,bicubic +vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k,384,97.410,2.590,99.770,0.230,64.27,1.000,bicubic +regnety_320.swag_ft_in1k,384,97.400,2.600,99.760,0.240,145.05,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,224,97.390,2.610,99.740,0.260,304.20,1.000,bicubic +convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,384,97.390,2.610,99.730,0.270,200.13,1.000,bicubic +caformer_b36.sail_in22k_ft_in1k,224,97.380,2.620,99.830,0.170,98.75,1.000,bicubic +convnextv2_base.fcmae_ft_in22k_in1k_384,384,97.370,2.630,99.720,0.280,88.72,1.000,bicubic +coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,384,97.370,2.630,99.700,0.300,73.88,1.000,bicubic +caformer_m36.sail_in22k_ft_in1k_384,384,97.360,2.640,99.780,0.220,56.20,1.000,bicubic +seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,384,97.360,2.640,99.730,0.270,149.39,1.000,bicubic +convformer_m36.sail_in22k_ft_in1k_384,384,97.360,2.640,99.680,0.320,57.05,1.000,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,224,97.350,2.650,99.800,0.200,632.05,1.000,bicubic +convnextv2_large.fcmae_ft_in22k_in1k,288,97.340,2.660,99.760,0.240,197.96,1.000,bicubic +beit_base_patch16_384.in22k_ft_in22k_in1k,384,97.340,2.660,99.710,0.290,86.74,1.000,bicubic +seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,288,97.330,2.670,99.760,0.240,93.59,0.950,bicubic +convnext_large.fb_in22k_ft_in1k_384,384,97.330,2.670,99.750,0.250,197.77,1.000,bicubic +maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,384,97.320,2.680,99.720,0.280,116.14,1.000,bicubic +deit3_large_patch16_224.fb_in22k_ft_in1k,224,97.320,2.680,99.680,0.320,304.37,1.000,bicubic +tf_efficientnetv2_l.in21k_ft_in1k,480,97.320,2.680,99.640,0.360,118.52,1.000,bicubic +tf_efficientnetv2_xl.in21k_ft_in1k,512,97.320,2.680,99.590,0.410,208.12,1.000,bicubic +seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,320,97.300,2.700,99.780,0.220,93.59,1.000,bicubic +swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,384,97.300,2.700,99.780,0.220,196.74,1.000,bicubic +volo_d5_512.sail_in1k,512,97.300,2.700,99.770,0.230,296.09,1.150,bicubic +beitv2_large_patch16_224.in1k_ft_in1k,224,97.290,2.710,99.740,0.260,304.43,0.950,bicubic +convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,320,97.290,2.710,99.740,0.260,200.13,1.000,bicubic +caformer_s36.sail_in22k_ft_in1k_384,384,97.280,2.720,99.720,0.280,39.30,1.000,bicubic +convnextv2_large.fcmae_ft_in22k_in1k,224,97.270,2.730,99.720,0.280,197.96,0.875,bicubic +convnextv2_huge.fcmae_ft_in1k,288,97.270,2.730,99.710,0.290,660.29,1.000,bicubic +swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,384,97.260,2.740,99.800,0.200,87.92,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k,224,97.260,2.740,99.740,0.260,350.20,0.875,bicubic +deit3_huge_patch14_224.fb_in22k_ft_in1k,224,97.250,2.750,99.720,0.280,632.13,1.000,bicubic +convnext_base.fb_in22k_ft_in1k,288,97.240,2.760,99.760,0.240,88.59,1.000,bicubic +volo_d5_448.sail_in1k,448,97.240,2.760,99.740,0.260,295.91,1.150,bicubic +convnext_base.fb_in22k_ft_in1k_384,384,97.240,2.760,99.720,0.280,88.59,1.000,bicubic +convnext_large.fb_in22k_ft_in1k,288,97.240,2.760,99.720,0.280,197.77,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in1k,336,97.240,2.760,99.720,0.280,304.53,1.000,bicubic +deit3_base_patch16_384.fb_in22k_ft_in1k,384,97.240,2.760,99.680,0.320,86.88,1.000,bicubic 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+wide_resnet50_2.racm_in1k,288,95.130,4.870,99.260,0.740,68.88,0.950,bicubic +fbnetv3_g.ra2_in1k,288,95.130,4.870,99.190,0.810,16.62,0.950,bilinear +inception_next_tiny.sail_in1k,224,95.120,4.880,99.140,0.860,28.06,0.875,bicubic +convit_base.fb_in1k,224,95.120,4.880,99.120,0.880,86.54,0.875,bicubic +ecaresnet50t.ra2_in1k,320,95.110,4.890,99.290,0.710,25.57,0.950,bicubic +efficientformerv2_s2.snap_dist_in1k,224,95.110,4.890,99.120,0.880,12.71,0.950,bicubic +mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k,256,95.100,4.900,99.250,0.750,11.07,0.950,bicubic +cs3sedarknet_l.c2ns_in1k,288,95.100,4.900,99.220,0.780,21.91,0.950,bicubic +davit_tiny.msft_in1k,224,95.100,4.900,99.140,0.860,28.36,0.950,bicubic +mobilevitv2_200.cvnets_in22k_ft_in1k,256,95.100,4.900,99.090,0.910,18.45,0.888,bicubic +coat_lite_small.in1k,224,95.100,4.900,99.020,0.980,19.84,0.900,bicubic +hgnet_small.paddle_in1k,288,95.090,4.910,99.320,0.680,24.36,1.000,bicubic +efficientnetv2_rw_t.ra2_in1k,288,95.090,4.910,99.210,0.790,13.65,1.000,bicubic +vit_relpos_medium_patch16_rpn_224.sw_in1k,224,95.090,4.910,99.210,0.790,38.73,0.900,bicubic +poolformer_m48.sail_in1k,224,95.090,4.910,99.100,0.900,73.47,0.950,bicubic +tresnet_xl.miil_in1k,224,95.080,4.920,99.250,0.750,78.44,0.875,bilinear +xception41p.ra3_in1k,299,95.080,4.920,99.190,0.810,26.91,0.940,bicubic +resnet152.tv2_in1k,224,95.080,4.920,99.180,0.820,60.19,0.965,bilinear +xcit_small_24_p16_224.fb_in1k,224,95.080,4.920,99.070,0.930,47.67,1.000,bicubic +ecaresnet101d_pruned.miil_in1k,288,95.060,4.940,99.240,0.760,24.88,0.950,bicubic +cs3sedarknet_x.c2ns_in1k,256,95.060,4.940,99.200,0.800,35.40,0.887,bicubic +swinv2_tiny_window8_256.ms_in1k,256,95.060,4.940,99.180,0.820,28.35,0.900,bicubic +coatnet_rmlp_nano_rw_224.sw_in1k,224,95.060,4.940,99.160,0.840,15.15,0.900,bicubic +crossvit_18_240.in1k,240,95.060,4.940,99.120,0.880,43.27,0.875,bicubic +poolformer_m36.sail_in1k,224,95.060,4.940,99.100,0.900,56.17,0.950,bicubic +cs3sedarknet_l.c2ns_in1k,256,95.050,4.950,99.140,0.860,21.91,0.887,bicubic +gcvit_xtiny.in1k,224,95.040,4.960,99.200,0.800,19.98,0.875,bicubic +poolformerv2_m36.sail_in1k,224,95.040,4.960,99.150,0.850,56.08,1.000,bicubic +deit_base_patch16_224.fb_in1k,224,95.040,4.960,98.960,1.040,86.57,0.900,bicubic +resnet61q.ra2_in1k,256,95.040,4.960,98.940,1.060,36.85,0.900,bicubic +cs3darknet_x.c2ns_in1k,256,95.030,4.970,99.190,0.810,35.05,0.950,bicubic +resnet51q.ra2_in1k,256,95.030,4.970,99.190,0.810,35.70,0.875,bilinear +hgnetv2_b2.ssld_stage1_in22k_in1k,224,95.030,4.970,99.180,0.820,11.22,0.965,bicubic +coatnet_nano_rw_224.sw_in1k,224,95.030,4.970,99.150,0.850,15.14,0.900,bicubic +focalnet_tiny_srf.ms_in1k,224,95.020,4.980,99.280,0.720,28.43,0.900,bicubic +halo2botnet50ts_256.a1h_in1k,256,95.020,4.980,99.050,0.950,22.64,0.950,bicubic +crossvit_base_240.in1k,240,95.020,4.980,98.990,1.010,105.03,0.875,bicubic +seresnext50_32x4d.racm_in1k,288,95.010,4.990,99.200,0.800,27.56,0.950,bicubic +tf_efficientnet_b3.ap_in1k,300,95.010,4.990,99.090,0.910,12.23,0.904,bicubic +regnety_040.ra3_in1k,224,95.000,5.000,99.220,0.780,20.65,0.950,bicubic +resnext50_32x4d.a1h_in1k,288,95.000,5.000,99.190,0.810,25.03,1.000,bicubic +pvt_v2_b2.in1k,224,95.000,5.000,99.140,0.860,25.36,0.900,bicubic +resnet152.a2_in1k,288,95.000,5.000,99.060,0.940,60.19,1.000,bicubic +resnet101.a1h_in1k,224,94.990,5.010,99.090,0.910,44.55,0.950,bicubic +coatnet_bn_0_rw_224.sw_in1k,224,94.980,5.020,99.230,0.770,27.44,0.950,bicubic +cait_xxs24_384.fb_dist_in1k,384,94.980,5.020,99.150,0.850,12.03,1.000,bicubic +crossvit_15_dagger_240.in1k,240,94.980,5.020,99.150,0.850,28.21,0.875,bicubic +convnext_tiny.fb_in1k,224,94.970,5.030,99.210,0.790,28.59,0.875,bicubic +visformer_small.in1k,224,94.970,5.030,99.210,0.790,40.22,0.900,bicubic +convmixer_1536_20.in1k,224,94.970,5.030,99.170,0.830,51.63,0.960,bicubic +swin_s3_tiny_224.ms_in1k,224,94.960,5.040,99.160,0.840,28.33,0.900,bicubic +eca_nfnet_l0.ra2_in1k,224,94.960,5.040,99.150,0.850,24.14,0.900,bicubic +resnetaa50d.sw_in12k_ft_in1k,224,94.950,5.050,99.270,0.730,25.58,0.950,bicubic +vit_srelpos_medium_patch16_224.sw_in1k,224,94.950,5.050,99.200,0.800,38.74,0.900,bicubic +xcit_large_24_p16_224.fb_in1k,224,94.950,5.050,98.830,1.170,189.10,1.000,bicubic +gernet_l.idstcv_in1k,256,94.940,5.060,99.200,0.800,31.08,0.875,bilinear +efficientnet_b3.ra2_in1k,288,94.940,5.060,99.170,0.830,12.23,0.875,bicubic +resnetv2_101.a1h_in1k,224,94.940,5.060,99.150,0.850,44.54,0.950,bicubic +nest_tiny_jx.goog_in1k,224,94.940,5.060,99.100,0.900,17.06,0.875,bicubic +coatnet_0_rw_224.sw_in1k,224,94.930,5.070,99.020,0.980,27.44,0.950,bicubic +regnety_032.ra_in1k,224,94.920,5.080,99.180,0.820,19.44,0.950,bicubic +tf_efficientnet_b3.aa_in1k,300,94.920,5.080,99.090,0.910,12.23,0.904,bicubic +vit_small_patch16_224.augreg_in21k_ft_in1k,224,94.910,5.090,99.270,0.730,22.05,0.900,bicubic +resnext101_32x8d.tv2_in1k,176,94.910,5.090,99.220,0.780,88.79,0.875,bilinear +convit_small.fb_in1k,224,94.910,5.090,99.120,0.880,27.78,0.875,bicubic +convnextv2_nano.fcmae_ft_in1k,224,94.910,5.090,99.000,1.000,15.62,0.875,bicubic +ecaresnet50t.a1_in1k,288,94.900,5.100,99.080,0.920,25.57,1.000,bicubic +mixer_b16_224.miil_in21k_ft_in1k,224,94.900,5.100,99.080,0.920,59.88,0.875,bilinear +resnet101.a1_in1k,288,94.900,5.100,99.030,0.970,44.55,1.000,bicubic +nfnet_l0.ra2_in1k,224,94.890,5.110,99.260,0.740,35.07,0.900,bicubic +regnety_032.tv2_in1k,224,94.890,5.110,99.250,0.750,19.44,0.965,bicubic +resnetv2_50d_evos.ah_in1k,288,94.890,5.110,99.200,0.800,25.59,1.000,bicubic +convnext_nano.d1h_in1k,288,94.890,5.110,99.130,0.870,15.59,1.000,bicubic +resnet101.a2_in1k,288,94.890,5.110,99.090,0.910,44.55,1.000,bicubic +resnet101.tv2_in1k,224,94.890,5.110,99.030,0.970,44.55,0.965,bilinear +efficientvit_b2.r224_in1k,224,94.890,5.110,99.000,1.000,24.33,0.950,bicubic +tf_efficientnet_b1.ns_jft_in1k,240,94.880,5.120,99.250,0.750,7.79,0.882,bicubic +xcit_tiny_24_p8_224.fb_in1k,224,94.880,5.120,99.190,0.810,12.11,1.000,bicubic +seresnext50_32x4d.racm_in1k,224,94.880,5.120,99.130,0.870,27.56,0.875,bicubic +tf_efficientnet_lite4.in1k,380,94.880,5.120,99.120,0.880,13.01,0.920,bilinear +xcit_small_12_p16_224.fb_in1k,224,94.870,5.130,99.040,0.960,26.25,1.000,bicubic +tf_efficientnetv2_b3.in21k_ft_in1k,240,94.870,5.130,99.020,0.980,14.36,0.900,bicubic +mobilenetv4_hybrid_medium.e500_r224_in1k,256,94.860,5.140,99.210,0.790,11.07,1.000,bicubic +coatnext_nano_rw_224.sw_in1k,224,94.860,5.140,99.200,0.800,14.70,0.900,bicubic +hgnet_small.paddle_in1k,224,94.860,5.140,99.200,0.800,24.36,0.965,bicubic +resnetaa50.a1h_in1k,288,94.860,5.140,99.140,0.860,25.56,1.000,bicubic +tresnet_l.miil_in1k,224,94.860,5.140,99.030,0.970,55.99,0.875,bilinear +edgenext_small.usi_in1k,320,94.850,5.150,99.410,0.590,5.59,1.000,bicubic +swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,224,94.840,5.160,99.280,0.720,28.29,0.900,bicubic +resnet50d.ra2_in1k,288,94.840,5.160,99.240,0.760,25.58,0.950,bicubic +vit_base_patch16_rpn_224.sw_in1k,224,94.830,5.170,99.090,0.910,86.54,0.900,bicubic +mobilenetv4_hybrid_medium.ix_e550_r256_in1k,256,94.820,5.180,99.110,0.890,11.07,0.950,bicubic +resnet50d.ra4_e3600_r224_in1k,224,94.820,5.180,99.050,0.950,25.58,0.950,bicubic +pit_b_224.in1k,224,94.820,5.180,98.820,1.180,73.76,0.900,bicubic +wide_resnet50_2.tv2_in1k,224,94.810,5.190,99.280,0.720,68.88,0.965,bilinear +cs3darknet_focus_l.c2ns_in1k,288,94.810,5.190,99.180,0.820,21.15,0.950,bicubic +swinv2_cr_tiny_ns_224.sw_in1k,224,94.780,5.220,99.130,0.870,28.33,0.900,bicubic +vit_base_patch32_clip_224.laion2b_ft_in1k,224,94.780,5.220,99.050,0.950,88.22,0.900,bicubic +seresnet50.ra2_in1k,288,94.770,5.230,99.130,0.870,28.09,0.950,bicubic +mobilevitv2_175.cvnets_in22k_ft_in1k,256,94.770,5.230,99.100,0.900,14.25,0.888,bicubic +twins_svt_small.in1k,224,94.770,5.230,99.080,0.920,24.06,0.900,bicubic +convnext_tiny_hnf.a2h_in1k,224,94.760,5.240,99.170,0.830,28.59,0.950,bicubic +gcresnet50t.ra2_in1k,288,94.760,5.240,99.120,0.880,25.90,1.000,bicubic +resnetv2_50x1_bit.goog_in21k_ft_in1k,448,94.750,5.250,99.180,0.820,25.55,1.000,bilinear +regnetx_080.tv2_in1k,224,94.750,5.250,99.050,0.950,39.57,0.965,bicubic +resnet152s.gluon_in1k,224,94.750,5.250,99.050,0.950,60.32,0.875,bicubic +lamhalobotnet50ts_256.a1h_in1k,256,94.750,5.250,98.990,1.010,22.57,0.950,bicubic +coat_mini.in1k,224,94.750,5.250,98.950,1.050,10.34,0.900,bicubic +resnet152.a2_in1k,224,94.750,5.250,98.810,1.190,60.19,0.950,bicubic +legacy_senet154.in1k,224,94.740,5.260,99.100,0.900,115.09,0.875,bilinear +mobilevitv2_150.cvnets_in22k_ft_in1k,256,94.740,5.260,98.920,1.080,10.59,0.888,bicubic +ecaresnet50t.ra2_in1k,256,94.730,5.270,99.080,0.920,25.57,0.875,bicubic +repvit_m3.dist_in1k,224,94.730,5.270,99.070,0.930,10.68,0.950,bicubic +xcit_tiny_12_p8_224.fb_dist_in1k,224,94.720,5.280,99.170,0.830,6.71,1.000,bicubic +resnest50d_4s2x40d.in1k,224,94.720,5.280,99.130,0.870,30.42,0.875,bicubic +poolformerv2_s36.sail_in1k,224,94.710,5.290,99.240,0.760,30.79,1.000,bicubic +resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,224,94.710,5.290,99.240,0.760,25.03,0.875,bilinear +resnetv2_50.a1h_in1k,288,94.710,5.290,99.110,0.890,25.55,1.000,bicubic +deit3_small_patch16_224.fb_in1k,224,94.710,5.290,98.730,1.270,22.06,0.900,bicubic +cs3darknet_l.c2ns_in1k,288,94.700,5.300,99.220,0.780,21.16,0.950,bicubic +pit_s_distilled_224.in1k,224,94.700,5.300,99.130,0.870,24.04,0.900,bicubic +regnetz_b16.ra3_in1k,288,94.700,5.300,99.120,0.880,9.72,1.000,bicubic +fastvit_sa12.apple_dist_in1k,256,94.700,5.300,99.070,0.930,11.58,0.900,bicubic +halonet50ts.a1h_in1k,256,94.700,5.300,98.840,1.160,22.73,0.940,bicubic +swin_tiny_patch4_window7_224.ms_in1k,224,94.690,5.310,99.100,0.900,28.29,0.900,bicubic +dm_nfnet_f0.dm_in1k,192,94.690,5.310,99.070,0.930,71.49,0.900,bicubic +senet154.gluon_in1k,224,94.690,5.310,98.970,1.030,115.09,0.875,bicubic +resnet152.a1_in1k,224,94.690,5.310,98.790,1.210,60.19,0.950,bicubic +ecaresnet50d.miil_in1k,288,94.680,5.320,99.230,0.770,25.58,0.950,bicubic +tresnet_m.miil_in1k_448,448,94.680,5.320,99.140,0.860,31.39,0.875,bilinear +crossvit_15_240.in1k,240,94.680,5.320,99.100,0.900,27.53,0.875,bicubic +vit_relpos_small_patch16_224.sw_in1k,224,94.680,5.320,99.100,0.900,21.98,0.900,bicubic +efficientnet_el.ra_in1k,300,94.670,5.330,99.130,0.870,10.59,0.904,bicubic +rexnet_200.nav_in1k,224,94.670,5.330,99.090,0.910,16.37,0.875,bicubic +efficientnetv2_rw_t.ra2_in1k,224,94.660,5.340,99.100,0.900,13.65,1.000,bicubic +wide_resnet50_2.racm_in1k,224,94.660,5.340,99.060,0.940,68.88,0.875,bicubic +seresnext101_64x4d.gluon_in1k,224,94.660,5.340,98.980,1.020,88.23,0.875,bicubic +vit_small_patch16_384.augreg_in1k,384,94.650,5.350,99.140,0.860,22.20,1.000,bicubic +tiny_vit_5m_224.dist_in22k_ft_in1k,224,94.630,5.370,99.140,0.860,5.39,0.950,bicubic +poolformer_s36.sail_in1k,224,94.630,5.370,99.060,0.940,30.86,0.900,bicubic +resnest50d.in1k,224,94.630,5.370,99.030,0.970,27.48,0.875,bilinear +resnet50_gn.a1h_in1k,288,94.620,5.380,99.150,0.850,25.56,0.950,bicubic +efficientnet_b3_pruned.in1k,300,94.620,5.380,99.040,0.960,9.86,0.904,bicubic +twins_pcpvt_small.in1k,224,94.610,5.390,99.140,0.860,24.11,0.900,bicubic +gcresnet50t.ra2_in1k,256,94.610,5.390,98.990,1.010,25.90,0.900,bicubic +repvgg_b3.rvgg_in1k,224,94.610,5.390,98.920,1.080,123.09,0.875,bilinear +fbnetv3_g.ra2_in1k,240,94.600,5.400,99.100,0.900,16.62,0.950,bilinear +pit_s_224.in1k,224,94.600,5.400,98.930,1.070,23.46,0.900,bicubic +crossvit_small_240.in1k,240,94.590,5.410,99.110,0.890,26.86,0.875,bicubic +resnet50.tv2_in1k,224,94.590,5.410,99.070,0.930,25.56,0.965,bilinear 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+efficientnet_el_pruned.in1k,300,94.070,5.930,99.010,0.990,10.59,0.904,bicubic +darknet53.c2ns_in1k,256,94.070,5.930,98.980,1.020,41.61,0.887,bicubic +tf_efficientnetv2_b2.in1k,260,94.070,5.930,98.930,1.070,10.10,0.890,bicubic +convnext_pico_ols.d1_in1k,288,94.060,5.940,98.930,1.070,9.06,1.000,bicubic +resnet50d.ra2_in1k,224,94.060,5.940,98.920,1.080,25.58,0.875,bicubic +hrnet_w48.ms_in1k,224,94.040,5.960,99.020,0.980,77.47,0.875,bilinear +resnet152.gluon_in1k,224,94.040,5.960,98.730,1.270,60.19,0.875,bicubic +dla102x2.in1k,224,94.030,5.970,99.010,0.990,41.28,0.875,bilinear +resnetrs50.tf_in1k,224,94.030,5.970,98.830,1.170,35.69,0.910,bicubic +wide_resnet50_2.tv2_in1k,176,94.020,5.980,99.100,0.900,68.88,0.875,bilinear +convnext_pico.d1_in1k,288,94.020,5.980,99.010,0.990,9.05,0.950,bicubic +regnetx_016.tv2_in1k,224,94.020,5.980,98.920,1.080,9.19,0.965,bicubic +resnet152.tv2_in1k,176,94.020,5.980,98.710,1.290,60.19,0.875,bilinear 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+resnet50d.a1_in1k,224,93.940,6.060,98.630,1.370,25.58,0.950,bicubic +resnet101.tv2_in1k,176,93.930,6.070,98.750,1.250,44.55,0.875,bilinear +regnetx_160.pycls_in1k,224,93.920,6.080,99.080,0.920,54.28,0.875,bicubic +xception71.tf_in1k,299,93.920,6.080,98.920,1.080,42.34,0.903,bicubic +cspresnet50.ra_in1k,256,93.910,6.090,98.850,1.150,21.62,0.887,bilinear +nf_regnet_b1.ra2_in1k,288,93.910,6.090,98.780,1.220,10.22,0.900,bicubic +regnety_080.pycls_in1k,224,93.900,6.100,98.990,1.010,39.18,0.875,bicubic +resnet50.c2_in1k,224,93.900,6.100,98.790,1.210,25.56,0.950,bicubic +vit_base_patch16_224.sam_in1k,224,93.890,6.110,98.890,1.110,86.57,0.900,bicubic +resnet152c.gluon_in1k,224,93.890,6.110,98.800,1.200,60.21,0.875,bicubic +eca_resnet33ts.ra2_in1k,256,93.880,6.120,98.890,1.110,19.68,0.900,bicubic +hgnet_tiny.paddle_in1k,224,93.870,6.130,99.000,1.000,14.74,0.965,bicubic +gcresnet33ts.ra2_in1k,256,93.870,6.130,98.930,1.070,19.88,0.900,bicubic +resnet101.a3_in1k,224,93.870,6.130,98.760,1.240,44.55,0.950,bicubic +mobilenetv4_conv_blur_medium.e500_r224_in1k,224,93.860,6.140,99.110,0.890,9.72,0.950,bicubic +ecaresnet50t.a3_in1k,224,93.860,6.140,98.860,1.140,25.57,0.950,bicubic +ecaresnet50d_pruned.miil_in1k,224,93.850,6.150,98.960,1.040,19.94,0.875,bicubic +repvgg_b2g4.rvgg_in1k,224,93.850,6.150,98.930,1.070,61.76,0.875,bilinear +ese_vovnet39b.ra_in1k,224,93.850,6.150,98.900,1.100,24.57,0.875,bicubic +mobilenetv4_conv_medium.e500_r256_in1k,256,93.850,6.150,98.870,1.130,9.72,0.950,bicubic +xcit_tiny_24_p16_224.fb_in1k,224,93.850,6.150,98.750,1.250,12.12,1.000,bicubic +hgnetv2_b0.ssld_stage2_ft_in1k,288,93.840,6.160,99.030,0.970,6.00,1.000,bicubic +hrnet_w64.ms_in1k,224,93.840,6.160,98.920,1.080,128.06,0.875,bilinear +resnext50_32x4d.ra_in1k,224,93.840,6.160,98.820,1.180,25.03,0.875,bicubic +resnext50d_32x4d.bt_in1k,224,93.840,6.160,98.730,1.270,25.05,0.875,bicubic +edgenext_small_rw.sw_in1k,256,93.830,6.170,98.720,1.280,7.83,0.900,bicubic +fbnetv3_d.ra2_in1k,256,93.820,6.180,98.900,1.100,10.31,0.950,bilinear +resnet101.gluon_in1k,224,93.820,6.180,98.710,1.290,44.55,0.875,bicubic +efficientnet_b2_pruned.in1k,260,93.810,6.190,98.910,1.090,8.31,0.890,bicubic +tiny_vit_5m_224.in1k,224,93.800,6.200,98.940,1.060,5.39,0.950,bicubic +xception65.tf_in1k,299,93.800,6.200,98.910,1.090,39.92,0.903,bicubic +resnet50.b2k_in1k,224,93.800,6.200,98.680,1.320,25.56,0.950,bicubic +resnet50.d_in1k,224,93.800,6.200,98.630,1.370,25.56,0.950,bicubic +resnext50_32x4d.a1_in1k,224,93.800,6.200,98.440,1.560,25.03,0.950,bicubic +darknetaa53.c2ns_in1k,256,93.790,6.210,98.940,1.060,36.02,0.887,bilinear +regnetx_080.pycls_in1k,224,93.790,6.210,98.910,1.090,39.57,0.875,bicubic +dpn131.mx_in1k,224,93.790,6.210,98.860,1.140,79.25,0.875,bicubic +resnext101_32x8d.tv_in1k,224,93.780,6.220,98.950,1.050,88.79,0.875,bilinear +dla169.in1k,224,93.780,6.220,98.860,1.140,53.39,0.875,bilinear +tf_efficientnet_b0.ns_jft_in1k,224,93.770,6.230,98.970,1.030,5.29,0.875,bicubic +repvit_m1_0.dist_300e_in1k,224,93.770,6.230,98.920,1.080,7.30,0.950,bicubic +wide_resnet101_2.tv_in1k,224,93.770,6.230,98.830,1.170,126.89,0.875,bilinear +resnext50_32x4d.a2_in1k,224,93.770,6.230,98.670,1.330,25.03,0.950,bicubic +convnextv2_femto.fcmae_ft_in1k,288,93.760,6.240,98.950,1.050,5.23,0.950,bicubic +efficientnet_em.ra2_in1k,240,93.760,6.240,98.910,1.090,6.90,0.882,bicubic +dpn68b.ra_in1k,288,93.760,6.240,98.520,1.480,12.61,1.000,bicubic +hrnet_w40.ms_in1k,224,93.750,6.250,98.830,1.170,57.56,0.875,bilinear +resnet50.tv2_in1k,176,93.750,6.250,98.750,1.250,25.56,0.875,bilinear +tf_efficientnet_b2.in1k,260,93.740,6.260,98.940,1.060,9.11,0.890,bicubic +tf_efficientnet_b1.aa_in1k,240,93.740,6.260,98.800,1.200,7.79,0.882,bicubic +mobileone_s4.apple_in1k,224,93.740,6.260,98.670,1.330,14.95,0.900,bilinear +tf_efficientnetv2_b1.in1k,240,93.730,6.270,98.850,1.150,8.14,0.882,bicubic +resnet101c.gluon_in1k,224,93.730,6.270,98.760,1.240,44.57,0.875,bicubic +resnet50.a2_in1k,224,93.730,6.270,98.720,1.280,25.56,0.950,bicubic +convnext_pico_ols.d1_in1k,224,93.720,6.280,98.860,1.140,9.06,0.950,bicubic +resnetblur50.bt_in1k,224,93.710,6.290,98.800,1.200,25.56,0.875,bicubic +levit_192.fb_dist_in1k,224,93.710,6.290,98.780,1.220,10.95,0.900,bicubic +levit_conv_192.fb_dist_in1k,224,93.710,6.290,98.780,1.220,10.95,0.900,bicubic +fastvit_s12.apple_in1k,256,93.710,6.290,98.720,1.280,9.47,0.900,bicubic +resnet50.b1k_in1k,224,93.700,6.300,98.800,1.200,25.56,0.950,bicubic +resnext50_32x4d.gluon_in1k,224,93.700,6.300,98.670,1.330,25.03,0.875,bicubic +regnetx_040.pycls_in1k,224,93.690,6.310,98.950,1.050,22.12,0.875,bicubic +seresnet50.a2_in1k,224,93.690,6.310,98.650,1.350,28.09,0.950,bicubic +resmlp_36_224.fb_in1k,224,93.670,6.330,98.920,1.080,44.69,0.875,bicubic +mobilenetv4_conv_medium.e500_r224_in1k,224,93.670,6.330,98.800,1.200,9.72,0.950,bicubic +rexnet_130.nav_in1k,224,93.670,6.330,98.700,1.300,7.56,0.875,bicubic +regnetx_064.pycls_in1k,224,93.660,6.340,99.050,0.950,26.21,0.875,bicubic +efficientvit_b1.r256_in1k,256,93.660,6.340,98.830,1.170,9.10,1.000,bicubic +legacy_xception.tf_in1k,299,93.660,6.340,98.760,1.240,22.86,0.897,bicubic +hrnet_w44.ms_in1k,224,93.650,6.350,98.960,1.040,67.06,0.875,bilinear +fbnetv3_b.ra2_in1k,256,93.650,6.350,98.900,1.100,8.60,0.950,bilinear +tf_efficientnet_b1.ap_in1k,240,93.640,6.360,98.780,1.220,7.79,0.882,bicubic +resnet33ts.ra2_in1k,256,93.640,6.360,98.760,1.240,19.68,0.900,bicubic +resnet34d.ra2_in1k,288,93.640,6.360,98.750,1.250,21.82,0.950,bicubic +regnety_040.pycls_in1k,224,93.630,6.370,98.960,1.040,20.65,0.875,bicubic +resnet50.am_in1k,224,93.630,6.370,98.860,1.140,25.56,0.875,bicubic +efficientformerv2_s1.snap_dist_in1k,224,93.620,6.380,98.960,1.040,6.19,0.950,bicubic +hgnetv2_b1.ssld_stage1_in22k_in1k,224,93.620,6.380,98.770,1.230,6.34,0.965,bicubic +hgnetv2_b1.ssld_stage2_ft_in1k,224,93.620,6.380,98.750,1.250,6.34,0.965,bicubic +dla60_res2next.in1k,224,93.610,6.390,98.810,1.190,17.03,0.875,bilinear +tf_efficientnetv2_b2.in1k,208,93.610,6.390,98.690,1.310,10.10,0.890,bicubic +halonet26t.a1h_in1k,256,93.610,6.390,98.640,1.360,12.48,0.950,bicubic +inception_v3.gluon_in1k,299,93.600,6.400,98.860,1.140,23.83,0.875,bicubic +resnet32ts.ra2_in1k,288,93.600,6.400,98.740,1.260,17.96,1.000,bicubic +hgnetv2_b0.ssld_stage1_in22k_in1k,288,93.590,6.410,98.980,1.020,6.00,1.000,bicubic +tf_efficientnet_cc_b1_8e.in1k,240,93.590,6.410,98.710,1.290,39.72,0.882,bicubic +resnet50s.gluon_in1k,224,93.580,6.420,98.850,1.150,25.68,0.875,bicubic +res2net50_26w_6s.in1k,224,93.580,6.420,98.760,1.240,37.05,0.875,bilinear +resnet32ts.ra2_in1k,256,93.580,6.420,98.730,1.270,17.96,0.900,bicubic +repvgg_b2.rvgg_in1k,224,93.570,6.430,99.070,0.930,89.02,0.875,bilinear +dla102x.in1k,224,93.570,6.430,98.860,1.140,26.31,0.875,bilinear +resnet50d.gluon_in1k,224,93.570,6.430,98.710,1.290,25.58,0.875,bicubic +eca_halonext26ts.c1_in1k,256,93.560,6.440,98.690,1.310,10.76,0.940,bicubic +resnext50_32x4d.a3_in1k,224,93.550,6.450,98.770,1.230,25.03,0.950,bicubic +convnext_tiny.fb_in22k_ft_in1k,288,93.550,6.450,98.620,1.380,28.59,1.000,bicubic +seresnet50.a1_in1k,224,93.550,6.450,98.570,1.430,28.09,0.950,bicubic +gmlp_s16_224.ra3_in1k,224,93.540,6.460,98.780,1.220,19.42,0.875,bicubic +repghostnet_200.in1k,224,93.540,6.460,98.600,1.400,9.80,0.875,bicubic +res2net101_26w_4s.in1k,224,93.530,6.470,98.620,1.380,45.21,0.875,bilinear +coat_lite_mini.in1k,224,93.520,6.480,98.800,1.200,11.01,0.900,bicubic +resnet50.ram_in1k,224,93.520,6.480,98.600,1.400,25.56,0.875,bicubic +pvt_v2_b1.in1k,224,93.510,6.490,98.880,1.120,14.01,0.900,bicubic +vit_tiny_patch16_384.augreg_in21k_ft_in1k,384,93.500,6.500,98.840,1.160,5.79,1.000,bicubic +efficientnet_b0.ra4_e3600_r224_in1k,256,93.500,6.500,98.830,1.170,5.29,1.000,bicubic +cait_xxs24_224.fb_dist_in1k,224,93.500,6.500,98.770,1.230,11.96,1.000,bicubic +coat_tiny.in1k,224,93.500,6.500,98.680,1.320,5.50,0.900,bicubic +fastvit_t12.apple_in1k,256,93.500,6.500,98.670,1.330,7.55,0.900,bicubic +regnety_032.pycls_in1k,224,93.490,6.510,98.960,1.040,19.44,0.875,bicubic +convnext_pico.d1_in1k,224,93.480,6.520,98.840,1.160,9.05,0.875,bicubic +mobilevitv2_125.cvnets_in1k,256,93.480,6.520,98.840,1.160,7.48,0.888,bicubic +selecsls60b.in1k,224,93.480,6.520,98.840,1.160,32.77,0.875,bicubic +xception41.tf_in1k,299,93.480,6.520,98.740,1.260,26.97,0.903,bicubic +hrnet_w18.ms_aug_in1k,224,93.470,6.530,99.000,1.000,21.30,0.950,bilinear +wide_resnet50_2.tv_in1k,224,93.470,6.530,98.970,1.030,68.88,0.875,bilinear +repvit_m0_9.dist_450e_in1k,224,93.470,6.530,98.880,1.120,5.49,0.950,bicubic +resnext50_32x4d.tv2_in1k,176,93.470,6.530,98.670,1.330,25.03,0.875,bilinear 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+repvgg_b1.rvgg_in1k,224,93.410,6.590,98.770,1.230,57.42,0.875,bilinear +botnet26t_256.c1_in1k,256,93.410,6.590,98.660,1.340,12.49,0.950,bicubic +hrnet_w30.ms_in1k,224,93.400,6.600,98.830,1.170,37.71,0.875,bilinear +resnet50d.a3_in1k,224,93.400,6.600,98.750,1.250,25.58,0.950,bicubic +eca_botnext26ts_256.c1_in1k,256,93.400,6.600,98.700,1.300,10.59,0.950,bicubic +convnext_femto_ols.d1_in1k,288,93.390,6.610,98.890,1.110,5.23,0.950,bicubic +resnet50.a1h_in1k,176,93.390,6.610,98.760,1.240,25.56,0.900,bicubic +xcit_nano_12_p8_384.fb_dist_in1k,384,93.380,6.620,98.880,1.120,3.05,1.000,bicubic +resnet152.a3_in1k,160,93.370,6.630,98.580,1.420,60.19,0.950,bicubic +xcit_tiny_12_p16_224.fb_dist_in1k,224,93.340,6.660,98.770,1.230,6.72,1.000,bicubic +seresnext26t_32x4d.bt_in1k,288,93.340,6.660,98.700,1.300,16.81,0.950,bicubic +repvit_m1.dist_in1k,224,93.340,6.660,98.660,1.340,5.49,0.950,bicubic +vit_base_patch16_224.augreg_in1k,224,93.340,6.660,98.640,1.360,86.57,0.900,bicubic +efficientvit_b1.r224_in1k,224,93.320,6.680,98.620,1.380,9.10,0.950,bicubic +regnetx_032.pycls_in1k,224,93.300,6.700,98.700,1.300,15.30,0.875,bicubic +legacy_seresnet101.in1k,224,93.290,6.710,98.750,1.250,49.33,0.875,bilinear +resnest26d.gluon_in1k,224,93.280,6.720,98.870,1.130,17.07,0.875,bilinear +dla102.in1k,224,93.280,6.720,98.790,1.210,33.27,0.875,bilinear +dpn68b.ra_in1k,224,93.280,6.720,98.530,1.470,12.61,0.950,bicubic +pit_xs_distilled_224.in1k,224,93.270,6.730,98.790,1.210,11.00,0.900,bicubic +resnet152.tv_in1k,224,93.270,6.730,98.750,1.250,60.19,0.875,bilinear +mixnet_l.ft_in1k,224,93.270,6.730,98.680,1.320,7.33,0.875,bicubic +cs3darknet_m.c2ns_in1k,288,93.240,6.760,98.710,1.290,9.31,0.950,bicubic +fbnetv3_d.ra2_in1k,224,93.230,6.770,98.670,1.330,10.31,0.950,bilinear +tf_efficientnetv2_b1.in1k,192,93.230,6.770,98.540,1.460,8.14,0.882,bicubic +dla60x.in1k,224,93.220,6.780,98.720,1.280,17.35,0.875,bilinear +tf_efficientnet_em.in1k,240,93.200,6.800,98.680,1.320,6.90,0.882,bicubic +inception_v3.tf_in1k,299,93.200,6.800,98.480,1.520,23.83,0.875,bicubic +convnextv2_femto.fcmae_ft_in1k,224,93.190,6.810,98.870,1.130,5.23,0.875,bicubic +res2net50_26w_4s.in1k,224,93.190,6.810,98.660,1.340,25.70,0.875,bilinear +mobilevit_s.cvnets_in1k,256,93.180,6.820,98.800,1.200,5.58,0.900,bicubic +vit_base_patch32_384.augreg_in1k,384,93.180,6.820,98.620,1.380,88.30,1.000,bicubic +regnety_008_tv.tv2_in1k,224,93.170,6.830,98.680,1.320,6.43,0.965,bicubic +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,256,93.170,6.830,98.320,1.680,119.42,0.900,bicubic +res2next50.in1k,224,93.140,6.860,98.650,1.350,24.67,0.875,bilinear +ghostnetv2_160.in1k,224,93.130,6.870,98.710,1.290,12.39,0.875,bicubic +efficientnet_b0.ra4_e3600_r224_in1k,224,93.130,6.870,98.660,1.340,5.29,0.900,bicubic +cs3darknet_focus_m.c2ns_in1k,288,93.120,6.880,98.750,1.250,9.30,0.950,bicubic +bat_resnext26ts.ch_in1k,256,93.120,6.880,98.720,1.280,10.73,0.900,bicubic +mobilevitv2_100.cvnets_in1k,256,93.110,6.890,98.760,1.240,4.90,0.888,bicubic +resnet50.bt_in1k,224,93.110,6.890,98.600,1.400,25.56,0.875,bicubic +repvgg_b1g4.rvgg_in1k,224,93.060,6.940,98.820,1.180,39.97,0.875,bilinear +levit_128.fb_dist_in1k,224,93.060,6.940,98.670,1.330,9.21,0.900,bicubic +regnety_016.pycls_in1k,224,93.060,6.940,98.660,1.340,11.20,0.875,bicubic +efficientnet_b1_pruned.in1k,240,93.060,6.940,98.560,1.440,6.33,0.882,bicubic +seresnext26d_32x4d.bt_in1k,288,93.050,6.950,98.710,1.290,16.81,0.950,bicubic +levit_conv_128.fb_dist_in1k,224,93.050,6.950,98.690,1.310,9.21,0.900,bicubic +tf_mixnet_l.in1k,224,93.050,6.950,98.550,1.450,7.33,0.875,bicubic +tf_efficientnetv2_b0.in1k,224,93.040,6.960,98.700,1.300,7.14,0.875,bicubic +efficientnet_b1.ft_in1k,256,93.020,6.980,98.710,1.290,7.79,1.000,bicubic +res2net50_14w_8s.in1k,224,93.020,6.980,98.700,1.300,25.06,0.875,bilinear +hardcorenas_f.miil_green_in1k,224,93.020,6.980,98.650,1.350,8.20,0.875,bilinear +resnet34.a1_in1k,288,93.020,6.980,98.590,1.410,21.80,1.000,bicubic +densenetblur121d.ra_in1k,288,93.010,6.990,98.600,1.400,8.00,0.950,bicubic +mobileone_s3.apple_in1k,224,93.000,7.000,98.610,1.390,10.17,0.900,bilinear +selecsls60.in1k,224,92.990,7.010,98.830,1.170,30.67,0.875,bicubic +hrnet_w32.ms_in1k,224,92.970,7.030,98.830,1.170,41.23,0.875,bilinear +hrnet_w18_small_v2.gluon_in1k,224,92.970,7.030,98.780,1.220,15.60,0.875,bicubic +visformer_tiny.in1k,224,92.970,7.030,98.740,1.260,10.32,0.900,bicubic +seresnext26ts.ch_in1k,288,92.970,7.030,98.710,1.290,10.39,1.000,bicubic +resnet50.a3_in1k,224,92.970,7.030,98.550,1.450,25.56,0.950,bicubic +dpn68b.mx_in1k,224,92.970,7.030,98.500,1.500,12.61,0.875,bicubic +inception_v3.tf_adv_in1k,299,92.970,7.030,98.490,1.510,23.83,0.875,bicubic +efficientnet_es.ra_in1k,224,92.960,7.040,98.690,1.310,5.44,0.875,bicubic +resnet26t.ra2_in1k,320,92.960,7.040,98.680,1.320,16.01,1.000,bicubic +densenet161.tv_in1k,224,92.950,7.050,98.800,1.200,28.68,0.875,bicubic +convnext_atto_ols.a2_in1k,288,92.950,7.050,98.660,1.340,3.70,0.950,bicubic +hardcorenas_e.miil_green_in1k,224,92.950,7.050,98.580,1.420,8.07,0.875,bilinear +tf_efficientnet_b1.in1k,240,92.940,7.060,98.660,1.340,7.79,0.882,bicubic +resnet50c.gluon_in1k,224,92.930,7.070,98.710,1.290,25.58,0.875,bicubic +fbnetv3_b.ra2_in1k,224,92.920,7.080,98.760,1.240,8.60,0.950,bilinear +convnextv2_atto.fcmae_ft_in1k,288,92.910,7.090,98.560,1.440,3.71,0.950,bicubic +poolformerv2_s12.sail_in1k,224,92.910,7.090,98.530,1.470,11.89,1.000,bicubic +pit_xs_224.in1k,224,92.900,7.100,98.790,1.210,10.62,0.900,bicubic +resnext50_32x4d.tv_in1k,224,92.900,7.100,98.730,1.270,25.03,0.875,bilinear +resnetrs50.tf_in1k,160,92.900,7.100,98.600,1.400,35.69,0.910,bicubic +inception_v3.tv_in1k,299,92.900,7.100,98.320,1.680,23.83,0.875,bicubic +resnet101.tv_in1k,224,92.890,7.110,98.650,1.350,44.55,0.875,bilinear +convnext_femto_ols.d1_in1k,224,92.880,7.120,98.690,1.310,5.23,0.875,bicubic +resmlp_12_224.fb_distilled_in1k,224,92.880,7.120,98.660,1.340,15.35,0.875,bicubic +coat_lite_tiny.in1k,224,92.880,7.120,98.650,1.350,5.72,0.900,bicubic +convnext_femto.d1_in1k,224,92.880,7.120,98.600,1.400,5.22,0.875,bicubic +resnet101.a3_in1k,160,92.880,7.120,98.520,1.480,44.55,0.950,bicubic +tf_efficientnet_cc_b0_8e.in1k,224,92.880,7.120,98.510,1.490,24.01,0.875,bicubic +tf_efficientnet_cc_b0_4e.in1k,224,92.860,7.140,98.440,1.560,13.31,0.875,bicubic +hgnetv2_b0.ssld_stage2_ft_in1k,224,92.840,7.160,98.690,1.310,6.00,0.965,bicubic +rexnet_100.nav_in1k,224,92.840,7.160,98.640,1.360,4.80,0.875,bicubic +tinynet_a.in1k,192,92.820,7.180,98.570,1.430,6.19,0.875,bicubic +gcresnext26ts.ch_in1k,288,92.810,7.190,98.610,1.390,10.48,1.000,bicubic +seresnext26ts.ch_in1k,256,92.810,7.190,98.600,1.400,10.39,0.900,bicubic +res2net50_48w_2s.in1k,224,92.810,7.190,98.450,1.550,25.29,0.875,bilinear +cs3darknet_focus_m.c2ns_in1k,256,92.800,7.200,98.600,1.400,9.30,0.887,bicubic +seresnext26t_32x4d.bt_in1k,224,92.800,7.200,98.540,1.460,16.81,0.875,bicubic +convnext_atto.d2_in1k,288,92.780,7.220,98.600,1.400,3.70,0.950,bicubic +crossvit_9_dagger_240.in1k,240,92.780,7.220,98.500,1.500,8.78,0.875,bicubic +eca_resnext26ts.ch_in1k,288,92.770,7.230,98.710,1.290,10.30,1.000,bicubic +hrnet_w18.ms_in1k,224,92.750,7.250,98.650,1.350,21.30,0.875,bilinear +densenet121.ra_in1k,288,92.750,7.250,98.630,1.370,7.98,0.950,bicubic +densenet201.tv_in1k,224,92.740,7.260,98.680,1.320,20.01,0.875,bicubic +ese_vovnet19b_dw.ra_in1k,288,92.740,7.260,98.650,1.350,6.54,0.950,bicubic +dla60.in1k,224,92.710,7.290,98.620,1.380,22.04,0.875,bilinear +tf_efficientnet_lite2.in1k,260,92.710,7.290,98.550,1.450,6.09,0.890,bicubic +cs3darknet_m.c2ns_in1k,256,92.700,7.300,98.680,1.320,9.31,0.887,bicubic +resnet34d.ra2_in1k,224,92.680,7.320,98.450,1.550,21.82,0.875,bicubic +hgnetv2_b0.ssld_stage1_in22k_in1k,224,92.670,7.330,98.690,1.310,6.00,0.965,bicubic +legacy_seresnet50.in1k,224,92.670,7.330,98.660,1.340,28.09,0.875,bilinear +resnet26t.ra2_in1k,256,92.670,7.330,98.600,1.400,16.01,0.940,bicubic +gmixer_24_224.ra3_in1k,224,92.670,7.330,98.240,1.760,24.72,0.875,bicubic +mobilenetv2_120d.ra_in1k,224,92.660,7.340,98.560,1.440,5.83,0.875,bicubic +repvgg_a2.rvgg_in1k,224,92.660,7.340,98.530,1.470,28.21,0.875,bilinear +mobileone_s2.apple_in1k,224,92.630,7.370,98.680,1.320,7.88,0.900,bilinear +hardcorenas_d.miil_green_in1k,224,92.620,7.380,98.480,1.520,7.50,0.875,bilinear +tf_efficientnet_b0.ap_in1k,224,92.620,7.380,98.410,1.590,5.29,0.875,bicubic +ecaresnet50t.a3_in1k,160,92.600,7.400,98.600,1.400,25.57,0.950,bicubic +regnetx_016.pycls_in1k,224,92.600,7.400,98.570,1.430,9.19,0.875,bicubic +resnet26d.bt_in1k,288,92.590,7.410,98.640,1.360,16.01,0.950,bicubic +resnet34.a2_in1k,288,92.590,7.410,98.600,1.400,21.80,1.000,bicubic +legacy_seresnext26_32x4d.in1k,224,92.590,7.410,98.450,1.550,16.79,0.875,bicubic +fastvit_t8.apple_dist_in1k,256,92.590,7.410,98.430,1.570,4.03,0.900,bicubic +resnet50.gluon_in1k,224,92.570,7.430,98.540,1.460,25.56,0.875,bicubic +skresnet34.ra_in1k,224,92.570,7.430,98.510,1.490,22.28,0.875,bicubic +xcit_tiny_12_p16_224.fb_in1k,224,92.540,7.460,98.650,1.350,6.72,1.000,bicubic +poolformer_s12.sail_in1k,224,92.510,7.490,98.390,1.610,11.92,0.900,bicubic +selecsls42b.in1k,224,92.480,7.520,98.440,1.560,32.46,0.875,bicubic +mobilenetv1_125.ra4_e3600_r224_in1k,256,92.470,7.530,98.580,1.420,6.27,1.000,bicubic +efficientnet_b0.ra_in1k,224,92.460,7.540,98.680,1.320,5.29,0.875,bicubic +seresnext26d_32x4d.bt_in1k,224,92.460,7.540,98.540,1.460,16.81,0.875,bicubic +gcresnext26ts.ch_in1k,256,92.460,7.540,98.510,1.490,10.48,0.900,bicubic +regnetx_008.tv2_in1k,224,92.460,7.540,98.450,1.550,7.26,0.965,bicubic +eca_resnext26ts.ch_in1k,256,92.450,7.550,98.610,1.390,10.30,0.900,bicubic +gernet_s.idstcv_in1k,224,92.440,7.560,98.500,1.500,8.17,0.875,bilinear +resnext26ts.ra2_in1k,288,92.430,7.570,98.390,1.610,10.30,1.000,bicubic +repghostnet_150.in1k,224,92.400,7.600,98.540,1.460,6.58,0.875,bicubic +xcit_nano_12_p8_224.fb_dist_in1k,224,92.400,7.600,98.520,1.480,3.05,1.000,bicubic +densenetblur121d.ra_in1k,224,92.400,7.600,98.410,1.590,8.00,0.875,bicubic +tf_efficientnet_b0.aa_in1k,224,92.390,7.610,98.480,1.520,5.29,0.875,bicubic +seresnet50.a3_in1k,224,92.380,7.620,98.330,1.670,28.09,0.950,bicubic +resnext50_32x4d.a3_in1k,160,92.350,7.650,98.360,1.640,25.03,0.950,bicubic +mobilenetv1_100h.ra4_e3600_r224_in1k,256,92.350,7.650,98.260,1.740,5.28,0.950,bicubic +densenet169.tv_in1k,224,92.340,7.660,98.570,1.430,14.15,0.875,bicubic +efficientnet_b1.ft_in1k,224,92.340,7.660,98.360,1.640,7.79,0.875,bicubic +convmixer_1024_20_ks9_p14.in1k,224,92.330,7.670,98.410,1.590,24.38,0.960,bicubic +resnet34.bt_in1k,288,92.320,7.680,98.640,1.360,21.80,0.950,bicubic +hardcorenas_c.miil_green_in1k,224,92.320,7.680,98.330,1.670,5.52,0.875,bilinear +tf_efficientnet_lite1.in1k,240,92.310,7.690,98.520,1.480,5.42,0.882,bicubic +dpn68.mx_in1k,224,92.290,7.710,98.600,1.400,12.61,0.875,bicubic +resnet50d.a3_in1k,160,92.280,7.720,98.330,1.670,25.58,0.950,bicubic +tf_efficientnet_b0.in1k,224,92.270,7.730,98.560,1.440,5.29,0.875,bicubic +ghostnetv2_130.in1k,224,92.270,7.730,98.370,1.630,8.96,0.875,bicubic +mixnet_m.ft_in1k,224,92.270,7.730,98.370,1.630,5.01,0.875,bicubic +tf_efficientnetv2_b0.in1k,192,92.260,7.740,98.370,1.630,7.14,0.875,bicubic +resnext26ts.ra2_in1k,256,92.260,7.740,98.270,1.730,10.30,0.900,bicubic +vit_small_patch32_224.augreg_in21k_ft_in1k,224,92.250,7.750,98.480,1.520,22.88,0.900,bicubic +resnet26d.bt_in1k,224,92.230,7.770,98.470,1.530,16.01,0.875,bicubic +mobilenetv3_large_100.miil_in21k_ft_in1k,224,92.230,7.770,98.230,1.770,5.48,0.875,bilinear 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+ese_vovnet19b_dw.ra_in1k,224,92.010,7.990,98.520,1.480,6.54,0.875,bicubic +mobilevitv2_075.cvnets_in1k,256,92.010,7.990,98.320,1.680,2.87,0.888,bicubic +hardcorenas_b.miil_green_in1k,224,91.960,8.040,98.420,1.580,5.18,0.875,bilinear +vit_tiny_patch16_224.augreg_in21k_ft_in1k,224,91.950,8.050,98.330,1.670,5.72,0.900,bicubic +fastvit_t8.apple_in1k,256,91.940,8.060,98.410,1.590,4.03,0.900,bicubic +densenet121.ra_in1k,224,91.940,8.060,98.280,1.720,7.98,0.875,bicubic +resnet34.a1_in1k,224,91.930,8.070,98.390,1.610,21.80,0.950,bicubic +repghostnet_130.in1k,224,91.930,8.070,98.360,1.640,5.48,0.875,bicubic +regnety_008.pycls_in1k,224,91.880,8.120,98.420,1.580,6.26,0.875,bicubic +efficientformerv2_s0.snap_dist_in1k,224,91.860,8.140,98.370,1.630,3.60,0.950,bicubic +mixnet_s.ft_in1k,224,91.820,8.180,98.340,1.660,4.13,0.875,bicubic +mobileone_s1.apple_in1k,224,91.780,8.220,98.470,1.530,4.83,0.900,bilinear +resnet34.a2_in1k,224,91.770,8.230,98.260,1.740,21.80,0.950,bicubic +repvgg_b0.rvgg_in1k,224,91.710,8.290,98.460,1.540,15.82,0.875,bilinear +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,384,91.710,8.290,98.420,1.580,6.36,1.000,bicubic +efficientnet_es_pruned.in1k,224,91.710,8.290,98.400,1.600,5.44,0.875,bicubic +tf_mixnet_s.in1k,224,91.710,8.290,98.230,1.770,4.13,0.875,bicubic +hardcorenas_a.miil_green_in1k,224,91.680,8.320,98.140,1.860,5.26,0.875,bilinear +semnasnet_100.rmsp_in1k,224,91.660,8.340,98.260,1.740,3.89,0.875,bicubic +resnet50.a3_in1k,160,91.660,8.340,98.060,1.940,25.56,0.950,bicubic +mobilenetv1_100h.ra4_e3600_r224_in1k,224,91.650,8.350,98.060,1.940,5.28,0.875,bicubic +ghostnetv2_100.in1k,224,91.630,8.370,98.280,1.720,6.16,0.875,bicubic +regnety_006.pycls_in1k,224,91.600,8.400,98.420,1.580,6.06,0.875,bicubic +regnety_004.tv2_in1k,224,91.590,8.410,98.300,1.700,4.34,0.965,bicubic +edgenext_x_small.in1k,288,91.590,8.410,98.190,1.810,2.34,1.000,bicubic +levit_128s.fb_dist_in1k,224,91.580,8.420,98.420,1.580,7.78,0.900,bicubic 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+seresnet50.a3_in1k,160,91.270,8.730,97.950,2.050,28.09,0.950,bicubic +dla34.in1k,224,91.260,8.740,98.160,1.840,15.74,0.875,bilinear +mnasnet_100.rmsp_in1k,224,91.260,8.740,98.030,1.970,4.38,0.875,bicubic +fbnetc_100.rmsp_in1k,224,91.250,8.750,97.830,2.170,5.57,0.875,bilinear +regnetx_008.pycls_in1k,224,91.220,8.780,98.380,1.620,7.26,0.875,bicubic +resnet34.bt_in1k,224,91.200,8.800,98.200,1.800,21.80,0.875,bicubic +hrnet_w18_small_v2.ms_in1k,224,91.190,8.810,98.350,1.650,15.60,0.875,bilinear +xcit_nano_12_p8_224.fb_in1k,224,91.190,8.810,98.280,1.720,3.05,1.000,bicubic +repvgg_a1.rvgg_in1k,224,91.160,8.840,98.190,1.810,14.09,0.875,bilinear +regnetx_004_tv.tv2_in1k,224,91.160,8.840,98.100,1.900,5.50,0.965,bicubic +resnest14d.gluon_in1k,224,91.140,8.860,98.350,1.650,10.61,0.875,bilinear +deit_tiny_distilled_patch16_224.fb_in1k,224,91.130,8.870,98.270,1.730,5.91,0.900,bicubic +tinynet_b.in1k,188,91.130,8.870,98.070,1.930,3.73,0.875,bicubic +mixer_b16_224.goog_in21k_ft_in1k,224,91.120,8.880,97.410,2.590,59.88,0.875,bicubic +resnet34.gluon_in1k,224,91.110,8.890,98.180,1.820,21.80,0.875,bicubic +repghostnet_111.in1k,224,91.110,8.890,98.050,1.950,4.54,0.875,bicubic +mobilenetv4_conv_small.e2400_r224_in1k,256,91.100,8.900,98.080,1.920,3.77,0.950,bicubic +crossvit_9_240.in1k,240,91.070,8.930,98.310,1.690,8.55,0.875,bicubic +resnet18.fb_swsl_ig1b_ft_in1k,224,91.060,8.940,98.200,1.800,11.69,0.875,bilinear +vgg19_bn.tv_in1k,224,90.980,9.020,98.120,1.880,143.68,0.875,bilinear +resnet18d.ra2_in1k,288,90.770,9.230,98.150,1.850,11.71,0.950,bicubic +regnety_004.pycls_in1k,224,90.760,9.240,98.070,1.930,4.34,0.875,bicubic +efficientvit_m4.r224_in1k,224,90.760,9.240,98.040,1.960,8.80,0.875,bicubic +pit_ti_distilled_224.in1k,224,90.750,9.250,98.250,1.750,5.10,0.900,bicubic +regnetx_006.pycls_in1k,224,90.750,9.250,98.090,1.910,6.20,0.875,bicubic +resnet18.fb_ssl_yfcc100m_ft_in1k,224,90.690,9.310,98.040,1.960,11.69,0.875,bilinear +spnasnet_100.rmsp_in1k,224,90.670,9.330,97.960,2.040,4.42,0.875,bilinear +repghostnet_100.in1k,224,90.660,9.340,98.110,1.890,4.07,0.875,bicubic +convit_tiny.fb_in1k,224,90.580,9.420,98.200,1.800,5.71,0.875,bicubic +vit_base_patch32_224.augreg_in1k,224,90.580,9.420,97.740,2.260,88.22,0.900,bicubic +crossvit_tiny_240.in1k,240,90.540,9.460,97.960,2.040,7.01,0.875,bicubic +mobilenetv4_conv_small.e2400_r224_in1k,224,90.540,9.460,97.820,2.180,3.77,0.875,bicubic +vgg16_bn.tv_in1k,224,90.520,9.480,97.990,2.010,138.37,0.875,bilinear +ghostnet_100.in1k,224,90.470,9.530,97.930,2.070,5.18,0.875,bicubic +pit_ti_224.in1k,224,90.450,9.550,98.010,1.990,4.85,0.900,bicubic +tf_mobilenetv3_large_075.in1k,224,90.330,9.670,97.860,2.140,3.99,0.875,bilinear +hrnet_w18_small.gluon_in1k,224,90.310,9.690,97.760,2.240,13.19,0.875,bicubic +resnet34.a3_in1k,224,90.300,9.700,97.890,2.110,21.80,0.950,bicubic +resnet34.tv_in1k,224,90.290,9.710,97.970,2.030,21.80,0.875,bilinear +semnasnet_075.rmsp_in1k,224,90.280,9.720,97.930,2.070,2.91,0.875,bicubic +mobilenetv4_conv_small.e1200_r224_in1k,256,90.260,9.740,98.090,1.910,3.77,0.950,bicubic +xcit_nano_12_p16_224.fb_dist_in1k,224,90.210,9.790,97.760,2.240,3.05,1.000,bicubic +resnet18.a1_in1k,288,90.190,9.810,97.780,2.220,11.69,1.000,bicubic +skresnet18.ra_in1k,224,90.170,9.830,97.780,2.220,11.96,0.875,bicubic +mobilenetv4_conv_small.e1200_r224_in1k,224,90.130,9.870,97.980,2.020,3.77,0.875,bicubic +efficientvit_m3.r224_in1k,224,90.060,9.940,97.830,2.170,6.90,0.875,bicubic +resnet18d.ra2_in1k,224,90.020,9.980,97.840,2.160,11.71,0.875,bicubic +hrnet_w18_small.ms_in1k,224,89.880,10.120,97.890,2.110,13.19,0.875,bilinear +mobilenetv2_100.ra_in1k,224,89.880,10.120,97.820,2.180,3.50,0.875,bicubic +vit_base_patch32_224.sam_in1k,224,89.880,10.120,97.600,2.400,88.22,0.900,bicubic +repvgg_a0.rvgg_in1k,224,89.810,10.190,97.790,2.210,9.11,0.875,bilinear +edgenext_xx_small.in1k,288,89.810,10.190,97.500,2.500,1.33,1.000,bicubic +vgg19.tv_in1k,224,89.700,10.300,97.530,2.470,143.67,0.875,bilinear +deit_tiny_patch16_224.fb_in1k,224,89.640,10.360,97.960,2.040,5.72,0.900,bicubic +resnet18.a2_in1k,288,89.540,10.460,97.660,2.340,11.69,1.000,bicubic +regnetx_004.pycls_in1k,224,89.490,10.510,97.820,2.180,5.16,0.875,bicubic +repghostnet_080.in1k,224,89.470,10.530,97.440,2.560,3.28,0.875,bicubic +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,224,89.410,10.590,97.700,2.300,6.34,0.900,bicubic +vgg16.tv_in1k,224,89.400,10.600,97.500,2.500,138.36,0.875,bilinear +legacy_seresnet18.in1k,224,89.290,10.710,97.710,2.290,11.78,0.875,bicubic +edgenext_xx_small.in1k,256,89.260,10.740,97.250,2.750,1.33,0.900,bicubic +resnet14t.c3_in1k,224,89.240,10.760,97.450,2.550,10.08,0.950,bicubic +vgg13_bn.tv_in1k,224,89.230,10.770,97.520,2.480,133.05,0.875,bilinear +tf_mobilenetv3_large_minimal_100.in1k,224,89.200,10.800,97.290,2.710,3.92,0.875,bilinear +resnet18.a1_in1k,224,89.150,10.850,97.330,2.670,11.69,0.950,bicubic +mobilevitv2_050.cvnets_in1k,256,89.060,10.940,97.590,2.410,1.37,0.888,bicubic +pvt_v2_b0.in1k,224,88.990,11.010,97.680,2.320,3.67,0.900,bicubic +efficientvit_m2.r224_in1k,224,88.940,11.060,97.350,2.650,4.19,0.875,bicubic +lcnet_100.ra2_in1k,224,88.920,11.080,97.370,2.630,2.95,0.875,bicubic +xcit_nano_12_p16_224.fb_in1k,224,88.900,11.100,97.440,2.560,3.05,1.000,bicubic +mobileone_s0.apple_in1k,224,88.780,11.220,97.230,2.770,5.29,0.875,bilinear +resnet18.gluon_in1k,224,88.690,11.310,97.110,2.890,11.69,0.875,bicubic +resnet34.a3_in1k,160,88.650,11.350,97.370,2.630,21.80,0.950,bicubic +resnet14t.c3_in1k,176,88.470,11.530,96.950,3.050,10.08,0.875,bicubic +tinynet_c.in1k,184,88.420,11.580,97.270,2.730,2.46,0.875,bicubic +vgg11_bn.tv_in1k,224,88.410,11.590,97.280,2.720,132.87,0.875,bilinear +efficientvit_b0.r224_in1k,224,88.270,11.730,96.880,3.120,3.41,0.950,bicubic +regnety_002.pycls_in1k,224,88.210,11.790,97.430,2.570,3.16,0.875,bicubic +resnet18.a2_in1k,224,88.210,11.790,97.180,2.820,11.69,0.950,bicubic +resnet18.tv_in1k,224,88.200,11.800,97.110,2.890,11.69,0.875,bilinear +mobilevit_xxs.cvnets_in1k,256,87.970,12.030,97.190,2.810,1.27,0.900,bicubic +vgg13.tv_in1k,224,87.550,12.450,97.120,2.880,133.05,0.875,bilinear +vgg11.tv_in1k,224,87.340,12.660,97.130,2.870,132.86,0.875,bilinear +regnetx_002.pycls_in1k,224,87.340,12.660,96.980,3.020,2.68,0.875,bicubic +efficientvit_m1.r224_in1k,224,87.190,12.810,97.000,3.000,2.98,0.875,bicubic +repghostnet_058.in1k,224,87.190,12.810,96.710,3.290,2.55,0.875,bicubic +dla60x_c.in1k,224,87.110,12.890,97.150,2.850,1.32,0.875,bilinear +resnet18.a3_in1k,224,87.020,12.980,96.670,3.330,11.69,0.950,bicubic +lcnet_075.ra2_in1k,224,87.010,12.990,96.550,3.450,2.36,0.875,bicubic +mixer_l16_224.goog_in21k_ft_in1k,224,87.000,13.000,94.020,5.980,208.20,0.875,bicubic +resnet10t.c3_in1k,224,86.710,13.290,96.730,3.270,5.44,0.950,bicubic +mobilenetv3_small_100.lamb_in1k,224,86.200,13.800,96.470,3.530,2.54,0.875,bicubic +tf_mobilenetv3_small_100.in1k,224,86.010,13.990,96.390,3.610,2.54,0.875,bilinear +repghostnet_050.in1k,224,85.560,14.440,96.110,3.890,2.31,0.875,bicubic +resnet10t.c3_in1k,176,85.490,14.510,96.290,3.710,5.44,0.875,bicubic +mnasnet_small.lamb_in1k,224,85.490,14.510,95.970,4.030,2.03,0.875,bicubic +tinynet_d.in1k,152,85.480,14.520,96.020,3.980,2.34,0.875,bicubic +dla46x_c.in1k,224,85.450,14.550,96.450,3.550,1.07,0.875,bilinear +resnet18.a3_in1k,160,85.040,14.960,95.940,4.060,11.69,0.950,bicubic +mobilenetv2_050.lamb_in1k,224,84.990,15.010,95.640,4.360,1.97,0.875,bicubic +dla46_c.in1k,224,84.720,15.280,96.210,3.790,1.30,0.875,bilinear +tf_mobilenetv3_small_075.in1k,224,84.520,15.480,95.790,4.210,2.04,0.875,bilinear +mobilenetv3_small_075.lamb_in1k,224,84.140,15.860,95.500,4.500,2.04,0.875,bicubic +efficientvit_m0.r224_in1k,224,83.340,16.660,95.630,4.370,2.35,0.875,bicubic +lcnet_050.ra2_in1k,224,83.130,16.870,94.950,5.050,1.88,0.875,bicubic +tf_mobilenetv3_small_minimal_100.in1k,224,82.640,17.360,94.990,5.010,2.04,0.875,bilinear +tinynet_e.in1k,106,79.840,20.160,94.000,6.000,2.04,0.875,bicubic +mobilenetv3_small_050.lamb_in1k,224,78.090,21.910,93.040,6.960,1.59,0.875,bicubic +test_efficientnet.r160_in1k,160,67.700,32.300,88.400,11.600,0.36,0.875,bicubic +test_byobnet.r160_in1k,160,67.360,32.640,88.390,11.610,0.46,0.875,bicubic +test_vit.r160_in1k,160,63.990,36.010,87.680,12.320,0.37,0.875,bicubic diff --git a/pytorch-image-models/tests/__init__.py b/pytorch-image-models/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pytorch-image-models/tests/test_optim.py b/pytorch-image-models/tests/test_optim.py new file mode 100644 index 0000000000000000000000000000000000000000..0d540f66a33aaefa2b1e4094c17be7c1296151c5 --- /dev/null +++ b/pytorch-image-models/tests/test_optim.py @@ -0,0 +1,616 @@ +""" Optimzier Tests + +These tests were adapted from PyTorch' optimizer tests. + +""" +import functools +import importlib +import os +from copy import deepcopy + +import pytest +import torch +from torch.nn import Parameter +from torch.testing._internal.common_utils import TestCase + +from timm.optim import create_optimizer_v2, list_optimizers, get_optimizer_class, get_optimizer_info, OptimInfo +from timm.optim import param_groups_layer_decay, param_groups_weight_decay +from timm.scheduler import PlateauLRScheduler + +torch_backend = os.environ.get('TORCH_BACKEND') +if torch_backend is not None: + importlib.import_module(torch_backend) +torch_device = os.environ.get('TORCH_DEVICE', 'cuda') + +# HACK relying on internal PyTorch test functionality for comparisons that I don't want to write +torch_tc = TestCase() + + +def _test_basic_cases_template(weight, bias, input, constructor, scheduler_constructors): + weight = Parameter(weight) + bias = Parameter(bias) + input = Parameter(input) + optimizer = constructor(weight, bias) + schedulers = [] + for scheduler_constructor in scheduler_constructors: + schedulers.append(scheduler_constructor(optimizer)) + + # to check if the optimizer can be printed as a string + optimizer.