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pytorch-image-models/hfdocs/source/models/ensemble-adversarial.mdx

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+# # 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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Ensemble Adversarial
+  Paper:
+    Title: Adversarial Attacks and Defences Competition
+    URL: https://paperswithcode.com/paper/adversarial-attacks-and-defences-competition
+Models:
+- Name: ens_adv_inception_resnet_v2
+  In Collection: Ensemble Adversarial
+  Metadata:
+    FLOPs: 16959133120
+    Parameters: 55850000
+    File Size: 223774238
+    Architecture:
+    - 1x1 Convolution
+    - Auxiliary Classifier
+    - Average Pooling
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inception-v3 Module
+    - Max Pooling
+    - ReLU
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: ens_adv_inception_resnet_v2
+    Crop Pct: '0.897'
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_resnet_v2.py#L351
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ens_adv_inception_resnet_v2-2592a550.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 1.0%
+      Top 5 Accuracy: 17.32%
+-->

pytorch-image-models/hfdocs/source/models/ese-vovnet.mdx

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+# 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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: ESE VovNet
+  Paper:
+    Title: 'CenterMask : Real-Time Anchor-Free Instance Segmentation'
+    URL: https://paperswithcode.com/paper/centermask-real-time-anchor-free-instance-1
+Models:
+- Name: ese_vovnet19b_dw
+  In Collection: ESE VovNet
+  Metadata:
+    FLOPs: 1711959904
+    Parameters: 6540000
+    File Size: 26243175
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Max Pooling
+    - One-Shot Aggregation
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: ese_vovnet19b_dw
+    Layers: 19
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/vovnet.py#L361
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ese_vovnet19b_dw-a8741004.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.82%
+      Top 5 Accuracy: 93.28%
+- Name: ese_vovnet39b
+  In Collection: ESE VovNet
+  Metadata:
+    FLOPs: 9089259008
+    Parameters: 24570000
+    File Size: 98397138
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Max Pooling
+    - One-Shot Aggregation
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: ese_vovnet39b
+    Layers: 39
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/vovnet.py#L371
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ese_vovnet39b-f912fe73.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.31%
+      Top 5 Accuracy: 94.72%
+-->

pytorch-image-models/hfdocs/source/models/gloun-resnext.mdx

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+# (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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Gloun ResNeXt
+  Paper:
+    Title: Aggregated Residual Transformations for Deep Neural Networks
+    URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
+Models:
+- Name: gluon_resnext101_32x4d
+  In Collection: Gloun ResNeXt
+  Metadata:
+    FLOPs: 10298145792
+    Parameters: 44180000
+    File Size: 177367414
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_resnext101_32x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L193
+  Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.33%
+      Top 5 Accuracy: 94.91%
+- Name: gluon_resnext101_64x4d
+  In Collection: Gloun ResNeXt
+  Metadata:
+    FLOPs: 19954172928
+    Parameters: 83460000
+    File Size: 334737852
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_resnext101_64x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L201
+  Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.63%
+      Top 5 Accuracy: 95.0%
+- Name: gluon_resnext50_32x4d
+  In Collection: Gloun ResNeXt
+  Metadata:
+    FLOPs: 5472648192
+    Parameters: 25030000
+    File Size: 100441719
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_resnext50_32x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L185
+  Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.35%
+      Top 5 Accuracy: 94.42%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Gloun SEResNeXt
+  Paper:
+    Title: Squeeze-and-Excitation Networks
+    URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
+Models:
+- Name: gluon_seresnext101_32x4d
+  In Collection: Gloun SEResNeXt
+  Metadata:
+    FLOPs: 10302923504
+    Parameters: 48960000
+    File Size: 196505510
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_seresnext101_32x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L219
+  Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.87%
+      Top 5 Accuracy: 95.29%
+- Name: gluon_seresnext101_64x4d
+  In Collection: Gloun SEResNeXt
+  Metadata:
+    FLOPs: 19958950640
+    Parameters: 88230000
+    File Size: 353875948
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_seresnext101_64x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L229
+  Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.88%
+      Top 5 Accuracy: 95.31%
+- Name: gluon_seresnext50_32x4d
+  In Collection: Gloun SEResNeXt
+  Metadata:
+    FLOPs: 5475179184
+    Parameters: 27560000
+    File Size: 110578827
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_seresnext50_32x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L209
+  Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.92%
+      Top 5 Accuracy: 94.82%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Gloun Xception
+  Paper:
+    Title: 'Xception: Deep Learning with Depthwise Separable Convolutions'
+    URL: https://paperswithcode.com/paper/xception-deep-learning-with-depthwise
+Models:
+- Name: gluon_xception65
+  In Collection: Gloun Xception
+  Metadata:
+    FLOPs: 17594889728
+    Parameters: 39920000
+    File Size: 160551306
+    Architecture:
+    - 1x1 Convolution
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: gluon_xception65
+    Crop Pct: '0.903'
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_xception.py#L241
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_xception-7015a15c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.7%
+      Top 5 Accuracy: 94.87%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: HRNet
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Visual Recognition
+    URL: https://paperswithcode.com/paper/190807919
+Models:
+- Name: hrnet_w18
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 5547205500
+    Parameters: 21300000
+    File Size: 85718883
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w18
+    Epochs: 100
+    Layers: 18
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L800
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.76%
+      Top 5 Accuracy: 93.44%
+- Name: hrnet_w18_small
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 2071651488
+    Parameters: 13190000
+    File Size: 52934302
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w18_small
+    Epochs: 100
+    Layers: 18
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L790
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.34%
+      Top 5 Accuracy: 90.68%
+- Name: hrnet_w18_small_v2
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 3360023160
+    Parameters: 15600000
+    File Size: 62682879
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w18_small_v2
+    Epochs: 100
+    Layers: 18
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L795
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.11%
+      Top 5 Accuracy: 92.41%
+- Name: hrnet_w30
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 10474119492
+    Parameters: 37710000
+    File Size: 151452218
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w30
+    Epochs: 100
+    Layers: 30
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L805
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.21%
+      Top 5 Accuracy: 94.22%
+- Name: hrnet_w32
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 11524528320
+    Parameters: 41230000
+    File Size: 165547812
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    Training Time: 60 hours
+    ID: hrnet_w32
+    Epochs: 100
+    Layers: 32
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L810
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.45%
+      Top 5 Accuracy: 94.19%
+- Name: hrnet_w40
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 16381182192
+    Parameters: 57560000
+    File Size: 230899236
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w40
+    Epochs: 100
+    Layers: 40
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L815
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.93%
+      Top 5 Accuracy: 94.48%
+- Name: hrnet_w44
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 19202520264
+    Parameters: 67060000
+    File Size: 268957432
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w44
+    Epochs: 100
+    Layers: 44
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L820
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.89%
+      Top 5 Accuracy: 94.37%
+- Name: hrnet_w48
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 22285865760
+    Parameters: 77470000
+    File Size: 310603710
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    Training Time: 80 hours
+    ID: hrnet_w48
+    Epochs: 100
+    Layers: 48
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L825
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.32%
+      Top 5 Accuracy: 94.51%
+- Name: hrnet_w64
+  In Collection: HRNet
+  Metadata:
+    FLOPs: 37239321984
+    Parameters: 128060000
+    File Size: 513071818
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - ReLU
+    - Residual Connection
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x NVIDIA V100 GPUs
+    ID: hrnet_w64
+    Epochs: 100
+    Layers: 64
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L830
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.46%
+      Top 5 Accuracy: 94.65%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: IG ResNeXt
+  Paper:
+    Title: Exploring the Limits of Weakly Supervised Pretraining
+    URL: https://paperswithcode.com/paper/exploring-the-limits-of-weakly-supervised
+Models:
+- Name: ig_resnext101_32x16d
+  In Collection: IG ResNeXt
+  Metadata:
+    FLOPs: 46623691776
+    Parameters: 194030000
+    File Size: 777518664
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - IG-3.5B-17k
+    - ImageNet
+    Training Resources: 336x GPUs
+    ID: ig_resnext101_32x16d
+    Epochs: 100
+    Layers: 101
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 8064
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L874
+  Weights: https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 84.16%
+      Top 5 Accuracy: 97.19%
+- Name: ig_resnext101_32x32d
+  In Collection: IG ResNeXt
+  Metadata:
+    FLOPs: 112225170432
+    Parameters: 468530000
+    File Size: 1876573776
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - IG-3.5B-17k
+    - ImageNet
+    Training Resources: 336x GPUs
+    ID: ig_resnext101_32x32d
+    Epochs: 100
+    Layers: 101
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 8064
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+    Minibatch Size: 8064
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L885
+  Weights: https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 85.09%
+      Top 5 Accuracy: 97.44%
+- Name: ig_resnext101_32x48d
+  In Collection: IG ResNeXt
+  Metadata:
+    FLOPs: 197446554624
+    Parameters: 828410000
+    File Size: 3317136976
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - IG-3.5B-17k
+    - ImageNet
+    Training Resources: 336x GPUs
+    ID: ig_resnext101_32x48d
+    Epochs: 100
+    Layers: 101
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 8064
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L896
+  Weights: https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 85.42%
+      Top 5 Accuracy: 97.58%
+- Name: ig_resnext101_32x8d
+  In Collection: IG ResNeXt
+  Metadata:
+    FLOPs: 21180417024
+    Parameters: 88790000
+    File Size: 356056638
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Nesterov Accelerated Gradient
+    - Weight Decay
+    Training Data:
+    - IG-3.5B-17k
+    - ImageNet
+    Training Resources: 336x GPUs
+    ID: ig_resnext101_32x8d
+    Epochs: 100
+    Layers: 101
