Model card for convnext_tiny.fb_in1k

A ConvNeXt image classification model. Pretrained on ImageNet-1k by paper authors.

Model Details

• Model Type: Image classification / feature backbone
• Model Stats:
  • Params (M): 28.6
  • GMACs: 4.5
  • Activations (M): 13.4
  • Image size: train = 224 x 224, test = 288 x 288
• Papers:
  • A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
• Original: https://github.com/facebookresearch/ConvNeXt
• Dataset: ImageNet-1k

Model Usage

Image Classification

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('convnext_tiny.fb_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)

Feature Map Extraction

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'convnext_tiny.fb_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 96, 56, 56])
    #  torch.Size([1, 192, 28, 28])
    #  torch.Size([1, 384, 14, 14])
    #  torch.Size([1, 768, 7, 7])

    print(o.shape)

Image Embeddings

from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'convnext_tiny.fb_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 768, 7, 7) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor

Model Comparison

Explore the dataset and runtime metrics of this model in timm model results.

All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

model	top1	top5	img_size	param_count	gmacs	macts	samples_per_sec	batch_size
convnextv2_huge.fcmae_ft_in22k_in1k_512	88.848	98.742	512	660.29	600.81	413.07	28.58	48
convnextv2_huge.fcmae_ft_in22k_in1k_384	88.668	98.738	384	660.29	337.96	232.35	50.56	64
convnext_xxlarge.clip_laion2b_soup_ft_in1k	88.612	98.704	256	846.47	198.09	124.45	122.45	256
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384	88.312	98.578	384	200.13	101.11	126.74	196.84	256
convnextv2_large.fcmae_ft_in22k_in1k_384	88.196	98.532	384	197.96	101.1	126.74	128.94	128
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320	87.968	98.47	320	200.13	70.21	88.02	283.42	256
convnext_xlarge.fb_in22k_ft_in1k_384	87.75	98.556	384	350.2	179.2	168.99	124.85	192
convnextv2_base.fcmae_ft_in22k_in1k_384	87.646	98.422	384	88.72	45.21	84.49	209.51	256
convnext_large.fb_in22k_ft_in1k_384	87.476	98.382	384	197.77	101.1	126.74	194.66	256
convnext_large_mlp.clip_laion2b_augreg_ft_in1k	87.344	98.218	256	200.13	44.94	56.33	438.08	256
convnextv2_large.fcmae_ft_in22k_in1k	87.26	98.248	224	197.96	34.4	43.13	376.84	256
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384	87.138	98.212	384	88.59	45.21	84.49	365.47	256
convnext_xlarge.fb_in22k_ft_in1k	87.002	98.208	224	350.2	60.98	57.5	368.01	256
convnext_base.fb_in22k_ft_in1k_384	86.796	98.264	384	88.59	45.21	84.49	366.54	256
convnextv2_base.fcmae_ft_in22k_in1k	86.74	98.022	224	88.72	15.38	28.75	624.23	256
convnext_large.fb_in22k_ft_in1k	86.636	98.028	224	197.77	34.4	43.13	581.43	256
convnext_base.clip_laiona_augreg_ft_in1k_384	86.504	97.97	384	88.59	45.21	84.49	368.14	256