__repr__() + + def fn(): + optimizer.zero_grad() + y = weight.mv(input) + if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device(): + y = y.cuda(bias.get_device()) + loss = (y + bias).pow(2).sum() + loss.backward() + return loss + + initial_value = fn().item() + for _i in range(200): + for scheduler in schedulers: + if isinstance(scheduler, PlateauLRScheduler): + val_loss = fn() + scheduler.step(val_loss) + else: + scheduler.step() + optimizer.step(fn) + + assert fn().item() < initial_value + + +def _test_state_dict(weight, bias, input, constructor): + weight = Parameter(weight) + bias = Parameter(bias) + input = Parameter(input) + + def fn_base(optimizer, weight, bias): + optimizer.zero_grad() + i = input_device if weight.device.type != 'cpu' else input + loss = (weight.mv(i) + bias).pow(2).sum() + loss.backward() + return loss + + optimizer = constructor(weight, bias) + fn = functools.partial(fn_base, optimizer, weight, bias) + + # Prime the optimizer + for _i in range(20): + optimizer.step(fn) + # Clone the weights and construct new optimizer for them + with torch.no_grad(): + weight_c = Parameter(weight.clone().detach()) + bias_c = Parameter(bias.clone().detach()) + optimizer_c = constructor(weight_c, bias_c) + fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c) + # Load state dict + state_dict = deepcopy(optimizer.state_dict()) + state_dict_c = deepcopy(optimizer.state_dict()) + optimizer_c.load_state_dict(state_dict_c) + + # Run both optimizations in parallel + for _i in range(20): + optimizer.step(fn) + optimizer_c.step(fn_c) + torch_tc.assertEqual(weight, weight_c) + torch_tc.assertEqual(bias, bias_c) + # Make sure state dict is deterministic with equal but not identical parameters + torch_tc.assertEqual(optimizer.state_dict(), optimizer_c.state_dict()) + # Make sure repeated parameters have identical representation in state dict + optimizer_c.param_groups.extend(optimizer_c.param_groups) + torch_tc.assertEqual(optimizer.state_dict()['param_groups'][-1], optimizer_c.state_dict()['param_groups'][-1]) + + # Check that state dict can be loaded even when we cast parameters + # to a different type and move to a different device. + if torch_device == 'cpu': + return + elif torch_device == 'cuda' and not torch.cuda.is_available(): + return + + with torch.no_grad(): + input_device = Parameter(input.clone().detach().float().to(torch_device)) + weight_device = Parameter(weight.clone().detach().to(torch_device)) + bias_device = Parameter(bias.clone().detach().to(torch_device)) + optimizer_device = constructor(weight_device, bias_device) + fn_device = functools.partial(fn_base, optimizer_device, weight_device, bias_device) + + state_dict = deepcopy(optimizer.state_dict()) + state_dict_c = deepcopy(optimizer.state_dict()) + optimizer_device.load_state_dict(state_dict_c) + + # Make sure state dict wasn't modified + torch_tc.assertEqual(state_dict, state_dict_c) + + for _i in range(20): + optimizer.step(fn) + optimizer_device.step(fn_device) + torch_tc.assertEqual(weight, weight_device) + torch_tc.assertEqual(bias, bias_device) + + # validate deepcopy() copies all public attributes + def getPublicAttr(obj): + return set(k for k in obj.__dict__ if not k.startswith('_')) + + assert getPublicAttr(optimizer) == getPublicAttr(deepcopy(optimizer)) + + +def _test_basic_cases(constructor, scheduler_constructors=None): + if scheduler_constructors is None: + scheduler_constructors = [] + _test_state_dict( + torch.randn(10, 5), + torch.randn(10), + torch.randn(5), + constructor + ) + _test_basic_cases_template( + torch.randn(10, 5), + torch.randn(10), + torch.randn(5), + constructor, + scheduler_constructors + ) + # non-contiguous parameters + _test_basic_cases_template( + torch.randn(10, 5, 2)[..., 0], + torch.randn(10, 2)[..., 0], + torch.randn(5), + constructor, + scheduler_constructors + ) + # CUDA + if torch_device == 'cpu': + return + elif torch_device == 'cuda' and not torch.cuda.is_available(): + return + + _test_basic_cases_template( + torch.randn(10, 5).to(torch_device), + torch.randn(10).to(torch_device), + torch.randn(5).to(torch_device), + constructor, + scheduler_constructors + ) + + +def _test_model(optimizer, params, device=torch.device('cpu'), after_step=0): + weight = torch.tensor( + [[-0.2109, -0.4976], [-0.1413, -0.3420], [-0.2524, 0.6976]], + device=device, requires_grad=True) + bias = torch.tensor([-0.1085, -0.2979, 0.6892], device=device, requires_grad=True) + weight2 = torch.tensor([[-0.0508, -0.3941, -0.2843]], device=device, requires_grad=True) + bias2 = torch.tensor([-0.0711], device=device, requires_grad=True) + input = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], device=device).reshape(3, 2) + + model = torch.nn.Sequential(torch.nn.Linear(2, 3), + torch.nn.Sigmoid(), + torch.nn.Linear(3, 1), + torch.nn.Sigmoid()) + model.to(device) + + pretrained_dict = model.state_dict() + pretrained_dict['0.weight'] = weight + pretrained_dict['0.bias'] = bias + pretrained_dict['2.weight'] = weight2 + pretrained_dict['2.bias'] = bias2 + model.load_state_dict(pretrained_dict) + + optimizer = create_optimizer_v2(model, opt=optimizer, **params) + + prev_loss = float('inf') + for i in range(20): + optimizer.zero_grad() + output = model(input) + loss = output.sum() + loss.backward() + loss = loss.item() + if i > after_step: + assert loss < prev_loss + prev_loss = loss + optimizer.step() + + +def rosenbrock(tensor): + x, y = tensor + return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2 + + +def drosenbrock(tensor): + x, y = tensor + return torch.tensor((-400 * x * (y - x ** 2) - 2 * (1 - x), 200 * (y - x ** 2))) + + +def _test_rosenbrock(constructor, scheduler_constructors=None): + if scheduler_constructors is None: + scheduler_constructors = [] + params_t = torch.tensor([1.5, 1.5]) + + params = Parameter(params_t) + optimizer = constructor([params]) + schedulers = [] + for scheduler_constructor in scheduler_constructors: + schedulers.append(scheduler_constructor(optimizer)) + + solution = torch.tensor([1, 1]) + initial_dist = params.clone().detach().dist(solution) + + + def get_grad(_param, _sparse_grad, _w): + grad = drosenbrock(params.clone().detach()) + # Depending on w, provide only the x or y gradient + if _sparse_grad: + if _w: + i = torch.tensor([[0, 0]], dtype=torch.int64) + x = grad[0] + v = torch.tensor([x / 4.0, x - x / 4.0]) + else: + i = torch.tensor([[1, 1]], dtype=torch.int64) + y = grad[1] + v = torch.tensor([y - y / 4.0, y / 4.0]) + grad_out = torch.sparse_coo_tensor(i, v, (2,), dtype=v.dtype) + else: + if _w: + grad_out = torch.tensor([grad[0], 0], dtype=_param.dtype) + else: + grad_out = torch.tensor([0, grad[1]], dtype=_param.dtype) + return grad_out + + + def eval(_param, _sparse_grad, _w): + # Depending on w, provide only the x or y gradient + optimizer.zero_grad() + loss = rosenbrock(_param) + loss.backward() + + grad_out = get_grad(_param, _sparse_grad, _w) + with torch.no_grad(): + _param.grad = grad_out.to_dense() + + return loss + + for i in range(2000): + # Do cyclic coordinate descent + w = i % 2 + optimizer.step(functools.partial(eval, params, True, w)) + for scheduler in schedulers: + if isinstance(scheduler, PlateauLRScheduler): + scheduler.step(rosenbrock(params)) + else: + scheduler.step() + + torch_tc.assertLessEqual(params.clone().detach().dist(solution), initial_dist) + + +def _build_params_dict(weight, bias, **kwargs): + return [{'params': [weight]}, dict(params=[bias], **kwargs)] + + +def _build_params_dict_single(weight, bias, **kwargs): + return [dict(params=bias, **kwargs)] + + +@pytest.mark.parametrize('optimizer', list_optimizers(exclude_filters=('fused*', 'bnb*'))) +def test_optim_factory(optimizer): + assert issubclass(get_optimizer_class(optimizer, bind_defaults=False), torch.optim.Optimizer) + + opt_info = get_optimizer_info(optimizer) + assert isinstance(opt_info, OptimInfo) + + lr = (1e-2,) * 4 + if optimizer in ('mars', 'nadam', 'claprop', 'crmsproptf', 'cadafactorbv', 'csgdw', 'clamb'): + lr = (1e-3,) * 4 + elif optimizer in ('cmars',): + lr = (1e-4,) * 4 + + try: + if not opt_info.second_order: # basic tests don't support second order right now + # test basic cases that don't need specific tuning via factory test + _test_basic_cases( + lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=lr[0]) + ) + _test_basic_cases( + lambda weight, bias: create_optimizer_v2( + _build_params_dict(weight, bias, lr=lr[1]), + optimizer, + lr=lr[1] / 10) + ) + _test_basic_cases( + lambda weight, bias: create_optimizer_v2( + _build_params_dict_single(weight, bias, lr=lr[2]), + optimizer, + lr=lr[2] / 10) + ) + _test_basic_cases( + lambda weight, bias: create_optimizer_v2( + _build_params_dict_single(weight, bias, lr=lr[3]), + optimizer) + ) + except TypeError as e: + if 'radamw' in optimizer: + pytest.skip("Expected for 'radamw' (decoupled decay) to fail in older PyTorch versions.") + else: + raise e + + + +#@pytest.mark.parametrize('optimizer', ['sgd', 'momentum']) +# FIXME momentum variant frequently fails in GitHub runner, but never local after many attempts +@pytest.mark.parametrize('optimizer', ['sgd']) +def test_sgd(optimizer): + # _test_basic_cases( + # lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3), + # [lambda opt: StepLR(opt, gamma=0.9, step_size=10)] + # ) + # _test_basic_cases( + # lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3), + # [lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4, warmup_method="linear")] + # ) + # _test_basic_cases( + # lambda weight, bias: optimizer([weight, bias], lr=1e-3), + # [lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4, warmup_method="constant")] + # ) + # _test_basic_cases( + # lambda weight, bias: optimizer([weight, bias], lr=1e-3), + # [lambda opt: StepLR(opt, gamma=0.9, step_size=10), + # lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4)] + # ) + # _test_basic_cases( + # lambda weight, bias: optimizer([weight, bias], lr=1e-3), + # [lambda opt: StepLR(opt, gamma=0.9, step_size=10), + # lambda opt: ReduceLROnPlateau(opt)] + # ) + # _test_basic_cases( + # lambda weight, bias: optimizer([weight, bias], lr=1e-3), + # [lambda opt: StepLR(opt, gamma=0.99, step_size=10), + # lambda opt: ExponentialLR(opt, gamma=0.99), + # lambda opt: ReduceLROnPlateau(opt)] + # ) + _test_basic_cases( + lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=3e-3, momentum=1) + ) + _test_basic_cases( + lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=3e-3, momentum=1, weight_decay=.1) + ) + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=1e-3)) + + +@pytest.mark.parametrize('optimizer', ['adamw', 'adam', 'nadam', 'adamax', 'nadamw']) +def test_adam(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=5e-2) + ) + _test_model(optimizer, dict(lr=5e-2)) + + +@pytest.mark.parametrize('optimizer', ['adopt', 'adoptw']) +def test_adopt(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=3e-3) + ) + _test_model(optimizer, dict(lr=5e-2), after_step=1) # note no convergence in first step for ADOPT + + +@pytest.mark.parametrize('optimizer', ['adan', 'adanw']) +def test_adan(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=5e-2), after_step=1) # note no convergence in first step for ADOPT + + +@pytest.mark.parametrize('optimizer', ['adabelief']) +def test_adabelief(optimizer): + _test_basic_cases( + lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1) + ) + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=5e-2) + ) + _test_model(optimizer, dict(lr=5e-2)) + + +@pytest.mark.parametrize('optimizer', ['radam', 