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 8064
+    Image Size: '224'
+    Weight Decay: 0.001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L863
+  Weights: https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.7%
+      Top 5 Accuracy: 96.64%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Inception ResNet v2
+  Paper:
+    Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
+      Learning
+    URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
+Models:
+- Name: inception_resnet_v2
+  In Collection: Inception ResNet v2
+  Metadata:
+    FLOPs: 16959133120
+    Parameters: 55850000
+    File Size: 223774238
+    Architecture:
+    - Average Pooling
+    - Dropout
+    - Inception-ResNet-v2 Reduction-B
+    - Inception-ResNet-v2-A
+    - Inception-ResNet-v2-B
+    - Inception-ResNet-v2-C
+    - Reduction-A
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 20x NVIDIA Kepler GPUs
+    ID: inception_resnet_v2
+    LR: 0.045
+    Dropout: 0.2
+    Crop Pct: '0.897'
+    Momentum: 0.9
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_resnet_v2.py#L343
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/inception_resnet_v2-940b1cd6.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 0.95%
+      Top 5 Accuracy: 17.29%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Inception v3
+  Paper:
+    Title: Rethinking the Inception Architecture for Computer Vision
+    URL: https://paperswithcode.com/paper/rethinking-the-inception-architecture-for
+Models:
+- Name: inception_v3
+  In Collection: Inception v3
+  Metadata:
+    FLOPs: 7352418880
+    Parameters: 23830000
+    File Size: 108857766
+    Architecture:
+    - 1x1 Convolution
+    - Auxiliary Classifier
+    - Average Pooling
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inception-v3 Module
+    - Max Pooling
+    - ReLU
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Gradient Clipping
+    - Label Smoothing
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 50x NVIDIA Kepler GPUs
+    ID: inception_v3
+    LR: 0.045
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L442
+  Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.46%
+      Top 5 Accuracy: 93.48%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Inception v4
+  Paper:
+    Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
+      Learning
+    URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
+Models:
+- Name: inception_v4
+  In Collection: Inception v4
+  Metadata:
+    FLOPs: 15806527936
+    Parameters: 42680000
+    File Size: 171082495
+    Architecture:
+    - Average Pooling
+    - Dropout
+    - Inception-A
+    - Inception-B
+    - Inception-C
+    - Reduction-A
+    - Reduction-B
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 20x NVIDIA Kepler GPUs
+    ID: inception_v4
+    LR: 0.045
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 1.01%
+      Top 5 Accuracy: 16.85%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Legacy SE ResNet
+  Paper:
+    Title: Squeeze-and-Excitation Networks
+    URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
+Models:
+- Name: legacy_seresnet101
+  In Collection: Legacy SE ResNet
+  Metadata:
+    FLOPs: 9762614000
+    Parameters: 49330000
+    File Size: 197822624
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnet101
+    LR: 0.6
+    Epochs: 100
+    Layers: 101
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L426
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.38%
+      Top 5 Accuracy: 94.26%
+- Name: legacy_seresnet152
+  In Collection: Legacy SE ResNet
+  Metadata:
+    FLOPs: 14553578160
+    Parameters: 66819999
+    File Size: 268033864
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnet152
+    LR: 0.6
+    Epochs: 100
+    Layers: 152
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L433
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.67%
+      Top 5 Accuracy: 94.38%
+- Name: legacy_seresnet18
+  In Collection: Legacy SE ResNet
+  Metadata:
+    FLOPs: 2328876024
+    Parameters: 11780000
+    File Size: 47175663
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnet18
+    LR: 0.6
+    Epochs: 100
+    Layers: 18
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L405
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 71.74%
+      Top 5 Accuracy: 90.34%
+- Name: legacy_seresnet34
+  In Collection: Legacy SE ResNet
+  Metadata:
+    FLOPs: 4706201004
+    Parameters: 21960000
+    File Size: 87958697
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnet34
+    LR: 0.6
+    Epochs: 100
+    Layers: 34
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L412
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 74.79%
+      Top 5 Accuracy: 92.13%
+- Name: legacy_seresnet50
+  In Collection: Legacy SE ResNet
+  Metadata:
+    FLOPs: 4974351024
+    Parameters: 28090000
+    File Size: 112611220
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnet50
+    LR: 0.6
+    Epochs: 100
+    Layers: 50
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '224'
+    Interpolation: bilinear
+    Minibatch Size: 1024
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L419
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.64%
+      Top 5 Accuracy: 93.74%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Legacy SE ResNeXt
+  Paper:
+    Title: Squeeze-and-Excitation Networks
+    URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
+Models:
+- Name: legacy_seresnext101_32x4d
+  In Collection: Legacy SE ResNeXt
+  Metadata:
+    FLOPs: 10287698672
+    Parameters: 48960000
+    File Size: 196466866
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnext101_32x4d
+    LR: 0.6
+    Epochs: 100
+    Layers: 101
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L462
+  Weights: http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.23%
+      Top 5 Accuracy: 95.02%
+- Name: legacy_seresnext26_32x4d
+  In Collection: Legacy SE ResNeXt
+  Metadata:
+    FLOPs: 3187342304
+    Parameters: 16790000
+    File Size: 67346327
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnext26_32x4d
+    LR: 0.6
+    Epochs: 100
+    Layers: 26
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L448
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.11%
+      Top 5 Accuracy: 93.31%
+- Name: legacy_seresnext50_32x4d
+  In Collection: Legacy SE ResNeXt
+  Metadata:
+    FLOPs: 5459954352
+    Parameters: 27560000
+    File Size: 110559176
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_seresnext50_32x4d
+    LR: 0.6
+    Epochs: 100
+    Layers: 50
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L455
+  Weights: http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.08%
+      Top 5 Accuracy: 94.43%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Legacy SENet
+  Paper:
+    Title: Squeeze-and-Excitation Networks
+    URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
+Models:
+- Name: legacy_senet154
+  In Collection: Legacy SENet
+  Metadata:
+    FLOPs: 26659556016
+    Parameters: 115090000
+    File Size: 461488402
+    Architecture:
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Max Pooling
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: legacy_senet154
+    LR: 0.6
+    Epochs: 100
+    Layers: 154
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L440
+  Weights: http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.33%
+      Top 5 Accuracy: 95.51%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: MixNet
+  Paper:
+    Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
+    URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
+Models:
+- Name: mixnet_l
+  In Collection: MixNet
+  Metadata:
+    FLOPs: 738671316
+    Parameters: 7330000
+    File Size: 29608232
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: mixnet_l
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1669
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.98%
+      Top 5 Accuracy: 94.18%
+- Name: mixnet_m
+  In Collection: MixNet
+  Metadata:
+    FLOPs: 454543374
+    Parameters: 5010000
+    File Size: 20298347
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: mixnet_m
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1660
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.27%
+      Top 5 Accuracy: 93.42%
+- Name: mixnet_s
+  In Collection: MixNet
+  Metadata:
+    FLOPs: 321264910
+    Parameters: 4130000
+    File Size: 16727982
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: mixnet_s
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1651
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.99%
+      Top 5 Accuracy: 92.79%
+- Name: mixnet_xl
+  In Collection: MixNet
+  Metadata:
+    FLOPs: 1195880424
+    Parameters: 11900000
+    File Size: 48001170
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: mixnet_xl
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1678
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.47%
+      Top 5 Accuracy: 94.93%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: MNASNet
+  Paper:
+    Title: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile'
+    URL: https://paperswithcode.com/paper/mnasnet-platform-aware-neural-architecture
+Models:
+- Name: mnasnet_100
+  In Collection: MNASNet
+  Metadata:
+    FLOPs: 416415488
+    Parameters: 4380000
+    File Size: 17731774
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Inverted Residual Block
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: mnasnet_100
+    Layers: 100
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4000
+    Image Size: '224'
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L894
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 74.67%
+      Top 5 Accuracy: 92.1%
+- Name: semnasnet_100
+  In Collection: MNASNet
+  Metadata:
+    FLOPs: 414570766
+    Parameters: 3890000
+    File Size: 15731489
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Inverted Residual Block
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: semnasnet_100
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L928
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.45%
+      Top 5 Accuracy: 92.61%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: MobileNet V2
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://paperswithcode.com/paper/mobilenetv2-inverted-residuals-and-linear
+Models:
+- Name: mobilenetv2_100
+  In Collection: MobileNet V2
+  Metadata:
+    FLOPs: 401920448
+    Parameters: 3500000
+    File Size: 14202571
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - Inverted Residual Block
+    - Max Pooling
+    - ReLU6
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: mobilenetv2_100
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L955
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.95%
+      Top 5 Accuracy: 91.0%
+- Name: mobilenetv2_110d
+  In Collection: MobileNet V2
+  Metadata:
+    FLOPs: 573958832
+    Parameters: 4520000
+    File Size: 18316431
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - Inverted Residual Block
+    - Max Pooling
+    - ReLU6
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: mobilenetv2_110d
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L969
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.05%
+      Top 5 Accuracy: 92.19%
+- Name: mobilenetv2_120d
+  In Collection: MobileNet V2
+  Metadata:
+    FLOPs: 888510048
+    Parameters: 5830000
+    File Size: 23651121
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - Inverted Residual Block
+    - Max Pooling
+    - ReLU6
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: mobilenetv2_120d
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L977
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.28%
+      Top 5 Accuracy: 93.51%
+- Name: mobilenetv2_140
+  In Collection: MobileNet V2
+  Metadata:
+    FLOPs: 770196784
+    Parameters: 6110000
+    File Size: 24673555
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - Inverted Residual Block
+    - Max Pooling
+    - ReLU6
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: mobilenetv2_140
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L962
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.51%
+      Top 5 Accuracy: 93.0%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: MobileNet V3
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://paperswithcode.com/paper/searching-for-mobilenetv3
+Models:
+- Name: mobilenetv3_large_100
+  In Collection: MobileNet V3
+  Metadata:
+    FLOPs: 287193752
+    Parameters: 5480000
+    File Size: 22076443
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x4 TPU Pod
+    ID: mobilenetv3_large_100
+    LR: 0.1
+    Dropout: 0.8
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L363
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.77%
+      Top 5 Accuracy: 92.54%
+- Name: mobilenetv3_rw
+  In Collection: MobileNet V3
+  Metadata:
+    FLOPs: 287190638
+    Parameters: 5480000
+    File Size: 22064048
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x4 TPU Pod
+    ID: mobilenetv3_rw
+    LR: 0.1
+    Dropout: 0.8
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L384
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.62%
+      Top 5 Accuracy: 92.71%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: NASNet
+  Paper:
+    Title: Learning Transferable Architectures for Scalable Image Recognition
+    URL: https://paperswithcode.com/paper/learning-transferable-architectures-for
+Models:
+- Name: nasnetalarge
+  In Collection: NASNet
+  Metadata:
+    FLOPs: 30242402862
+    Parameters: 88750000
+    File Size: 356056626
+    Architecture:
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 50x Tesla K40 GPUs
+    ID: nasnetalarge
+    Dropout: 0.5
+    Crop Pct: '0.911'
+    Momentum: 0.9
+    Image Size: '331'
+    Interpolation: bicubic
+    Label Smoothing: 0.1
+    RMSProp \\( \epsilon \\): 1.0
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/nasnet.py#L562
+  Weights: http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.63%
+      Top 5 Accuracy: 96.05%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Noisy Student
+  Paper:
+    Title: Self-training with Noisy Student improves ImageNet classification
+    URL: https://paperswithcode.com/paper/self-training-with-noisy-student-improves
+Models:
+- Name: tf_efficientnet_b0_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 488688572
+    Parameters: 5290000
+    File Size: 21386709
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b0_ns
+    LR: 0.128
+    Epochs: 700
+    Dropout: 0.5
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1427
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.66%
+      Top 5 Accuracy: 94.37%
+- Name: tf_efficientnet_b1_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 883633200
+    Parameters: 7790000
+    File Size: 31516408
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b1_ns
+    LR: 0.128
+    Epochs: 700
+    Dropout: 0.5
+    Crop Pct: '0.882'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '240'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1437
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.39%
+      Top 5 Accuracy: 95.74%
+- Name: tf_efficientnet_b2_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 1234321170
+    Parameters: 9110000
+    File Size: 36801803
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b2_ns
+    LR: 0.128
+    Epochs: 700
+    Dropout: 0.5
+    Crop Pct: '0.89'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '260'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1447
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.39%
+      Top 5 Accuracy: 96.24%
+- Name: tf_efficientnet_b3_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 2275247568
+    Parameters: 12230000
+    File Size: 49385734
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b3_ns
+    LR: 0.128
+    Epochs: 700
+    Dropout: 0.5
+    Crop Pct: '0.904'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '300'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1457
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 84.04%
+      Top 5 Accuracy: 96.91%
+- Name: tf_efficientnet_b4_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 5749638672
+    Parameters: 19340000
+    File Size: 77995057
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b4_ns
+    LR: 0.128
+    Epochs: 700
+    Dropout: 0.5
+    Crop Pct: '0.922'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '380'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1467
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 85.15%
+      Top 5 Accuracy: 97.47%
+- Name: tf_efficientnet_b5_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 13176501888
+    Parameters: 30390000
+    File Size: 122404944
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b5_ns
+    LR: 0.128
+    Epochs: 350
+    Dropout: 0.5
+    Crop Pct: '0.934'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '456'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1477
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 86.08%
+      Top 5 Accuracy: 97.75%
+- Name: tf_efficientnet_b6_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 24180518488
+    Parameters: 43040000
+    File Size: 173239537
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b6_ns
+    LR: 0.128
+    Epochs: 350
+    Dropout: 0.5
+    Crop Pct: '0.942'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '528'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1487
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 86.45%
+      Top 5 Accuracy: 97.88%
+- Name: tf_efficientnet_b7_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 48205304880
+    Parameters: 66349999
+    File Size: 266853140
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    ID: tf_efficientnet_b7_ns
+    LR: 0.128
+    Epochs: 350
+    Dropout: 0.5
+    Crop Pct: '0.949'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '600'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1498
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 86.83%
+      Top 5 Accuracy: 98.08%
+- Name: tf_efficientnet_l2_ns
+  In Collection: Noisy Student
+  Metadata:
+    FLOPs: 611646113804
+    Parameters: 480310000
+    File Size: 1925950424
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: Cloud TPU v3 Pod
+    Training Time: 6 days
+    ID: tf_efficientnet_l2_ns
+    LR: 0.128
+    Epochs: 350
+    Dropout: 0.5
+    Crop Pct: '0.96'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '800'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1520
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 88.35%
+      Top 5 Accuracy: 98.66%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: PNASNet
+  Paper:
+    Title: Progressive Neural Architecture Search
+    URL: https://paperswithcode.com/paper/progressive-neural-architecture-search
+Models:
+- Name: pnasnet5large
+  In Collection: PNASNet
+  Metadata:
+    FLOPs: 31458865950
+    Parameters: 86060000
+    File Size: 345153926
+    Architecture:
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 100x NVIDIA P100 GPUs
+    ID: pnasnet5large
+    LR: 0.015
+    Dropout: 0.5
+    Crop Pct: '0.911'
+    Momentum: 0.9
+    Batch Size: 1600
+    Image Size: '331'
+    Interpolation: bicubic
+    Label Smoothing: 0.1
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/pnasnet.py#L343
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 0.98%
+      Top 5 Accuracy: 18.58%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: RegNetX
+  Paper:
+    Title: Designing Network Design Spaces
+    URL: https://paperswithcode.com/paper/designing-network-design-spaces
+Models:
+- Name: regnetx_002
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 255276032
+    Parameters: 2680000
+    File Size: 10862199
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_002
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L337
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 68.75%
+      Top 5 Accuracy: 88.56%
+- Name: regnetx_004
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 510619136
+    Parameters: 5160000
+    File Size: 20841309
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_004
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L343
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.39%
+      Top 5 Accuracy: 90.82%
+- Name: regnetx_006
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 771659136
+    Parameters: 6200000
+    File Size: 24965172
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_006
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L349
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 73.84%
+      Top 5 Accuracy: 91.68%
+- Name: regnetx_008
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 1027038208
+    Parameters: 7260000
+    File Size: 29235944
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_008
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L355
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.05%
+      Top 5 Accuracy: 92.34%
+- Name: regnetx_016
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 2059337856
+    Parameters: 9190000
+    File Size: 36988158
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_016
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L361
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.95%
+      Top 5 Accuracy: 93.43%
+- Name: regnetx_032
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 4082555904
+    Parameters: 15300000
+    File Size: 61509573
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_032
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L367
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.15%
+      Top 5 Accuracy: 94.09%
+- Name: regnetx_040
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 5095167744
+    Parameters: 22120000
+    File Size: 88844824
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_040
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L373
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.48%
+      Top 5 Accuracy: 94.25%
+- Name: regnetx_064
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 8303405824
+    Parameters: 26210000
+    File Size: 105184854
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_064
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L379
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.06%
+      Top 5 Accuracy: 94.47%
+- Name: regnetx_080
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 10276726784
+    Parameters: 39570000
+    File Size: 158720042
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_080
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L385
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.21%
+      Top 5 Accuracy: 94.55%
+- Name: regnetx_120
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 15536378368
+    Parameters: 46110000
+    File Size: 184866342
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_120
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L391
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.61%
+      Top 5 Accuracy: 94.73%
+- Name: regnetx_160
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 20491740672
+    Parameters: 54280000
+    File Size: 217623862
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_160
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L397
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.84%
+      Top 5 Accuracy: 94.82%
+- Name: regnetx_320
+  In Collection: RegNetX
+  Metadata:
+    FLOPs: 40798958592
+    Parameters: 107810000
+    File Size: 431962133
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnetx_320
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L403
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.25%
+      Top 5 Accuracy: 95.03%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: RegNetY
+  Paper:
+    Title: Designing Network Design Spaces
+    URL: https://paperswithcode.com/paper/designing-network-design-spaces
+Models:
+- Name: regnety_002
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 255754236
+    Parameters: 3160000
+    File Size: 12782926
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_002
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L409
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 70.28%
+      Top 5 Accuracy: 89.55%
+- Name: regnety_004
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 515664568
+    Parameters: 4340000
+    File Size: 17542753
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_004
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L415
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 74.02%
+      Top 5 Accuracy: 91.76%
+- Name: regnety_006
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 771746928
+    Parameters: 6060000
+    File Size: 24394127
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_006