convnext_base.clip_laion2b_augreg_ft_in12k_in1k	86.344	97.97	256	88.59	20.09	37.55	816.14	256
convnextv2_huge.fcmae_ft_in1k	86.256	97.75	224	660.29	115.0	79.07	154.72	256
convnext_small.in12k_ft_in1k_384	86.182	97.92	384	50.22	25.58	63.37	516.19	256
convnext_base.clip_laion2b_augreg_ft_in1k	86.154	97.68	256	88.59	20.09	37.55	819.86	256
convnext_base.fb_in22k_ft_in1k	85.822	97.866	224	88.59	15.38	28.75	1037.66	256
convnext_small.fb_in22k_ft_in1k_384	85.778	97.886	384	50.22	25.58	63.37	518.95	256
convnextv2_large.fcmae_ft_in1k	85.742	97.584	224	197.96	34.4	43.13	375.23	256
convnext_small.in12k_ft_in1k	85.174	97.506	224	50.22	8.71	21.56	1474.31	256
convnext_tiny.in12k_ft_in1k_384	85.118	97.608	384	28.59	13.14	39.48	856.76	256
convnextv2_tiny.fcmae_ft_in22k_in1k_384	85.112	97.63	384	28.64	13.14	39.48	491.32	256
convnextv2_base.fcmae_ft_in1k	84.874	97.09	224	88.72	15.38	28.75	625.33	256
convnext_small.fb_in22k_ft_in1k	84.562	97.394	224	50.22	8.71	21.56	1478.29	256
convnext_large.fb_in1k	84.282	96.892	224	197.77	34.4	43.13	584.28	256
convnext_tiny.in12k_ft_in1k	84.186	97.124	224	28.59	4.47	13.44	2433.7	256
convnext_tiny.fb_in22k_ft_in1k_384	84.084	97.14	384	28.59	13.14	39.48	862.95	256
convnextv2_tiny.fcmae_ft_in22k_in1k	83.894	96.964	224	28.64	4.47	13.44	1452.72	256
convnext_base.fb_in1k	83.82	96.746	224	88.59	15.38	28.75	1054.0	256
convnextv2_nano.fcmae_ft_in22k_in1k_384	83.37	96.742	384	15.62	7.22	24.61	801.72	256
convnext_small.fb_in1k	83.142	96.434	224	50.22	8.71	21.56	1464.0	256
convnextv2_tiny.fcmae_ft_in1k	82.92	96.284	224	28.64	4.47	13.44	1425.62	256
convnext_tiny.fb_in22k_ft_in1k	82.898	96.616	224	28.59	4.47	13.44	2480.88	256
convnext_nano.in12k_ft_in1k	82.282	96.344	224	15.59	2.46	8.37	3926.52	256
convnext_tiny_hnf.a2h_in1k	82.216	95.852	224	28.59	4.47	13.44	2529.75	256
convnext_tiny.fb_in1k	82.066	95.854	224	28.59	4.47	13.44	2346.26	256
convnextv2_nano.fcmae_ft_in22k_in1k	82.03	96.166	224	15.62	2.46	8.37	2300.18	256
convnextv2_nano.fcmae_ft_in1k	81.83	95.738	224	15.62	2.46	8.37	2321.48	256
convnext_nano_ols.d1h_in1k	80.866	95.246	224	15.65	2.65	9.38	3523.85	256
convnext_nano.d1h_in1k	80.768	95.334	224	15.59	2.46	8.37	3915.58	256
convnextv2_pico.fcmae_ft_in1k	80.304	95.072	224	9.07	1.37	6.1	3274.57	256
convnext_pico.d1_in1k	79.526	94.558	224	9.05	1.37	6.1	5686.88	256
convnext_pico_ols.d1_in1k	79.522	94.692	224	9.06	1.43	6.5	5422.46	256
convnextv2_femto.fcmae_ft_in1k	78.488	93.98	224	5.23	0.79	4.57	4264.2	256
convnext_femto_ols.d1_in1k	77.86	93.83	224	5.23	0.82	4.87	6910.6	256
convnext_femto.d1_in1k	77.454	93.68	224	5.22	0.79	4.57	7189.92	256
convnextv2_atto.fcmae_ft_in1k	76.664	93.044	224	3.71	0.55	3.81	4728.91	256
convnext_atto_ols.a2_in1k	75.88	92.846	224	3.7	0.58	4.11	7963.16	256
convnext_atto.d2_in1k	75.664	92.9	224	3.7	0.55	3.81	8439.22	256

Citation

@article{liu2022convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2022},
}

@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}