'radabelief']) +def test_rectified(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=1e-3)) + + +@pytest.mark.parametrize('optimizer', ['adadelta', 'adagrad']) +def test_adaother(optimizer): + _test_basic_cases( + lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1) + ) + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-1) + ) + _test_model(optimizer, dict(lr=5e-2)) + + +@pytest.mark.parametrize('optimizer', ['adafactor', 'adafactorbv']) +def test_adafactor(optimizer): + _test_basic_cases( + lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1) + ) + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=5e-2) + ) + _test_model(optimizer, dict(lr=5e-2)) + + +@pytest.mark.parametrize('optimizer', ['lamb', 'lambc']) +def test_lamb(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=1e-3)) + + +@pytest.mark.parametrize('optimizer', ['laprop']) +def test_laprop(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-2) + ) + _test_model(optimizer, dict(lr=1e-2)) + + +@pytest.mark.parametrize('optimizer', ['lars', 'larc', 'nlars', 'nlarc']) +def test_lars(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=1e-3)) + + +@pytest.mark.parametrize('optimizer', ['madgrad', 'madgradw']) +def test_madgrad(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-2) + ) + _test_model(optimizer, dict(lr=1e-2)) + + +@pytest.mark.parametrize('optimizer', ['mars']) +def test_mars(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=5e-2), after_step=1) # note no convergence in first step for ADOPT + + +@pytest.mark.parametrize('optimizer', ['novograd']) +def test_novograd(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=1e-3)) + + +@pytest.mark.parametrize('optimizer', ['rmsprop', 'rmsproptf']) +def test_rmsprop(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-2) + ) + _test_model(optimizer, dict(lr=1e-2)) + + +@pytest.mark.parametrize('optimizer', ['adamp']) +def test_adamp(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=5e-2) + ) + _test_model(optimizer, dict(lr=5e-2)) + + +@pytest.mark.parametrize('optimizer', ['sgdp']) +def test_sgdp(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + _test_model(optimizer, dict(lr=1e-3)) + + +@pytest.mark.parametrize('optimizer', ['lookahead_sgd', 'lookahead_momentum']) +def test_lookahead_sgd(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-3) + ) + + +@pytest.mark.parametrize('optimizer', ['lookahead_adamw', 'lookahead_adam']) +def test_lookahead_adam(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=5e-2) + ) + + +@pytest.mark.parametrize('optimizer', ['lookahead_radam']) +def test_lookahead_radam(optimizer): + _test_rosenbrock( + lambda params: create_optimizer_v2(params, optimizer, lr=1e-4) + ) + + +def test_param_groups_layer_decay_with_end_decay(): + model = torch.nn.Sequential( + torch.nn.Linear(10, 5), + torch.nn.ReLU(), + torch.nn.Linear(5, 2) + ) + + param_groups = param_groups_layer_decay( + model, + weight_decay=0.05, + layer_decay=0.75, + end_layer_decay=0.5, + verbose=True + ) + + assert len(param_groups) > 0 + # Verify layer scaling is applied with end decay + for group in param_groups: + assert 'lr_scale' in group + assert group['lr_scale'] <= 1.0 + assert group['lr_scale'] >= 0.5 + + +def test_param_groups_layer_decay_with_matcher(): + class ModelWithMatcher(torch.nn.Module): + def __init__(self): + super().__init__() + self.layer1 = torch.nn.Linear(10, 5) + self.layer2 = torch.nn.Linear(5, 2) + + def group_matcher(self, coarse=False): + return lambda name: int(name.split('.')[0][-1]) + + model = ModelWithMatcher() + param_groups = param_groups_layer_decay( + model, + weight_decay=0.05, + layer_decay=0.75, + verbose=True + ) + + assert len(param_groups) > 0 + # Verify layer scaling is applied + for group in param_groups: + assert 'lr_scale' in group + assert 'weight_decay' in group + assert len(group['params']) > 0 + + +def test_param_groups_weight_decay(): + model = torch.nn.Sequential( + torch.nn.Linear(10, 5), + torch.nn.ReLU(), + torch.nn.Linear(5, 2) + ) + weight_decay = 0.01 + no_weight_decay_list = ['1.weight'] + + param_groups = param_groups_weight_decay( + model, + weight_decay=weight_decay, + no_weight_decay_list=no_weight_decay_list + ) + + assert len(param_groups) == 2 + assert param_groups[0]['weight_decay'] == 0.0 + assert param_groups[1]['weight_decay'] == weight_decay + + # Verify parameters are correctly grouped + no_decay_params = set(param_groups[0]['params']) + decay_params = set(param_groups[1]['params']) + + for name, param in model.named_parameters(): + if param.ndim <= 1 or name.endswith(".bias") or name in no_weight_decay_list: + assert param in no_decay_params + else: + assert param in decay_params + diff --git a/pytorch-image-models/tests/test_utils.py b/pytorch-image-models/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1e2126eead8cef9bce68e619c23d9074b834d751 --- /dev/null +++ b/pytorch-image-models/tests/test_utils.py @@ -0,0 +1,192 @@ +from torch.nn.modules.batchnorm import BatchNorm2d +from torchvision.ops.misc import FrozenBatchNorm2d + +import timm +import pytest +from timm.utils.model import freeze, unfreeze +from timm.utils.model import ActivationStatsHook +from timm.utils.model import extract_spp_stats + +from timm.utils.model import _freeze_unfreeze +from timm.utils.model import avg_sq_ch_mean, avg_ch_var, avg_ch_var_residual +from timm.utils.model import reparameterize_model +from timm.utils.model import get_state_dict + +def test_freeze_unfreeze(): + model = timm.create_model('resnet18') + + # Freeze all + freeze(model) + # Check top level module + assert model.fc.weight.requires_grad == False + # Check submodule + assert model.layer1[0].conv1.weight.requires_grad == False + # Check BN + assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d) + + # Unfreeze all + unfreeze(model) + # Check top level module + assert model.fc.weight.requires_grad == True + # Check submodule + assert model.layer1[0].conv1.weight.requires_grad == True + # Check BN + assert isinstance(model.layer1[0].bn1, BatchNorm2d) + + # Freeze some + freeze(model, ['layer1', 'layer2.0']) + # Check frozen + assert model.layer1[0].conv1.weight.requires_grad == False + assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d) + assert model.layer2[0].conv1.weight.requires_grad == False + # Check not frozen + assert model.layer3[0].conv1.weight.requires_grad == True + assert isinstance(model.layer3[0].bn1, BatchNorm2d) + assert model.layer2[1].conv1.weight.requires_grad == True + + # Unfreeze some + unfreeze(model, ['layer1', 'layer2.0']) + # Check not frozen + assert model.layer1[0].conv1.weight.requires_grad == True + assert isinstance(model.layer1[0].bn1, BatchNorm2d) + assert model.layer2[0].conv1.weight.requires_grad == True + + # Freeze/unfreeze BN + # From root + freeze(model, ['layer1.0.bn1']) + assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d) + unfreeze(model, ['layer1.0.bn1']) + assert isinstance(model.layer1[0].bn1, BatchNorm2d) + # From direct parent + freeze(model.layer1[0], ['bn1']) + assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d) + unfreeze(model.layer1[0], ['bn1']) + assert isinstance(model.layer1[0].bn1, BatchNorm2d) + +def test_activation_stats_hook_validation(): + model = timm.create_model('resnet18') + + def test_hook(model, input, output): + return output.mean().item() + + # Test error case with mismatched lengths + with pytest.raises(ValueError, match="Please provide `hook_fns` for each `hook_fn_locs`"): + ActivationStatsHook( + model, + hook_fn_locs=['layer1.0.conv1', 'layer1.0.conv2'], + hook_fns=[test_hook] + ) + + +def test_extract_spp_stats(): + model = timm.create_model('resnet18') + + def test_hook(model, input, output): + return output.mean().item() + + stats = extract_spp_stats( + model, + hook_fn_locs=['layer1.0.conv1'], + hook_fns=[test_hook], + input_shape=[2, 3, 32, 32] + ) + + assert isinstance(stats, dict) + assert test_hook.__name__ in stats + assert isinstance(stats[test_hook.__name__], list) + assert len(stats[test_hook.__name__]) > 0 + +def test_freeze_unfreeze_bn_root(): + import torch.nn as nn + from timm.layers import BatchNormAct2d + + # Create batch norm layers + bn = nn.BatchNorm2d(10) + bn_act = BatchNormAct2d(10) + + # Test with BatchNorm2d as root + with pytest.raises(AssertionError): + _freeze_unfreeze(bn, mode="freeze") + + # Test with BatchNormAct2d as root + with pytest.raises(AssertionError): + _freeze_unfreeze(bn_act, mode="freeze") + + +def test_activation_stats_functions(): + import torch + + # Create sample input tensor [batch, channels, height, width] + x = torch.randn(2, 3, 4, 4) + + # Test avg_sq_ch_mean + result1 = avg_sq_ch_mean(None, None, x) + assert isinstance(result1, float) + + # Test avg_ch_var + result2 = avg_ch_var(None, None, x) + assert isinstance(result2, float) + + # Test avg_ch_var_residual + result3 = avg_ch_var_residual(None, None, x) + assert isinstance(result3, float) + + +def test_reparameterize_model(): + import torch.nn as nn + + class FusableModule(nn.Module): + def __init__(self): + super().__init__() + self.conv = nn.Conv2d(3, 3, 1) + + def fuse(self): + return nn.Identity() + + class ModelWithFusable(nn.Module): + def __init__(self): + super().__init__() + self.fusable = FusableModule() + self.normal = nn.Linear(10, 10) + + model = ModelWithFusable() + + # Test with inplace=False (should create a copy) + new_model = reparameterize_model(model, inplace=False) + assert isinstance(new_model.fusable, nn.Identity) + assert isinstance(model.fusable, FusableModule) # Original unchanged + + # Test with inplace=True + reparameterize_model(model, inplace=True) + assert isinstance(model.fusable, nn.Identity) + + +def test_get_state_dict_custom_unwrap(): + import torch.nn as nn + + class CustomModel(nn.Module): + def __init__(self): + super().__init__() + self.linear = nn.Linear(10, 10) + + model = CustomModel() + + def custom_unwrap(m): + return m + + state_dict = get_state_dict(model, unwrap_fn=custom_unwrap) + assert 'linear.weight' in state_dict + assert 'linear.bias' in state_dict + + +def test_freeze_unfreeze_string_input(): + model = timm.create_model('resnet18') + + # Test with string input + _freeze_unfreeze(model, 'layer1', mode='freeze') + assert model.layer1[0].conv1.weight.requires_grad == False + + # Test unfreezing with string input + _freeze_unfreeze(model, 'layer1', mode='unfreeze') + assert model.layer1[0].conv1.weight.requires_grad == True + diff --git a/pytorch-image-models/timm/data/_info/imagenet21k_goog_to_12k_indices.txt b/pytorch-image-models/timm/data/_info/imagenet21k_goog_to_12k_indices.txt new file mode 100644 index 0000000000000000000000000000000000000000..953537f8cb064bf3cfcc87cff4806584575dda24 --- /dev/null +++ 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a/pytorch-image-models/timm/data/tf_preprocessing.py b/pytorch-image-models/timm/data/tf_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..b58e0cf3cb5ac3be75fd76a0058892c8edd39703 --- /dev/null +++ b/pytorch-image-models/timm/data/tf_preprocessing.py @@ -0,0 +1,233 @@ +""" Tensorflow Preprocessing Adapter + +Allows use of Tensorflow preprocessing pipeline in PyTorch Transform + +Copyright of original Tensorflow code below. + +Hacked together by / Copyright 2020 Ross Wightman +""" + +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""ImageNet preprocessing for MnasNet.""" +import tensorflow.compat.v1 as tf +import numpy as np + +IMAGE_SIZE = 224 +CROP_PADDING = 32 + +tf.compat.v1.disable_eager_execution() + +def distorted_bounding_box_crop(image_bytes, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(0.75, 1.33), + area_range=(0.05, 1.0), + max_attempts=100, + scope=None): + """Generates cropped_image using one of the bboxes randomly distorted. + + See `tf.image.sample_distorted_bounding_box` for