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L421
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.27%
+      Top 5 Accuracy: 92.53%
+- Name: regnety_008
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 1023448952
+    Parameters: 6260000
+    File Size: 25223268
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_008
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L427
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.32%
+      Top 5 Accuracy: 93.07%
+- Name: regnety_016
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 2070895094
+    Parameters: 11200000
+    File Size: 45115589
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_016
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L433
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.87%
+      Top 5 Accuracy: 93.73%
+- Name: regnety_032
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 4081118714
+    Parameters: 19440000
+    File Size: 78084523
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_032
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L439
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.01%
+      Top 5 Accuracy: 95.91%
+- Name: regnety_040
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 5105933432
+    Parameters: 20650000
+    File Size: 82913909
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_040
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L445
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.23%
+      Top 5 Accuracy: 94.64%
+- Name: regnety_064
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 8167730444
+    Parameters: 30580000
+    File Size: 122751416
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_064
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L451
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.73%
+      Top 5 Accuracy: 94.76%
+- Name: regnety_080
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 10233621420
+    Parameters: 39180000
+    File Size: 157124671
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_080
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L457
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.87%
+      Top 5 Accuracy: 94.83%
+- Name: regnety_120
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 15542094856
+    Parameters: 51820000
+    File Size: 207743949
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_120
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L463
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.38%
+      Top 5 Accuracy: 95.12%
+- Name: regnety_160
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 20450196852
+    Parameters: 83590000
+    File Size: 334916722
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_160
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 512
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L469
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.28%
+      Top 5 Accuracy: 94.97%
+- Name: regnety_320
+  In Collection: RegNetY
+  Metadata:
+    FLOPs: 41492618394
+    Parameters: 145050000
+    File Size: 580891965
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - ReLU
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA V100 GPUs
+    ID: regnety_320
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 5.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L475
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.8%
+      Top 5 Accuracy: 95.25%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Res2Net
+  Paper:
+    Title: 'Res2Net: A New Multi-scale Backbone Architecture'
+    URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
+Models:
+- Name: res2net101_26w_4s
+  In Collection: Res2Net
+  Metadata:
+    FLOPs: 10415881200
+    Parameters: 45210000
+    File Size: 181456059
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2Net Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2net101_26w_4s
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L152
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.19%
+      Top 5 Accuracy: 94.43%
+- Name: res2net50_14w_8s
+  In Collection: Res2Net
+  Metadata:
+    FLOPs: 5403546768
+    Parameters: 25060000
+    File Size: 100638543
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2Net Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2net50_14w_8s
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L196
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.14%
+      Top 5 Accuracy: 93.86%
+- Name: res2net50_26w_4s
+  In Collection: Res2Net
+  Metadata:
+    FLOPs: 5499974064
+    Parameters: 25700000
+    File Size: 103110087
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2Net Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2net50_26w_4s
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L141
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.99%
+      Top 5 Accuracy: 93.85%
+- Name: res2net50_26w_6s
+  In Collection: Res2Net
+  Metadata:
+    FLOPs: 8130156528
+    Parameters: 37050000
+    File Size: 148603239
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2Net Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2net50_26w_6s
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L163
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.57%
+      Top 5 Accuracy: 94.12%
+- Name: res2net50_26w_8s
+  In Collection: Res2Net
+  Metadata:
+    FLOPs: 10760338992
+    Parameters: 48400000
+    File Size: 194085165
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2Net Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2net50_26w_8s
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L174
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.19%
+      Top 5 Accuracy: 94.37%
+- Name: res2net50_48w_2s
+  In Collection: Res2Net
+  Metadata:
+    FLOPs: 5375291520
+    Parameters: 25290000
+    File Size: 101421406
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2Net Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2net50_48w_2s
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L185
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.53%
+      Top 5 Accuracy: 93.56%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Res2NeXt
+  Paper:
+    Title: 'Res2Net: A New Multi-scale Backbone Architecture'
+    URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
+Models:
+- Name: res2next50
+  In Collection: Res2NeXt
+  Metadata:
+    FLOPs: 5396798208
+    Parameters: 24670000
+    File Size: 99019592
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - ReLU
+    - Res2NeXt Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x Titan Xp GPUs
+    ID: res2next50
+    LR: 0.1
+    Epochs: 100
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L207
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.24%
+      Top 5 Accuracy: 93.91%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: ResNet
+  Paper:
+    Title: Deep Residual Learning for Image Recognition
+    URL: https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition
+Models:
+- Name: resnet18
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 2337073152
+    Parameters: 11690000
+    File Size: 46827520
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnet18
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L641
+  Weights: https://download.pytorch.org/models/resnet18-5c106cde.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 69.74%
+      Top 5 Accuracy: 89.09%
+- Name: resnet26
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 3026804736
+    Parameters: 16000000
+    File Size: 64129972
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnet26
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L675
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.29%
+      Top 5 Accuracy: 92.57%
+- Name: resnet34
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 4718469120
+    Parameters: 21800000
+    File Size: 87290831
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnet34
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L658
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.11%
+      Top 5 Accuracy: 92.28%
+- Name: resnet50
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 5282531328
+    Parameters: 25560000
+    File Size: 102488165
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnet50
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L691
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.04%
+      Top 5 Accuracy: 94.39%
+- Name: resnetblur50
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 6621606912
+    Parameters: 25560000
+    File Size: 102488165
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Blur Pooling
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnetblur50
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L1160
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.29%
+      Top 5 Accuracy: 94.64%
+- Name: tv_resnet101
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 10068547584
+    Parameters: 44550000
+    File Size: 178728960
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tv_resnet101
+    LR: 0.1
+    Epochs: 90
+    Crop Pct: '0.875'
+    LR Gamma: 0.1
+    Momentum: 0.9
+    Batch Size: 32
+    Image Size: '224'
+    LR Step Size: 30
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L761
+  Weights: https://download.pytorch.org/models/resnet101-5d3b4d8f.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.37%
+      Top 5 Accuracy: 93.56%
+- Name: tv_resnet152
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 14857660416
+    Parameters: 60190000
+    File Size: 241530880
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tv_resnet152
+    LR: 0.1
+    Epochs: 90
+    Crop Pct: '0.875'
+    LR Gamma: 0.1
+    Momentum: 0.9
+    Batch Size: 32
+    Image Size: '224'
+    LR Step Size: 30
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L769
+  Weights: https://download.pytorch.org/models/resnet152-b121ed2d.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.32%
+      Top 5 Accuracy: 94.05%
+- Name: tv_resnet34
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 4718469120
+    Parameters: 21800000
+    File Size: 87306240
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tv_resnet34
+    LR: 0.1
+    Epochs: 90
+    Crop Pct: '0.875'
+    LR Gamma: 0.1
+    Momentum: 0.9
+    Batch Size: 32
+    Image Size: '224'
+    LR Step Size: 30
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L745
+  Weights: https://download.pytorch.org/models/resnet34-333f7ec4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 73.3%
+      Top 5 Accuracy: 91.42%
+- Name: tv_resnet50
+  In Collection: ResNet
+  Metadata:
+    FLOPs: 5282531328
+    Parameters: 25560000
+    File Size: 102502400
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tv_resnet50
+    LR: 0.1
+    Epochs: 90
+    Crop Pct: '0.875'
+    LR Gamma: 0.1
+    Momentum: 0.9
+    Batch Size: 32
+    Image Size: '224'
+    LR Step Size: 30
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L753
+  Weights: https://download.pytorch.org/models/resnet50-19c8e357.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.16%
+      Top 5 Accuracy: 92.88%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: ResNeXt
+  Paper:
+    Title: Aggregated Residual Transformations for Deep Neural Networks
+    URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
+Models:
+- Name: resnext101_32x8d
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 21180417024
+    Parameters: 88790000
+    File Size: 356082095
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnext101_32x8d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L877
+  Weights: https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.3%
+      Top 5 Accuracy: 94.53%
+- Name: resnext50_32x4d
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 5472648192
+    Parameters: 25030000
+    File Size: 100435887
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnext50_32x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L851
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.79%
+      Top 5 Accuracy: 94.61%
+- Name: resnext50d_32x4d
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 5781119488
+    Parameters: 25050000
+    File Size: 100515304
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: resnext50d_32x4d
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L869
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.67%
+      Top 5 Accuracy: 94.87%
+- Name: tv_resnext50_32x4d
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 5472648192
+    Parameters: 25030000
+    File Size: 100441675
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tv_resnext50_32x4d
+    LR: 0.1
+    Epochs: 90
+    Crop Pct: '0.875'
+    LR Gamma: 0.1
+    Momentum: 0.9
+    Batch Size: 32
+    Image Size: '224'
+    LR Step Size: 30
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L842
+  Weights: https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.61%
+      Top 5 Accuracy: 93.68%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SE ResNet
+  Paper:
+    Title: Squeeze-and-Excitation Networks
+    URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
+Models:
+- Name: seresnet152d
+  In Collection: SE ResNet
+  Metadata:
+    FLOPs: 20161904304
+    Parameters: 66840000
+    File Size: 268144497
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: seresnet152d
+    LR: 0.6
+    Epochs: 100
+    Layers: 152
+    Dropout: 0.2
+    Crop Pct: '0.94'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '256'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1206
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 83.74%
+      Top 5 Accuracy: 96.77%
+- Name: seresnet50
+  In Collection: SE ResNet
+  Metadata:
+    FLOPs: 5285062320
+    Parameters: 28090000
+    File Size: 112621903
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA Titan X GPUs
+    ID: seresnet50
+    LR: 0.6
+    Epochs: 100
+    Layers: 50
+    Dropout: 0.2
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 1024
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1180
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.26%
+      Top 5 Accuracy: 95.07%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SelecSLS
+  Paper:
+    Title: 'XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera'
+    URL: https://paperswithcode.com/paper/xnect-real-time-multi-person-3d-human-pose
+Models:
+- Name: selecsls42b
+  In Collection: SelecSLS
+  Metadata:
+    FLOPs: 3824022528
+    Parameters: 32460000
+    File Size: 129948954
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - ReLU
+    - SelecSLS Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Cosine Annealing
+    - Random Erasing
+    Training Data:
+    - ImageNet
+    ID: selecsls42b
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L335
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.18%
+      Top 5 Accuracy: 93.39%
+- Name: selecsls60
+  In Collection: SelecSLS
+  Metadata:
+    FLOPs: 4610472600
+    Parameters: 30670000
+    File Size: 122839714
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - ReLU
+    - SelecSLS Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Cosine Annealing
+    - Random Erasing
+    Training Data:
+    - ImageNet
+    ID: selecsls60
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L342
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.99%
+      Top 5 Accuracy: 93.83%
+- Name: selecsls60b
+  In Collection: SelecSLS
+  Metadata:
+    FLOPs: 4657653144
+    Parameters: 32770000
+    File Size: 131252898
+    Architecture:
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - ReLU
+    - SelecSLS Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - Cosine Annealing
+    - Random Erasing
+    Training Data:
+    - ImageNet
+    ID: selecsls60b
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L349
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.41%
+      Top 5 Accuracy: 94.18%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SKResNet
+  Paper:
+    Title: Selective Kernel Networks
+    URL: https://paperswithcode.com/paper/selective-kernel-networks
+Models:
+- Name: skresnet18
+  In Collection: SKResNet
+  Metadata:
+    FLOPs: 2333467136
+    Parameters: 11960000
+    File Size: 47923238
+    Architecture:
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Max Pooling
+    - Residual Connection
+    - Selective Kernel
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x GPUs
+    ID: skresnet18
+    LR: 0.1
+    Epochs: 100
+    Layers: 18
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L148
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 73.03%
+      Top 5 Accuracy: 91.17%
+- Name: skresnet34
+  In Collection: SKResNet
+  Metadata:
+    FLOPs: 4711849952
+    Parameters: 22280000
+    File Size: 89299314
+    Architecture:
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Max Pooling
+    - Residual Connection
+    - Selective Kernel
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x GPUs
+    ID: skresnet34
+    LR: 0.1
+    Epochs: 100
+    Layers: 34
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L165
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.93%
+      Top 5 Accuracy: 93.32%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SKResNeXt
+  Paper:
+    Title: Selective Kernel Networks
+    URL: https://paperswithcode.com/paper/selective-kernel-networks
+Models:
+- Name: skresnext50_32x4d
+  In Collection: SKResNeXt
+  Metadata:
+    FLOPs: 5739845824
+    Parameters: 27480000
+    File Size: 110340975
+    Architecture:
+    - Convolution
+    - Dense Connections
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - Residual Connection
+    - Selective Kernel
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    Training Resources: 8x GPUs
+    ID: skresnext50_32x4d
+    LR: 0.1
+    Epochs: 100
+    Layers: 50
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 256
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L210
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.15%
+      Top 5 Accuracy: 94.64%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SPNASNet
+  Paper:
+    Title: 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4
+      Hours'
+    URL: https://paperswithcode.com/paper/single-path-nas-designing-hardware-efficient
+Models:
+- Name: spnasnet_100
+  In Collection: SPNASNet
+  Metadata:
+    FLOPs: 442385600
+    Parameters: 4420000
+    File Size: 17902337
+    Architecture:
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Depthwise Separable Convolution
+    - Dropout
+    - ReLU
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: spnasnet_100
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L995
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 74.08%
+      Top 5 Accuracy: 91.82%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SSL ResNet
+  Paper:
+    Title: Billion-scale semi-supervised learning for image classification
+    URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
+Models:
+- Name: ssl_resnet18
+  In Collection: SSL ResNet
+  Metadata:
+    FLOPs: 2337073152
+    Parameters: 11690000
+    File Size: 46811375
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - YFCC-100M
+    Training Resources: 64x GPUs
+    ID: ssl_resnet18
+    LR: 0.0015
+    Epochs: 30
+    Layers: 18
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L894
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.62%
+      Top 5 Accuracy: 91.42%
+- Name: ssl_resnet50
+  In Collection: SSL ResNet
+  Metadata:
+    FLOPs: 5282531328
+    Parameters: 25560000
+    File Size: 102480594
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - YFCC-100M
+    Training Resources: 64x GPUs
+    ID: ssl_resnet50
+    LR: 0.0015
+    Epochs: 30
+    Layers: 50
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L904
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.24%
+      Top 5 Accuracy: 94.83%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SWSL ResNet
+  Paper:
+    Title: Billion-scale semi-supervised learning for image classification
+    URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
+Models:
+- Name: swsl_resnet18
+  In Collection: SWSL ResNet
+  Metadata:
+    FLOPs: 2337073152
+    Parameters: 11690000
+    File Size: 46811375
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - IG-1B-Targeted
+    - ImageNet
+    Training Resources: 64x GPUs
+    ID: swsl_resnet18
+    LR: 0.0015
+    Epochs: 30
+    Layers: 18
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L954
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 73.28%
+      Top 5 Accuracy: 91.76%
+- Name: swsl_resnet50
+  In Collection: SWSL ResNet
+  Metadata:
+    FLOPs: 5282531328
+    Parameters: 25560000
+    File Size: 102480594
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Bottleneck Residual Block
+    - Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - IG-1B-Targeted
+    - ImageNet
+    Training Resources: 64x GPUs
+    ID: swsl_resnet50
+    LR: 0.0015
+    Epochs: 30
+    Layers: 50
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L965
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.14%
+      Top 5 Accuracy: 95.97%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: SWSL ResNext
+  Paper:
+    Title: Billion-scale semi-supervised learning for image classification
+    URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
+Models:
+- Name: swsl_resnext101_32x16d
+  In Collection: SWSL ResNext
+  Metadata:
+    FLOPs: 46623691776
+    Parameters: 194030000
+    File Size: 777518664
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - IG-1B-Targeted
+    - ImageNet
+    Training Resources: 64x GPUs
+    ID: swsl_resnext101_32x16d
+    LR: 0.0015
+    Epochs: 30
+    Layers: 101
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L1009
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 83.34%
+      Top 5 Accuracy: 96.84%
+- Name: swsl_resnext101_32x4d
+  In Collection: SWSL ResNext
+  Metadata:
+    FLOPs: 10298145792
+    Parameters: 44180000
+    File Size: 177341913
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - IG-1B-Targeted
+    - ImageNet
+    Training Resources: 64x GPUs
+    ID: swsl_resnext101_32x4d
+    LR: 0.0015
+    Epochs: 30
+    Layers: 101
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L987
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 83.22%
+      Top 5 Accuracy: 96.77%
+- Name: swsl_resnext101_32x8d
+  In Collection: SWSL ResNext
+  Metadata:
+    FLOPs: 21180417024
+    Parameters: 88790000
+    File Size: 356056638
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - IG-1B-Targeted
+    - ImageNet
+    Training Resources: 64x GPUs
+    ID: swsl_resnext101_32x8d
+    LR: 0.0015
+    Epochs: 30
+    Layers: 101
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L998
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 84.27%
+      Top 5 Accuracy: 97.17%
+- Name: swsl_resnext50_32x4d
+  In Collection: SWSL ResNext
+  Metadata:
+    FLOPs: 5472648192
+    Parameters: 25030000
+    File Size: 100428550
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Global Average Pooling
+    - Grouped Convolution
+    - Max Pooling
+    - ReLU
+    - ResNeXt Block
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - IG-1B-Targeted
+    - ImageNet
+    Training Resources: 64x GPUs
+    ID: swsl_resnext50_32x4d
+    LR: 0.0015
+    Epochs: 30
+    Layers: 50
+    Crop Pct: '0.875'
+    Batch Size: 1536
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L976
+  Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.17%
+      Top 5 Accuracy: 96.23%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: TF EfficientNet Lite
+  Paper:
+    Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
+    URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
+Models:
+- Name: tf_efficientnet_lite0
+  In Collection: TF EfficientNet Lite
+  Metadata:
+    FLOPs: 488052032
+    Parameters: 4650000
+    File Size: 18820223
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - RELU6
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_lite0
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1596
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 74.83%
+      Top 5 Accuracy: 92.17%
+- Name: tf_efficientnet_lite1
+  In Collection: TF EfficientNet Lite
+  Metadata:
+    FLOPs: 773639520
+    Parameters: 5420000
+    File Size: 21939331
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - RELU6
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_lite1
+    Crop Pct: '0.882'
+    Image Size: '240'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1607
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.67%
+      Top 5 Accuracy: 93.24%
+- Name: tf_efficientnet_lite2
+  In Collection: TF EfficientNet Lite
+  Metadata:
+    FLOPs: 1068494432
+    Parameters: 6090000
+    File Size: 24658687
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - RELU6
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_lite2