more documentation. + + Args: + image_bytes: `Tensor` of binary image data. + bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]` + where each coordinate is [0, 1) and the coordinates are arranged + as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole + image. + min_object_covered: An optional `float`. Defaults to `0.1`. The cropped + area of the image must contain at least this fraction of any bounding + box supplied. + aspect_ratio_range: An optional list of `float`s. The cropped area of the + image must have an aspect ratio = width / height within this range. + area_range: An optional list of `float`s. The cropped area of the image + must contain a fraction of the supplied image within in this range. + max_attempts: An optional `int`. Number of attempts at generating a cropped + region of the image of the specified constraints. After `max_attempts` + failures, return the entire image. + scope: Optional `str` for name scope. + Returns: + cropped image `Tensor` + """ + with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]): + shape = tf.image.extract_jpeg_shape(image_bytes) + sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box( + shape, + bounding_boxes=bbox, + min_object_covered=min_object_covered, + aspect_ratio_range=aspect_ratio_range, + area_range=area_range, + max_attempts=max_attempts, + use_image_if_no_bounding_boxes=True) + bbox_begin, bbox_size, _ = sample_distorted_bounding_box + + # Crop the image to the specified bounding box. + offset_y, offset_x, _ = tf.unstack(bbox_begin) + target_height, target_width, _ = tf.unstack(bbox_size) + crop_window = tf.stack([offset_y, offset_x, target_height, target_width]) + image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3) + + return image + + +def _at_least_x_are_equal(a, b, x): + """At least `x` of `a` and `b` `Tensors` are equal.""" + match = tf.equal(a, b) + match = tf.cast(match, tf.int32) + return tf.greater_equal(tf.reduce_sum(match), x) + + +def _decode_and_random_crop(image_bytes, image_size, resize_method): + """Make a random crop of image_size.""" + bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) + image = distorted_bounding_box_crop( + image_bytes, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(3. / 4, 4. / 3.), + area_range=(0.08, 1.0), + max_attempts=10, + scope=None) + original_shape = tf.image.extract_jpeg_shape(image_bytes) + bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3) + + image = tf.cond( + bad, + lambda: _decode_and_center_crop(image_bytes, image_size), + lambda: tf.image.resize([image], [image_size, image_size], resize_method)[0]) + + return image + + +def _decode_and_center_crop(image_bytes, image_size, resize_method): + """Crops to center of image with padding then scales image_size.""" + shape = tf.image.extract_jpeg_shape(image_bytes) + image_height = shape[0] + image_width = shape[1] + + padded_center_crop_size = tf.cast( + ((image_size / (image_size + CROP_PADDING)) * + tf.cast(tf.minimum(image_height, image_width), tf.float32)), + tf.int32) + + offset_height = ((image_height - padded_center_crop_size) + 1) // 2 + offset_width = ((image_width - padded_center_crop_size) + 1) // 2 + crop_window = tf.stack([offset_height, offset_width, + padded_center_crop_size, padded_center_crop_size]) + image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3) + image = tf.image.resize([image], [image_size, image_size], resize_method)[0] + + return image + + +def _flip(image): + """Random horizontal image flip.""" + image = tf.image.random_flip_left_right(image) + return image + + +def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE, interpolation='bicubic'): + """Preprocesses the given image for evaluation. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + interpolation: image interpolation method + + Returns: + A preprocessed image `Tensor`. + """ + resize_method = tf.image.ResizeMethod.BICUBIC if interpolation == 'bicubic' else tf.image.ResizeMethod.BILINEAR + image = _decode_and_random_crop(image_bytes, image_size, resize_method) + image = _flip(image) + image = tf.reshape(image, [image_size, image_size, 3]) + image = tf.image.convert_image_dtype( + image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32) + return image + + +def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE, interpolation='bicubic'): + """Preprocesses the given image for evaluation. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + interpolation: image interpolation method + + Returns: + A preprocessed image `Tensor`. + """ + resize_method = tf.image.ResizeMethod.BICUBIC if interpolation == 'bicubic' else tf.image.ResizeMethod.BILINEAR + image = _decode_and_center_crop(image_bytes, image_size, resize_method) + image = tf.reshape(image, [image_size, image_size, 3]) + image = tf.image.convert_image_dtype( + image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32) + return image + + +def preprocess_image(image_bytes, + is_training=False, + use_bfloat16=False, + image_size=IMAGE_SIZE, + interpolation='bicubic'): + """Preprocesses the given image. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + is_training: `bool` for whether the preprocessing is for training. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + interpolation: image interpolation method + + Returns: + A preprocessed image `Tensor` with value range of [0, 255]. + """ + if is_training: + return preprocess_for_train(image_bytes, use_bfloat16, image_size, interpolation) + else: + return preprocess_for_eval(image_bytes, use_bfloat16, image_size, interpolation) + + +class TfPreprocessTransform: + + def __init__(self, is_training=False, size=224, interpolation='bicubic'): + self.is_training = is_training + self.size = size[0] if isinstance(size, tuple) else size + self.interpolation = interpolation + self._image_bytes = None + self.process_image = self._build_tf_graph() + self.sess = None + + def _build_tf_graph(self): + with tf.device('/cpu:0'): + self._image_bytes = tf.placeholder( + shape=[], + dtype=tf.string, + ) + img = preprocess_image( + self._image_bytes, self.is_training, False, self.size, self.interpolation) + return img + + def __call__(self, image_bytes): + if self.sess is None: + self.sess = tf.Session() + img = self.sess.run(self.process_image, feed_dict={self._image_bytes: image_bytes}) + img = img.round().clip(0, 255).astype(np.uint8) + if img.ndim < 3: + img = np.expand_dims(img, axis=-1) + img = np.rollaxis(img, 2) # HWC to CHW + return img diff --git a/pytorch-image-models/timm/data/transforms.py b/pytorch-image-models/timm/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..215b7b5b61723d854761903b03a25f7e5b0c0567 --- /dev/null +++ b/pytorch-image-models/timm/data/transforms.py @@ -0,0 +1,583 @@ +import math +import numbers +import random +import warnings +from typing import List, Sequence, Tuple, Union + +import torch +import torchvision.transforms as transforms +import torchvision.transforms.functional as F +try: + from torchvision.transforms.functional import InterpolationMode + has_interpolation_mode = True +except ImportError: + has_interpolation_mode = False +from PIL import Image +import numpy as np + +__all__ = [ + "ToNumpy", "ToTensor", "str_to_interp_mode", "str_to_pil_interp", "interp_mode_to_str", + "RandomResizedCropAndInterpolation", "CenterCropOrPad", "center_crop_or_pad", "crop_or_pad", + "RandomCropOrPad", "RandomPad", "ResizeKeepRatio", "TrimBorder", "MaybeToTensor", "MaybePILToTensor" +] + + +class ToNumpy: + + def __call__(self, pil_img): + np_img = np.array(pil_img, dtype=np.uint8) + if np_img.ndim < 3: + np_img = np.expand_dims(np_img, axis=-1) + np_img = np.rollaxis(np_img, 2) # HWC to CHW + return np_img + + +class ToTensor: + """ ToTensor with no rescaling of values""" + def __init__(self, dtype=torch.float32): + self.dtype = dtype + + def __call__(self, pil_img): + return F.pil_to_tensor(pil_img).to(dtype=self.dtype) + + +class MaybeToTensor(transforms.ToTensor): + """Convert a PIL Image or ndarray to tensor if it's not already one. + """ + + def __init__(self) -> None: + super().__init__() + + def __call__(self, pic) -> torch.Tensor: + """ + Args: + pic (PIL Image or numpy.ndarray): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + if isinstance(pic, torch.Tensor): + return pic + return F.to_tensor(pic) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}()" + + +class MaybePILToTensor: + """Convert a PIL Image to a tensor of the same type - this does not scale values. + """ + + def __init__(self) -> None: + super().__init__() + + def __call__(self, pic): + """ + Note: A deep copy of the underlying array is performed. + + Args: + pic (PIL Image): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + if isinstance(pic, torch.Tensor): + return pic + return F.pil_to_tensor(pic) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}()" + + +# Pillow is deprecating the top-level resampling attributes (e.g., Image.BILINEAR) in +# favor of the Image.Resampling enum. The top-level resampling attributes will be +# removed in Pillow 10. +if hasattr(Image, "Resampling"): + _pil_interpolation_to_str = { + Image.Resampling.NEAREST: 'nearest', + Image.Resampling.BILINEAR: 'bilinear', + Image.Resampling.BICUBIC: 'bicubic', + Image.Resampling.BOX: 'box', + Image.Resampling.HAMMING: 'hamming', + Image.Resampling.LANCZOS: 'lanczos', + } +else: + _pil_interpolation_to_str = { + Image.NEAREST: 'nearest', + Image.BILINEAR: 'bilinear', + Image.BICUBIC: 'bicubic', + Image.BOX: 'box', + Image.HAMMING: 'hamming', + Image.LANCZOS: 'lanczos', + } + +_str_to_pil_interpolation = {b: a for a, b in _pil_interpolation_to_str.items()} + + +if has_interpolation_mode: + _torch_interpolation_to_str = { + InterpolationMode.NEAREST: 'nearest', + InterpolationMode.BILINEAR: 'bilinear', + InterpolationMode.BICUBIC: 'bicubic', + InterpolationMode.BOX: 'box', + InterpolationMode.HAMMING: 'hamming', + InterpolationMode.LANCZOS: 'lanczos', + } + _str_to_torch_interpolation = {b: a for a, b in _torch_interpolation_to_str.items()} +else: + _pil_interpolation_to_torch = {} + _torch_interpolation_to_str = {} + + +def str_to_pil_interp(mode_str): + return _str_to_pil_interpolation[mode_str] + + +def str_to_interp_mode(mode_str): + if has_interpolation_mode: + return _str_to_torch_interpolation[mode_str] + else: + return _str_to_pil_interpolation[mode_str] + + +def interp_mode_to_str(mode): + if has_interpolation_mode: + return _torch_interpolation_to_str[mode] + else: + return _pil_interpolation_to_str[mode] + + +_RANDOM_INTERPOLATION = (str_to_interp_mode('bilinear'), str_to_interp_mode('bicubic')) + + +def _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size."): + if isinstance(size, numbers.Number): + return int(size), int(size) + + if isinstance(size, Sequence) and len(size) == 1: + return size[0], size[0] + + if len(size) != 2: + raise ValueError(error_msg) + + return size + + +class RandomResizedCropAndInterpolation: + """Crop the given PIL Image to random size and aspect ratio with random interpolation. + + A crop of random size (default: of 0.08 to 1.0) of the original size and a random + aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop + is finally resized to given size. + This is popularly used to train the Inception networks. + + Args: + size: expected output size of each edge + scale: range of size of the origin size cropped + ratio: range of aspect ratio of the origin aspect ratio cropped + interpolation: Default: PIL.Image.BILINEAR + """ + + def __init__( + self, + size, + scale=(0.08, 1.0), + ratio=(3. / 4., 4. / 3.), + interpolation='bilinear', + ): + if isinstance(size, (list, tuple)): + self.size = tuple(size) + else: + self.size = (size, size) + if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): + warnings.warn("range should be of kind (min, max)") + + if interpolation == 'random': + self.interpolation = _RANDOM_INTERPOLATION + else: + self.interpolation = str_to_interp_mode(interpolation) + self.scale = scale + self.ratio = ratio + + @staticmethod + def get_params(img, scale, ratio): + """Get parameters for ``crop`` for a random sized crop. + + Args: + img (PIL Image): Image to be cropped. + scale (tuple): range of size of the origin size cropped + ratio (tuple): range of aspect ratio of the origin aspect ratio cropped + + Returns: + tuple: params (i, j, h, w) to be passed to ``crop`` for a random + sized crop. + """ + img_w, img_h = F.get_image_size(img) + area = img_w * img_h + + for attempt in range(10): + target_area = random.uniform(*scale) * area + log_ratio = (math.log(ratio[0]), math.log(ratio[1])) + aspect_ratio = math.exp(random.uniform(*log_ratio)) + + target_w = int(round(math.sqrt(target_area * aspect_ratio))) + target_h = int(round(math.sqrt(target_area / aspect_ratio))) + if target_w <= img_w and target_h <= img_h: + i = random.randint(0, img_h - target_h) + j = random.randint(0, img_w - target_w) + return i, j, target_h, target_w + + # Fallback to central crop + in_ratio = img_w / img_h + if in_ratio < min(ratio): + target_w = img_w + target_h = int(round(target_w / min(ratio))) + elif in_ratio > max(ratio): + target_h = img_h + target_w = int(round(target_h * max(ratio))) + else: # whole image + target_w = img_w + target_h = img_h + i = (img_h - target_h) // 2 + j = (img_w - target_w) // 2 + return i, j, target_h, target_w + + def __call__(self, img): + """ + Args: + img (PIL Image): Image to be cropped and resized. + + Returns: + PIL Image: Randomly cropped and resized image. + """ + i, j, h, w = self.get_params(img, self.scale, self.ratio) + if isinstance(self.interpolation, (tuple, list)): + interpolation = random.choice(self.interpolation) + else: + interpolation = self.interpolation + return F.resized_crop(img, i, j, h, w, self.size, interpolation) + + def __repr__(self): + if isinstance(self.interpolation, (tuple, list)): + interpolate_str = ' '.join([interp_mode_to_str(x) for x in self.interpolation]) + else: + interpolate_str = interp_mode_to_str(self.interpolation) + format_string = self.__class__.__name__ + '(size={0}'.format(self.size) + format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale)) + format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio)) + format_string += ', interpolation={0})'.format(interpolate_str) + return format_string + + +def center_crop_or_pad( + img: torch.Tensor, + output_size: Union[int, List[int]], + fill: Union[int, Tuple[int, int, int]] = 0, + padding_mode: str = 'constant', +) -> torch.Tensor: + """Center crops and/or pads the given image. + + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + img (PIL Image or Tensor): Image to be cropped. + output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int, + it is used for both directions. + fill (int, Tuple[int]): Padding color + + Returns: + PIL Image or Tensor: Cropped image. + """ + output_size = _setup_size(output_size) + crop_height, crop_width = output_size + _, image_height, image_width = F.get_dimensions(img) + + if crop_width > image_width or crop_height > image_height: + padding_ltrb = [ + (crop_width - image_width) // 2 if crop_width > image_width else 0, + (crop_height - image_height) // 2 if crop_height > image_height else 0, + (crop_width - image_width + 1) // 2 if crop_width > image_width else 0, + (crop_height - image_height + 1) // 2 if crop_height > image_height else 0, + ] + img = F.pad(img, padding_ltrb, fill=fill, padding_mode=padding_mode) + _, image_height, image_width = F.get_dimensions(img) + if crop_width == image_width and crop_height == image_height: + return img + + crop_top = int(round((image_height - crop_height) / 2.0)) + crop_left = int(round((image_width - crop_width) / 2.0)) + return F.crop(img, crop_top, crop_left, crop_height, crop_width) + + +class CenterCropOrPad(torch.nn.Module): + """Crops the given image at the center. + If the image is torch Tensor, it is expected + to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions. + If image size is smaller than output size along any edge, image is padded with 0 and then center cropped. + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]). + """ + + def __init__( + self, + size: Union[int, List[int]], + fill: Union[int, Tuple[int, int, int]] = 0, + padding_mode: str = 'constant', + ): + super().__init__() + self.size = _setup_size(size) + self.fill = fill + self.padding_mode = padding_mode + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + PIL Image or Tensor: Cropped image. + """ + return center_crop_or_pad(img, self.size, fill=self.fill, padding_mode=self.padding_mode) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + + +def crop_or_pad( + img: torch.Tensor, + top: int, + left: int, + height: int, + width: int, + fill: Union[int, Tuple[int, int, int]] = 0, + padding_mode: str = 'constant', +) -> torch.Tensor: + """ Crops and/or pads image to meet target size, with control over fill and padding_mode. + """ + _, image_height, image_width = F.get_dimensions(img) + right = left + width + bottom = top + height + if left < 0 or top < 0 or right > image_width or bottom > image_height: + padding_ltrb = [ + max(-left + min(0, right), 0), + max(-top + min(0, bottom), 0), + max(right - max(image_width, left), 0), + max(bottom - max(image_height, top), 0), + ] + img = F.pad(img, padding_ltrb, fill=fill, padding_mode=padding_mode) + + top = max(top, 0) + left = max(left, 0) + return F.crop(img, top, left, height, width) + + +class RandomCropOrPad(torch.nn.Module): + """ Crop and/or pad image with random placement within the crop or pad margin. + """ + + def __init__( + self, + size: Union[int, List[int]], + fill: Union[int, Tuple[int, int, int]] = 0, + padding_mode: str = 'constant', + ): + super().__init__() + self.size = _setup_size(size) + self.fill = fill + self.padding_mode = padding_mode + + @staticmethod + def get_params(img, size): + _, image_height, image_width = F.get_dimensions(img) + delta_height = image_height - size[0] + delta_width = image_width - size[1] + top = int(math.copysign(random.randint(0, abs(delta_height)), delta_height)) + left = int(math.copysign(random.randint(0, abs(delta_width)), delta_width)) + return top, left + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + PIL Image or Tensor: Cropped image. + """ + top, left = self.get_params(img, self.size) + return crop_or_pad( + img, + top=top, + left=left, + height=self.size[0], + width=self.size[1], + fill=self.fill, + padding_mode=self.padding_mode, + ) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" + + +class RandomPad: + def __init__(self, input_size, fill=0): + self.input_size = input_size + self.fill = fill + + @staticmethod + def get_params(img, input_size): + width, height = F.get_image_size(img) + delta_width = max(input_size[1] - width, 0) + delta_height = max(input_size[0] - height, 0) + pad_left = random.randint(0, delta_width) + pad_top = random.randint(0, delta_height) + pad_right = delta_width - pad_left + pad_bottom = delta_height - pad_top + return pad_left, pad_top, pad_right, pad_bottom + + def __call__(self, img): + padding = self.get_params(img, self.input_size) + img = F.pad(img, padding, self.fill) + return img + + +class ResizeKeepRatio: + """ Resize and Keep Aspect Ratio + """ + + def __init__( + self, + size, + longest=0., + interpolation='bilinear', + random_scale_prob=0., + random_scale_range=(0.85, 1.05), + random_scale_area=False, + random_aspect_prob=0., + random_aspect_range=(0.9, 1.11), + ): + """ + + Args: + size: + longest: + interpolation: + random_scale_prob: + random_scale_range: + random_scale_area: + random_aspect_prob: + random_aspect_range: + """ + if isinstance(size, (list, tuple)): + self.size = tuple(size) + else: + self.size = (size, size) + if interpolation == 'random': + self.interpolation = _RANDOM_INTERPOLATION + else: + self.interpolation = str_to_interp_mode(interpolation) + self.longest = float(longest) + self.random_scale_prob = random_scale_prob + self.random_scale_range = random_scale_range + self.random_scale_area = random_scale_area + self.random_aspect_prob = random_aspect_prob + self.random_aspect_range = random_aspect_range + + @staticmethod + def get_params( + img, + target_size, + longest, + random_scale_prob=0., + random_scale_range=(1.0, 1.33), + random_scale_area=False, + random_aspect_prob=0., + random_aspect_range=(0.9, 1.11) + ): + """Get parameters + """ + img_h, img_w = img_size = F.get_dimensions(img)[1:] + target_h, target_w = target_size + ratio_h = img_h / target_h + ratio_w = img_w / target_w + ratio = max(ratio_h, ratio_w) * longest + min(ratio_h, ratio_w) * (1. - longest) + + if random_scale_prob > 0 and random.random() < random_scale_prob: + ratio_factor = random.uniform(random_scale_range[0], random_scale_range[1]) + if random_scale_area: + # make ratio factor equivalent to RRC area crop where < 1.0 = area zoom, + # otherwise like affine scale where < 1.0 = linear zoom out + ratio_factor = 1. / math.sqrt(ratio_factor) + ratio_factor = (ratio_factor, ratio_factor) + else: + ratio_factor = (1., 1.) + + if random_aspect_prob > 0 and random.random() < random_aspect_prob: + log_aspect = (math.log(random_aspect_range[0]), math.log(random_aspect_range[1])) + aspect_factor = math.exp(random.uniform(*log_aspect)) + aspect_factor = math.sqrt(aspect_factor) + # currently applying random aspect adjustment equally to both dims, + # could change to keep output sizes above their target where possible + ratio_factor = (ratio_factor[0] / aspect_factor, ratio_factor[1] * aspect_factor) + + size = [round(x * f / ratio) for x, f in zip(img_size, ratio_factor)] + return size + + def __call__(self, img): + """ + Args: + img (PIL Image): Image to be cropped and resized. + + Returns: + PIL Image: Resized, padded to at least target size, possibly cropped to exactly target size + """ + size = self.get_params( + img, self.size, self.longest, + self.random_scale_prob, self.random_scale_range, self.random_scale_area, + self.random_aspect_prob, self.random_aspect_range + ) + if isinstance(self.interpolation, (tuple, list)): + interpolation = random.choice(self.interpolation) + else: + interpolation = self.interpolation + img = F.resize(img, size, interpolation) + return img + + def __repr__(self): + if isinstance(self.interpolation, (tuple, list)): + interpolate_str = ' '.join([interp_mode_to_str(x) for x in self.interpolation]) + else: + interpolate_str = interp_mode_to_str(self.interpolation) + format_string = self.__class__.__name__ + '(size={0}'.format(self.size) + format_string += f', interpolation={interpolate_str}' + format_string += f', longest={self.longest:.3f}' + format_string += f', random_scale_prob={self.random_scale_prob:.3f}' + format_string += f', random_scale_range=(' \ + f'{self.random_scale_range[0]:.3f}, {self.random_aspect_range[1]:.3f})' + format_string += f', random_aspect_prob={self.random_aspect_prob:.3f}' + format_string += f', random_aspect_range=(' \ + f'{self.random_aspect_range[0]:.3f}, {self.random_aspect_range[1]:.3f}))' + return format_string + + +class TrimBorder(torch.nn.Module): + + def __init__( + self, + border_size: int, + ): + super().__init__() + self.border_size = border_size + + def forward(self, img): + w, h = F.get_image_size(img) + top = left = self.border_size + top = min(top, h) + left = min(left, h) + height = max(0, h - 2 * self.border_size) + width = max(0, w - 2 * self.border_size) + return F.crop(img, top, left, height, width) \ No newline at end of file diff --git a/pytorch-image-models/timm/py.typed b/pytorch-image-models/timm/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pytorch-image-models/timm/version.py b/pytorch-image-models/timm/version.py new file mode 100644 index 0000000000000000000000000000000000000000..21470fe17a6a35eaf0ed83f29e64e7f50c75ac40 --- /dev/null +++ b/pytorch-image-models/timm/version.py @@ -0,0 +1 @@ +__version__ = '1.0.13.dev0'