+    Crop Pct: '0.89'
+    Image Size: '260'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1618
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.48%
+      Top 5 Accuracy: 93.75%
+- Name: tf_efficientnet_lite3
+  In Collection: TF EfficientNet Lite
+  Metadata:
+    FLOPs: 2011534304
+    Parameters: 8199999
+    File Size: 33161413
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - RELU6
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_lite3
+    Crop Pct: '0.904'
+    Image Size: '300'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1629
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.83%
+      Top 5 Accuracy: 94.91%
+- Name: tf_efficientnet_lite4
+  In Collection: TF EfficientNet Lite
+  Metadata:
+    FLOPs: 5164802912
+    Parameters: 13010000
+    File Size: 52558819
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - RELU6
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_lite4
+    Crop Pct: '0.92'
+    Image Size: '380'
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1640
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.54%
+      Top 5 Accuracy: 95.66%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: TF EfficientNet
+  Paper:
+    Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
+    URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
+Models:
+- Name: tf_efficientnet_b0
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 488688572
+    Parameters: 5290000
+    File Size: 21383997
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: TPUv3 Cloud TPU
+    ID: tf_efficientnet_b0
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1241
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.85%
+      Top 5 Accuracy: 93.23%
+- Name: tf_efficientnet_b1
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 883633200
+    Parameters: 7790000
+    File Size: 31512534
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b1
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.882'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '240'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1251
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.84%
+      Top 5 Accuracy: 94.2%
+- Name: tf_efficientnet_b2
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 1234321170
+    Parameters: 9110000
+    File Size: 36797929
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b2
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.89'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '260'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1261
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.07%
+      Top 5 Accuracy: 94.9%
+- Name: tf_efficientnet_b3
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 2275247568
+    Parameters: 12230000
+    File Size: 49381362
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b3
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.904'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '300'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1271
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.65%
+      Top 5 Accuracy: 95.72%
+- Name: tf_efficientnet_b4
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 5749638672
+    Parameters: 19340000
+    File Size: 77989689
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: TPUv3 Cloud TPU
+    ID: tf_efficientnet_b4
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.922'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '380'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1281
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 83.03%
+      Top 5 Accuracy: 96.3%
+- Name: tf_efficientnet_b5
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 13176501888
+    Parameters: 30390000
+    File Size: 122403150
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b5
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.934'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '456'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1291
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 83.81%
+      Top 5 Accuracy: 96.75%
+- Name: tf_efficientnet_b6
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 24180518488
+    Parameters: 43040000
+    File Size: 173232007
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b6
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.942'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '528'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1301
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 84.11%
+      Top 5 Accuracy: 96.89%
+- Name: tf_efficientnet_b7
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 48205304880
+    Parameters: 66349999
+    File Size: 266850607
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b7
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.949'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '600'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1312
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 84.93%
+      Top 5 Accuracy: 97.2%
+- Name: tf_efficientnet_b8
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 80962956270
+    Parameters: 87410000
+    File Size: 351379853
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Label Smoothing
+    - RMSProp
+    - Stochastic Depth
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_b8
+    LR: 0.256
+    Epochs: 350
+    Crop Pct: '0.954'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '672'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1323
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 85.35%
+      Top 5 Accuracy: 97.39%
+- Name: tf_efficientnet_el
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 9356616096
+    Parameters: 10590000
+    File Size: 42800271
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_el
+    Crop Pct: '0.904'
+    Image Size: '300'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1551
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.45%
+      Top 5 Accuracy: 95.17%
+- Name: tf_efficientnet_em
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 3636607040
+    Parameters: 6900000
+    File Size: 27933644
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_em
+    Crop Pct: '0.882'
+    Image Size: '240'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1541
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.71%
+      Top 5 Accuracy: 94.33%
+- Name: tf_efficientnet_es
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 2057577472
+    Parameters: 5440000
+    File Size: 22008479
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: tf_efficientnet_es
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1531
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.28%
+      Top 5 Accuracy: 93.6%
+- Name: tf_efficientnet_l2_ns_475
+  In Collection: TF EfficientNet
+  Metadata:
+    FLOPs: 217795669644
+    Parameters: 480310000
+    File Size: 1925950424
+    Architecture:
+    - 1x1 Convolution
+    - Average Pooling
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Dropout
+    - Inverted Residual Block
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - FixRes
+    - Label Smoothing
+    - Noisy Student
+    - RMSProp
+    - RandAugment
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    - JFT-300M
+    Training Resources: TPUv3 Cloud TPU
+    ID: tf_efficientnet_l2_ns_475
+    LR: 0.128
+    Epochs: 350
+    Dropout: 0.5
+    Crop Pct: '0.936'
+    Momentum: 0.9
+    Batch Size: 2048
+    Image Size: '475'
+    Weight Decay: 1.0e-05
+    Interpolation: bicubic
+    RMSProp Decay: 0.9
+    Label Smoothing: 0.1
+    BatchNorm Momentum: 0.99
+    Stochastic Depth Survival: 0.8
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1509
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 88.24%
+      Top 5 Accuracy: 98.55%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: TF MixNet
+  Paper:
+    Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
+    URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
+Models:
+- Name: tf_mixnet_l
+  In Collection: TF MixNet
+  Metadata:
+    FLOPs: 688674516
+    Parameters: 7330000
+    File Size: 29620756
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: tf_mixnet_l
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1720
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.78%
+      Top 5 Accuracy: 94.0%
+- Name: tf_mixnet_m
+  In Collection: TF MixNet
+  Metadata:
+    FLOPs: 416633502
+    Parameters: 5010000
+    File Size: 20310871
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: tf_mixnet_m
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1709
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 76.96%
+      Top 5 Accuracy: 93.16%
+- Name: tf_mixnet_s
+  In Collection: TF MixNet
+  Metadata:
+    FLOPs: 302587678
+    Parameters: 4130000
+    File Size: 16738218
+    Architecture:
+    - Batch Normalization
+    - Dense Connections
+    - Dropout
+    - Global Average Pooling
+    - Grouped Convolution
+    - MixConv
+    - Squeeze-and-Excitation Block
+    - Swish
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - MNAS
+    Training Data:
+    - ImageNet
+    ID: tf_mixnet_s
+    Crop Pct: '0.875'
+    Image Size: '224'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1698
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.68%
+      Top 5 Accuracy: 92.64%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: TF MobileNet V3
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://paperswithcode.com/paper/searching-for-mobilenetv3
+Models:
+- Name: tf_mobilenetv3_large_075
+  In Collection: TF MobileNet V3
+  Metadata:
+    FLOPs: 194323712
+    Parameters: 3990000
+    File Size: 16097377
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x4 TPU Pod
+    ID: tf_mobilenetv3_large_075
+    LR: 0.1
+    Dropout: 0.8
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L394
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 73.45%
+      Top 5 Accuracy: 91.34%
+- Name: tf_mobilenetv3_large_100
+  In Collection: TF MobileNet V3
+  Metadata:
+    FLOPs: 274535288
+    Parameters: 5480000
+    File Size: 22076649
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x4 TPU Pod
+    ID: tf_mobilenetv3_large_100
+    LR: 0.1
+    Dropout: 0.8
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L403
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 75.51%
+      Top 5 Accuracy: 92.61%
+- Name: tf_mobilenetv3_large_minimal_100
+  In Collection: TF MobileNet V3
+  Metadata:
+    FLOPs: 267216928
+    Parameters: 3920000
+    File Size: 15836368
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 4x4 TPU Pod
+    ID: tf_mobilenetv3_large_minimal_100
+    LR: 0.1
+    Dropout: 0.8
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 1.0e-05
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L412
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.24%
+      Top 5 Accuracy: 90.64%
+- Name: tf_mobilenetv3_small_075
+  In Collection: TF MobileNet V3
+  Metadata:
+    FLOPs: 48457664
+    Parameters: 2040000
+    File Size: 8242701
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: tf_mobilenetv3_small_075
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bilinear
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L421
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 65.72%
+      Top 5 Accuracy: 86.13%
+- Name: tf_mobilenetv3_small_100
+  In Collection: TF MobileNet V3
+  Metadata:
+    FLOPs: 65450600
+    Parameters: 2540000
+    File Size: 10256398
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: tf_mobilenetv3_small_100
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bilinear
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L430
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 67.92%
+      Top 5 Accuracy: 87.68%
+- Name: tf_mobilenetv3_small_minimal_100
+  In Collection: TF MobileNet V3
+  Metadata:
+    FLOPs: 60827936
+    Parameters: 2040000
+    File Size: 8258083
+    Architecture:
+    - 1x1 Convolution
+    - Batch Normalization
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Dropout
+    - Global Average Pooling
+    - Hard Swish
+    - Inverted Residual Block
+    - ReLU
+    - Residual Connection
+    - Softmax
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - RMSProp
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 16x GPUs
+    ID: tf_mobilenetv3_small_minimal_100
+    LR: 0.045
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Batch Size: 4096
+    Image Size: '224'
+    Weight Decay: 4.0e-05
+    Interpolation: bilinear
+    RMSProp Decay: 0.9
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L439
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 62.91%
+      Top 5 Accuracy: 84.24%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: TResNet
+  Paper:
+    Title: 'TResNet: High Performance GPU-Dedicated Architecture'
+    URL: https://paperswithcode.com/paper/tresnet-high-performance-gpu-dedicated
+Models:
+- Name: tresnet_l
+  In Collection: TResNet
+  Metadata:
+    FLOPs: 10873416792
+    Parameters: 53456696
+    File Size: 224440219
+    Architecture:
+    - 1x1 Convolution
+    - Anti-Alias Downsampling
+    - Convolution
+    - Global Average Pooling
+    - InPlace-ABN
+    - Leaky ReLU
+    - ReLU
+    - Residual Connection
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Cutout
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA 100 GPUs
+    ID: tresnet_l
+    LR: 0.01
+    Epochs: 300
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L267
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.49%
+      Top 5 Accuracy: 95.62%
+- Name: tresnet_l_448
+  In Collection: TResNet
+  Metadata:
+    FLOPs: 43488238584
+    Parameters: 53456696
+    File Size: 224440219
+    Architecture:
+    - 1x1 Convolution
+    - Anti-Alias Downsampling
+    - Convolution
+    - Global Average Pooling
+    - InPlace-ABN
+    - Leaky ReLU
+    - ReLU
+    - Residual Connection
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Cutout
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA 100 GPUs
+    ID: tresnet_l_448
+    LR: 0.01
+    Epochs: 300
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '448'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L285
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.26%
+      Top 5 Accuracy: 95.98%
+- Name: tresnet_m
+  In Collection: TResNet
+  Metadata:
+    FLOPs: 5733048064
+    Parameters: 41282200
+    File Size: 125861314
+    Architecture:
+    - 1x1 Convolution
+    - Anti-Alias Downsampling
+    - Convolution
+    - Global Average Pooling
+    - InPlace-ABN
+    - Leaky ReLU
+    - ReLU
+    - Residual Connection
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Cutout
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA 100 GPUs
+    Training Time: < 24 hours
+    ID: tresnet_m
+    LR: 0.01
+    Epochs: 300
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L261
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_80_8-dbc13962.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 80.8%
+      Top 5 Accuracy: 94.86%
+- Name: tresnet_m_448
+  In Collection: TResNet
+  Metadata:
+    FLOPs: 22929743104
+    Parameters: 29278464
+    File Size: 125861314
+    Architecture:
+    - 1x1 Convolution
+    - Anti-Alias Downsampling
+    - Convolution
+    - Global Average Pooling
+    - InPlace-ABN
+    - Leaky ReLU
+    - ReLU
+    - Residual Connection
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Cutout
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA 100 GPUs
+    ID: tresnet_m_448
+    LR: 0.01
+    Epochs: 300
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '448'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L279
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 81.72%
+      Top 5 Accuracy: 95.57%
+- Name: tresnet_xl
+  In Collection: TResNet
+  Metadata:
+    FLOPs: 15162534034
+    Parameters: 75646610
+    File Size: 314378965
+    Architecture:
+    - 1x1 Convolution
+    - Anti-Alias Downsampling
+    - Convolution
+    - Global Average Pooling
+    - InPlace-ABN
+    - Leaky ReLU
+    - ReLU
+    - Residual Connection
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Cutout
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA 100 GPUs
+    ID: tresnet_xl
+    LR: 0.01
+    Epochs: 300
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '224'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L273
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 82.05%
+      Top 5 Accuracy: 95.93%
+- Name: tresnet_xl_448
+  In Collection: TResNet
+  Metadata:
+    FLOPs: 60641712730
+    Parameters: 75646610
+    File Size: 224440219
+    Architecture:
+    - 1x1 Convolution
+    - Anti-Alias Downsampling
+    - Convolution
+    - Global Average Pooling
+    - InPlace-ABN
+    - Leaky ReLU
+    - ReLU
+    - Residual Connection
+    - Squeeze-and-Excitation Block
+    Tasks:
+    - Image Classification
+    Training Techniques:
+    - AutoAugment
+    - Cutout
+    - Label Smoothing
+    - SGD with Momentum
+    - Weight Decay
+    Training Data:
+    - ImageNet
+    Training Resources: 8x NVIDIA 100 GPUs
+    ID: tresnet_xl_448
+    LR: 0.01
+    Epochs: 300
+    Crop Pct: '0.875'
+    Momentum: 0.9
+    Image Size: '448'
+    Weight Decay: 0.0001
+    Interpolation: bilinear
+  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L291
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 83.06%
+      Top 5 Accuracy: 96.19%
+-->

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}
+}
+```
+
+<!--
+Type: model-index
+Collections:
+- Name: Xception
+  Paper:
+    Title: 'Xception: Deep Learning with Depthwise Separable Convolutions'
+    URL: https://paperswithcode.com/paper/xception-deep-learning-with-depthwise
+Models:
+- Name: xception
+  In Collection: Xception
+  Metadata:
+    FLOPs: 10600506792
+    Parameters: 22860000
+    File Size: 91675053
+    Architecture:
+    - 1x1 Convolution
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: xception
+    Crop Pct: '0.897'
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception.py#L229
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.05%
+      Top 5 Accuracy: 94.4%
+- Name: xception41
+  In Collection: Xception
+  Metadata:
+    FLOPs: 11681983232
+    Parameters: 26970000
+    File Size: 108422028
+    Architecture:
+    - 1x1 Convolution
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: xception41
+    Crop Pct: '0.903'
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L181
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_41-e6439c97.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.54%
+      Top 5 Accuracy: 94.28%
+- Name: xception65
+  In Collection: Xception
+  Metadata:
+    FLOPs: 17585702144
+    Parameters: 39920000
+    File Size: 160536780
+    Architecture:
+    - 1x1 Convolution
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: xception65
+    Crop Pct: '0.903'
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L200
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_65-c9ae96e8.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.55%
+      Top 5 Accuracy: 94.66%
+- Name: xception71
+  In Collection: Xception
+  Metadata:
+    FLOPs: 22817346560
+    Parameters: 42340000
+    File Size: 170295556
+    Architecture:
+    - 1x1 Convolution
+    - Convolution
+    - Dense Connections
+    - Depthwise Separable Convolution
+    - Global Average Pooling
+    - Max Pooling
+    - ReLU
+    - Residual Connection
+    - Softmax
+    Tasks:
+    - Image Classification
+    Training Data:
+    - ImageNet
+    ID: xception71
+    Crop Pct: '0.903'
+    Image Size: '299'
+    Interpolation: bicubic
+  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L219
+  Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_71-8eec7df1.pth
+  Results:
+  - Task: Image Classification
+    Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.88%
+      Top 5 Accuracy: 94.93%
+-->

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

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

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

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
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+mobilenetv3_small_100,30012.26,34.109,1024,224,0.06,1.42,2.54
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+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
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+efficientnet_cc_b0_8e,3929.29,260.595,1024,224,0.42,9.42,24.01
+efficientnet_cc_b0_4e,3918.01,261.346,1024,224,0.41,9.42,13.31
+darknet21,3914.26,261.596,1024,256,3.93,7.47,20.86
+efficientnet_b1,3876.9,264.116,1024,256,0.77,12.22,7.79
+tf_efficientnet_b1,3834.3,267.052,1024,240,0.71,10.88,7.79
+resnest14d,3793.21,269.944,1024,224,2.76,7.33,10.61
+sedarknet21,3784.73,270.549,1024,256,3.93,7.47,20.95
+resnext26ts,3775.5,271.211,1024,256,2.43,10.52,10.3
+tf_efficientnetv2_b2,3727.06,274.735,1024,260,1.72,9.84,10.1
+convnext_pico,3702.78,276.537,1024,288,2.27,10.08,9.05
+edgenext_x_small,3692.42,277.311,1024,288,0.68,7.5,2.34
+tf_efficientnet_cc_b0_8e,3691.33,277.395,1024,224,0.42,9.42,24.01
+dpn48b,3689.99,277.494,1024,224,1.69,8.92,9.13
+eca_resnext26ts,3675.59,278.583,1024,256,2.43,10.52,10.3
+seresnext26ts,3670.33,278.98,1024,256,2.43,10.52,10.39
+tf_efficientnet_cc_b0_4e,3665.41,279.357,1024,224,0.41,9.42,13.31
+tf_efficientnet_lite2,3662.0,279.618,1024,260,0.89,12.9,6.09
+nf_ecaresnet26,3619.99,282.862,1024,224,2.41,7.36,16.0
+nf_seresnet26,3618.8,282.955,1024,224,2.41,7.36,17.4
+gcresnext26ts,3594.7,284.852,1024,256,2.43,10.53,10.48
+mobilevitv2_100,3589.19,213.964,768,256,1.84,16.08,4.9
+gernet_l,3556.24,287.933,1024,256,4.57,8.0,31.08
+legacy_seresnext26_32x4d,3545.88,288.774,1024,224,2.49,9.39,16.79
+convnext_pico_ols,3532.27,289.886,1024,288,2.37,10.74,9.06
+resnet26t,3503.33,292.28,1024,256,3.35,10.52,16.01
+repvgg_a2,3454.82,296.386,1024,224,5.7,6.26,28.21
+mixnet_m,3418.52,299.526,1024,224,0.36,8.19,5.01
+efficientnet_b3_pruned,3356.7,305.049,1024,300,1.04,11.86,9.86
+nf_regnet_b1,3352.23,305.456,1024,288,1.02,9.2,10.22
+ecaresnext50t_32x4d,3339.2,306.649,1024,224,2.7,10.09,15.41
+ecaresnext26t_32x4d,3337.18,306.833,1024,224,2.7,10.09,15.41
+seresnext26tn_32x4d,3327.66,307.711,1024,224,2.7,10.09,16.81
+seresnext26t_32x4d,3327.23,307.751,1024,224,2.7,10.09,16.81
+seresnext26d_32x4d,3303.57,309.954,1024,224,2.73,10.19,16.81
+tf_mixnet_m,3301.19,310.17,1024,224,0.36,8.19,5.01
+convit_tiny,3286.62,311.554,1024,224,1.26,7.94,5.71
+mobilevit_xs,3278.19,234.265,768,256,1.05,16.33,2.32
+pit_s_224,3268.88,313.245,1024,224,2.88,11.56,23.46
+pit_s_distilled_224,3266.72,313.452,1024,224,2.9,11.64,24.04
+skresnet34,3242.45,315.8,1024,224,3.67,5.13,22.28
+eca_botnext26ts_256,3224.24,317.583,1024,256,2.46,11.6,10.59
+ecaresnet101d_pruned,3223.88,317.616,1024,224,3.48,7.69,24.88
+deit_small_distilled_patch16_224,3220.79,317.922,1024,224,4.63,12.02,22.44
+ecaresnetlight,3215.57,318.439,1024,224,4.11,8.42,30.16
+deit_small_patch16_224,3209.05,319.085,1024,224,4.61,11.95,22.05
+vit_small_patch16_224,3199.98,319.99,1024,224,4.61,11.95,22.05
+eca_halonext26ts,3173.71,322.639,1024,256,2.44,11.46,10.76
+convnextv2_atto,3162.98,323.733,1024,288,0.91,6.3,3.71
+resnetv2_50,3158.28,324.214,1024,224,4.11,11.11,25.55
+nf_regnet_b2,3133.63,326.765,1024,272,1.22,9.27,14.31
+rexnetr_200,3133.12,245.111,768,224,1.59,15.11,16.52
+botnet26t_256,3123.98,327.772,1024,256,3.32,11.98,12.49
+coat_lite_tiny,3113.54,328.874,1024,224,1.6,11.65,5.72
+vit_small_r26_s32_224,3112.34,329.001,1024,224,3.56,9.85,36.43
+bat_resnext26ts,3103.95,329.89,1024,256,2.53,12.51,10.73
+halonet26t,3103.39,329.95,1024,256,3.19,11.69,12.48
+pvt_v2_b1,3095.14,330.828,1024,224,2.12,15.39,14.01
+cspresnet50,3063.22,334.278,1024,256,4.54,11.5,21.62
+resnet32ts,3055.79,335.09,1024,256,4.63,11.58,17.96
+rexnet_200,3051.5,251.668,768,224,1.56,14.91,16.37
+lambda_resnet26t,3046.2,336.144,1024,256,3.02,11.87,10.96
+ssl_resnet50,3030.48,337.887,1024,224,4.11,11.11,25.56
+gluon_resnet50_v1b,3027.43,338.23,1024,224,4.11,11.11,25.56
+tv_resnet50,3027.39,338.232,1024,224,4.11,11.11,25.56
+swsl_resnet50,3027.07,338.268,1024,224,4.11,11.11,25.56
+resnet50,3025.4,338.455,1024,224,4.11,11.11,25.56
+deit3_small_patch16_224_in21ft1k,3023.02,338.721,1024,224,4.61,11.95,22.06
+deit3_small_patch16_224,3017.77,339.312,1024,224,4.61,11.95,22.06
+tresnet_m,3006.54,340.578,1024,224,5.74,7.31,31.39
+resnet33ts,3005.78,340.665,1024,256,4.76,11.66,19.68
+vit_small_resnet26d_224,2994.08,341.995,1024,224,5.07,11.12,63.61
+resnetv2_50t,2989.06,342.569,1024,224,4.32,11.82,25.57
+regnetx_032,2988.15,342.675,1024,224,3.2,11.37,15.3
+dpn68b,2981.13,343.481,1024,224,2.35,10.47,12.61
+hrnet_w18_small_v2,2978.67,343.765,1024,224,2.62,9.65,15.6
+dpn68,2975.29,344.155,1024,224,2.35,10.47,12.61
+resnetv2_50d,2971.15,344.633,1024,224,4.35,11.92,25.57
+efficientnet_em,2938.12,348.51,1024,240,3.04,14.34,6.9
+vit_base_patch32_plus_256,2934.64,348.925,1024,256,7.79,7.76,119.48
+coat_lite_mini,2921.75,350.462,1024,224,2.0,12.25,11.01
+tf_efficientnet_b2,2919.63,350.718,1024,260,1.02,13.83,9.11
+seresnet33ts,2919.51,350.732,1024,256,4.76,11.66,19.78
+eca_resnet33ts,2917.21,351.008,1024,256,4.76,11.66,19.68
+haloregnetz_b,2890.29,354.276,1024,224,1.97,11.94,11.68
+coatnet_pico_rw_224,2884.58,354.98,1024,224,2.05,14.62,10.85
+dla60,2883.99,355.049,1024,224,4.26,10.16,22.04
+gluon_resnet50_v1c,2872.58,356.463,1024,224,4.35,11.92,25.58
+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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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
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+focalnet_large_fl3,384,384.0,94.47,4064.948,105.06,168.04,239.13
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+dm_nfnet_f5,416,512.0,86.64,5909.833,170.71,204.56,377.21
+nfnet_f4,512,384.0,81.51,4711.211,216.26,262.26,316.07
+volo_d3_448,448,192.0,76.74,2501.831,96.33,446.83,86.63
+vit_so400m_patch14_siglip_384,384,512.0,75.92,6743.556,302.34,200.62,428.23
+nfnet_f6,448,512.0,75.59,6773.482,229.7,273.62,438.36
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+xcit_medium_24_p8_384,384,384.0,71.15,5396.903,186.67,354.69,84.32
+dm_nfnet_f4,512,384.0,69.56,5520.408,216.26,262.26,316.07
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+vit_gigantic_patch14_clip_224,224,512.0,66.18,7735.92,473.41,204.12,1844.91
+focalnet_xlarge_fl3,384,256.0,66.07,3874.786,185.61,223.99,408.79
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+eva02_large_patch14_448,448,512.0,59.6,8591.147,310.69,261.32,305.08
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+nfnet_f6,576,256.0,45.99,5566.397,378.69,452.2,438.36
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+samvit_base_patch16,1024,12.0,23.01,521.487,371.55,403.08,89.67
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+swinv2_cr_giant_384,384,32.0,15.01,2131.256,1450.71,1394.86,2598.76
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+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

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
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+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
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+mobilenetv2_035,224,19047.74,53.75,1024,1.68,0.07,2.86
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+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
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+resnet34,160,10402.15,98.431,1024,21.8,1.87,1.91
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+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
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+repghostnet_130,224,7749.8,132.123,1024,5.48,0.25,5.24
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+mnasnet_140,224,6835.02,149.805,1024,7.12,0.6,7.71
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+regnetz_005,224,6484.34,157.909,1024,7.12,0.52,5.86
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+regnety_064,288,1024.29,999.702,1024,30.58,10.56,27.11
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+vgg16,224,1021.7,1002.242,1024,138.36,15.47,13.56
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+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
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+regnetz_040,320,1006.41,508.726,512,27.12,6.35,37.78
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+regnety_080,288,1001.83,1022.121,1024,39.18,13.22,29.69
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+resnetrs101,288,992.71,1031.506,1024,63.62,13.56,28.53
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+nfnet_f1,224,951.44,1076.249,1024,132.63,17.87,22.94
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+eva02_small_patch14_336,336,946.95,1081.358,1024,22.13,15.48,54.33
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+vit_relpos_base_patch16_plus_240,240,923.53,1108.778,1024,117.38,27.3,34.33
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+mobilevitv2_175,384,803.11,318.749,256,14.25,12.47,63.29
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+eca_nfnet_l1,320,737.99,1387.537,1024,41.41,14.92,34.42
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+xception71,299,656.41,779.991,512,42.34,18.09,69.92
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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
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+spnasnet_100,224,22077.72,11.586,256,4.42,0.35,6.03
+mobilenetv4_hybrid_medium_075,224,22065.84,11.593,256,7.31,0.66,5.65
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+mobilenet_edgetpu_100,224,21052.52,12.151,256,4.09,1.0,5.75
+pit_xs_224,224,20835.05,12.278,256,10.62,1.4,7.71
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+regnety_008_tv,224,20329.15,12.583,256,6.43,0.84,5.42
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+semnasnet_100,224,19697.19,12.988,256,3.89,0.32,6.23
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+tinynet_b,188,18989.86,13.472,256,3.73,0.21,4.44
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+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
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+resnet50d,160,15291.83,16.732,256,25.58,2.22,6.08
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+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
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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
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+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
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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
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+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
+resnetblur50d,2509.45,408.045,1024,224,5.4,12.82,25.58
+nf_ecaresnet50,2490.39,411.168,1024,224,4.21,11.13,25.56
+inception_v3,2485.21,412.025,1024,299,5.73,8.97,23.83
+nf_seresnet50,2482.66,412.449,1024,224,4.21,11.13,28.09
+tf_inception_v3,2481.38,412.658,1024,299,5.73,8.97,23.83
+gc_efficientnetv2_rw_t,2480.59,412.793,1024,288,3.2,16.45,13.68
+adv_inception_v3,2479.41,412.983,1024,299,5.73,8.97,23.83
+repvgg_b1g4,2473.34,414.003,1024,224,8.15,10.64,39.97
+mobilevitv2_125,2472.28,414.18,1024,256,2.86,20.1,7.48
+gluon_inception_v3,2468.42,414.827,1024,299,5.73,8.97,23.83
+nf_regnet_b3,2461.52,415.991,1024,320,2.05,14.61,18.59
+xcit_small_12_p16_224_dist,2446.89,418.478,1024,224,4.82,12.58,26.25
+xcit_small_12_p16_224,2446.42,418.558,1024,224,4.82,12.58,26.25
+cspresnext50,2438.96,419.836,1024,256,4.05,15.86,20.57
+convnext_nano_ols,2435.0,420.521,1024,288,4.38,15.5,15.65
+regnetx_032,2429.42,421.489,1024,224,3.2,11.37,15.3
+densenet169,2426.29,422.031,1024,224,3.4,7.3,14.15
+sehalonet33ts,2419.4,423.234,1024,256,3.55,14.7,13.69
+tf_efficientnet_cc_b1_8e,2406.19,425.557,1024,240,0.75,15.44,39.72
+semobilevit_s,2402.02,426.294,1024,256,2.03,19.95,5.74
+resnetv2_101,2330.6,439.36,1024,224,7.83,16.23,44.54
+twins_pcpvt_small,2312.72,442.754,1024,224,3.83,18.08,24.11
+xcit_nano_12_p8_224_dist,2295.5,446.077,1024,224,2.16,15.71,3.05
+xcit_nano_12_p8_224,2292.87,446.587,1024,224,2.16,15.71,3.05
+gmlp_s16_224,2290.73,447.007,1024,224,4.42,15.1,19.42
+cs3darknet_focus_x,2287.2,447.697,1024,256,8.03,10.69,35.02
+vit_base_r26_s32_224,2275.25,450.047,1024,224,6.81,12.36,101.38
+gluon_resnet101_v1b,2260.37,453.01,1024,224,7.83,16.23,44.55
+tv_resnet101,2258.59,453.368,1024,224,7.83,16.23,44.55
+resnet101,2258.28,453.43,1024,224,7.83,16.23,44.55
+skresnet50,2234.62,458.23,1024,224,4.11,12.5,25.8
+ecaresnet26t,2232.29,458.709,1024,320,5.24,16.44,16.01
+edgenext_small,2226.69,459.86,1024,320,1.97,14.16,5.59
+dla102,2219.96,461.255,1024,224,7.19,14.18,33.27
+res2next50,2214.71,462.347,1024,224,4.2,13.71,24.67
+dla60_res2next,2210.67,463.194,1024,224,3.49,13.17,17.03
+resnetv2_101d,2203.82,464.633,1024,224,8.07,17.04,44.56
+gluon_resnet101_v1c,2194.65,466.578,1024,224,8.08,17.04,44.57
+resnest26d,2170.04,471.869,1024,224,3.64,9.97,17.07
+vgg13,2149.71,476.331,1024,224,11.31,12.25,133.05
+gluon_resnet101_v1d,2137.49,479.053,1024,224,8.08,17.04,44.57
+skresnet50d,2115.22,484.098,1024,224,4.36,13.31,25.82
+convnextv2_pico,2108.5,485.64,1024,288,2.27,10.08,9.07
+vit_base_resnet50d_224,2101.17,487.333,1024,224,8.73,16.92,110.97
+coatnet_0_rw_224,2082.49,491.706,1024,224,4.43,18.73,27.44
+crossvit_small_240,2081.5,491.94,1024,240,5.63,18.17,26.86
+deit3_medium_patch16_224_in21ft1k,2076.53,493.118,1024,224,8.0,15.93,38.85
+deit3_medium_patch16_224,2072.34,494.116,1024,224,8.0,15.93,38.85
+mobilevitv2_150,2071.36,494.349,1024,256,4.09,24.11,10.59
+mobilevitv2_150_in22ft1k,2070.3,494.603,1024,256,4.09,24.11,10.59
+sebotnet33ts_256,2067.91,247.581,512,256,3.89,17.46,13.7
+wide_resnet50_2,2057.08,497.78,1024,224,11.43,14.4,68.88
+vit_relpos_medium_patch16_rpn_224,2044.85,500.757,1024,224,7.97,17.02,38.73
+efficientformer_l3,2041.79,501.507,1024,224,3.93,12.01,31.41
+poolformer_s24,2040.35,501.863,1024,224,3.41,10.68,21.39
+vit_relpos_medium_patch16_224,2037.47,502.572,1024,224,7.97,17.02,38.75
+cspdarknet53,2035.94,502.949,1024,256,6.57,16.81,27.64
+resnet51q,2034.41,503.329,1024,288,8.07,20.94,35.7
+vit_srelpos_medium_patch16_224,2033.15,503.638,1024,224,7.96,16.21,38.74
+maxvit_rmlp_pico_rw_256,2008.78,509.748,1024,256,1.85,24.86,7.52
+vit_relpos_medium_patch16_cls_224,2007.24,510.141,1024,224,8.03,18.24,38.76
+dla102x,2006.55,510.315,1024,224,5.89,19.42,26.31
+legacy_seresnet101,2003.12,511.188,1024,224,7.61,15.74,49.33
+swin_tiny_patch4_window7_224,1995.14,513.235,1024,224,4.51,17.06,28.29
+repvgg_b1,1985.42,515.747,1024,224,13.16,10.64,57.42
+resnetaa101d,1982.98,516.381,1024,224,9.12,17.56,44.57
+coatnet_rmlp_0_rw_224,1981.75,516.703,1024,224,4.72,24.89,27.45
+tf_efficientnet_b3,1975.92,518.226,1024,300,1.87,23.83,12.23
+gcvit_xtiny,1969.68,519.869,1024,224,2.93,20.26,19.98
+hrnet_w18,1967.17,520.531,1024,224,4.32,16.31,21.3
+gluon_resnet101_v1s,1965.68,520.926,1024,224,9.19,18.64,44.67
+maxvit_pico_rw_256,1965.38,521.006,1024,256,1.83,22.3,7.46
+resnetaa50,1958.15,522.93,1024,288,8.52,19.24,25.56
+seresnet101,1954.63,523.871,1024,224,7.84,16.27,49.33
+efficientnet_b3,1949.54,525.239,1024,320,2.01,26.52,12.23
+efficientnet_b3a,1949.11,525.356,1024,320,2.01,26.52,12.23
+edgenext_small_rw,1932.68,529.816,1024,320,2.46,14.85,7.83
+regnetx_040,1932.62,529.839,1024,224,3.99,12.2,22.12
+cs3sedarknet_xdw,1925.4,531.825,1024,256,5.97,17.18,21.6
+coatnet_bn_0_rw_224,1920.71,533.123,1024,224,4.67,22.04,27.44
+xcit_tiny_12_p16_384_dist,1911.65,535.652,1024,384,3.64,18.26,6.72
+ssl_resnext101_32x4d,1910.73,535.909,1024,224,8.01,21.23,44.18
+swsl_resnext101_32x4d,1910.43,535.993,1024,224,8.01,21.23,44.18
+resnext101_32x4d,1909.99,536.115,1024,224,8.01,21.23,44.18
+gluon_resnext101_32x4d,1909.34,536.298,1024,224,8.01,21.23,44.18
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+darknetaa53,1898.12,539.468,1024,288,10.08,15.68,36.02
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+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
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+cait_s36_384,101.88,5025.316,512,384,47.99,367.4,68.37
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+deit3_large_patch16_384,99.8,2564.994,256,384,191.21,270.24,304.76
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+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
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+ig_resnext101_32x48d,56.43,6804.709,384,224,153.57,131.06,828.41
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+resnetv2_152x4_bitm,30.46,3151.929,96,480,844.84,414.26,936.53
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+swinv2_cr_giant_384,15.06,2124.735,32,384,1450.71,1394.86,2598.76
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+eva_giant_patch14_560,10.52,3043.009,32,560,1906.76,2577.17,1014.45

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
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+lcnet_050,224,1024.0,42147.98,24.285,0.05,1.26,1.88
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+tinynet_d,152,1024.0,37989.71,26.944,0.05,1.42,2.34
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+lcnet_075,224,1024.0,30664.19,33.384,0.1,1.99,2.36
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