Hugging Face's logo

timm
/
wide_resnet50_2.tv_in1k

Image Classification
timm
PyTorch
Safetensors
Model card Files Community

Model card for wide_resnet50_2.tv_in1k

A Wide-ResNet-B image classification model.

This model features:

  • ReLU activations
  • single layer 7x7 convolution with pooling
  • 1x1 convolution shortcut downsample

Trained on ImageNet-1k, original torchvision model weight.

Model Details

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('wide_resnet50_2.tv_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(
    'wide_resnet50_2.tv_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, 64, 112, 112])
    #  torch.Size([1, 256, 56, 56])
    #  torch.Size([1, 512, 28, 28])
    #  torch.Size([1, 1024, 14, 14])
    #  torch.Size([1, 2048, 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(
    'wide_resnet50_2.tv_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, 2048, 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.

model img_size top1 top5 param_count gmacs macts img/sec
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288 320 86.72 98.17 93.6 35.2 69.7 451
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288 288 86.51 98.08 93.6 28.5 56.4 560
seresnextaa101d_32x8d.sw_in12k_ft_in1k 288 86.49 98.03 93.6 28.5 56.4 557
seresnextaa101d_32x8d.sw_in12k_ft_in1k 224 85.96 97.82 93.6 17.2 34.2 923
resnext101_32x32d.fb_wsl_ig1b_ft_in1k 224 85.11 97.44 468.5 87.3 91.1 254
resnetrs420.tf_in1k 416 85.0 97.12 191.9 108.4 213.8 134
ecaresnet269d.ra2_in1k 352 84.96 97.22 102.1 50.2 101.2 291
ecaresnet269d.ra2_in1k 320 84.73 97.18 102.1 41.5 83.7 353
resnetrs350.tf_in1k 384 84.71 96.99 164.0 77.6 154.7 183
seresnextaa101d_32x8d.ah_in1k 288 84.57 97.08 93.6 28.5 56.4 557
resnetrs200.tf_in1k 320 84.45 97.08 93.2 31.5 67.8 446
resnetrs270.tf_in1k 352 84.43 96.97 129.9 51.1 105.5 280
seresnext101d_32x8d.ah_in1k 288 84.36 96.92 93.6 27.6 53.0 595
seresnet152d.ra2_in1k 320 84.35 97.04 66.8 24.1 47.7 610
resnetrs350.tf_in1k 288 84.3 96.94 164.0 43.7 87.1 333
resnext101_32x8d.fb_swsl_ig1b_ft_in1k 224 84.28 97.17 88.8 16.5 31.2 1100
resnetrs420.tf_in1k 320 84.24 96.86 191.9 64.2 126.6 228
seresnext101_32x8d.ah_in1k 288 84.19 96.87 93.6 27.2 51.6 613
resnext101_32x16d.fb_wsl_ig1b_ft_in1k 224 84.18 97.19 194.0 36.3 51.2 581
resnetaa101d.sw_in12k_ft_in1k 288 84.11 97.11 44.6 15.1 29.0 1144
resnet200d.ra2_in1k 320 83.97 96.82 64.7 31.2 67.3 518
resnetrs200.tf_in1k 256 83.87 96.75 93.2 20.2 43.4 692
seresnextaa101d_32x8d.ah_in1k 224 83.86 96.65 93.6 17.2 34.2 923
resnetrs152.tf_in1k 320 83.72 96.61 86.6 24.3 48.1 617
seresnet152d.ra2_in1k 256 83.69 96.78 66.8 15.4 30.6 943
seresnext101d_32x8d.ah_in1k 224 83.68 96.61 93.6 16.7 32.0 986
resnet152d.ra2_in1k 320 83.67 96.74 60.2 24.1 47.7 706
resnetrs270.tf_in1k 256 83.59 96.61 129.9 27.1 55.8 526
seresnext101_32x8d.ah_in1k 224 83.58 96.4 93.6 16.5 31.2 1013
resnetaa101d.sw_in12k_ft_in1k 224 83.54 96.83 44.6 9.1 17.6 1864
resnet152.a1h_in1k 288 83.46 96.54 60.2 19.1 37.3 904
resnext101_32x16d.fb_swsl_ig1b_ft_in1k 224 83.35 96.85 194.0 36.3 51.2 582
resnet200d.ra2_in1k 256 83.23 96.53 64.7 20.0 43.1 809
resnext101_32x4d.fb_swsl_ig1b_ft_in1k 224 83.22 96.75 44.2 8.0 21.2 1814
resnext101_64x4d.c1_in1k 288 83.16 96.38 83.5 25.7 51.6 590
resnet152d.ra2_in1k 256 83.14 96.38 60.2 15.4 30.5 1096
resnet101d.ra2_in1k 320 83.02 96.45 44.6 16.5 34.8 992
ecaresnet101d.miil_in1k 288 82.98 96.54 44.6 13.4 28.2 1077
resnext101_64x4d.tv_in1k 224 82.98 96.25 83.5 15.5 31.2 989
resnetrs152.tf_in1k 256 82.86 96.28 86.6 15.6 30.8 951
resnext101_32x8d.tv2_in1k 224 82.83 96.22 88.8 16.5 31.2 1099
resnet152.a1h_in1k 224 82.8 96.13 60.2 11.6 22.6 1486
resnet101.a1h_in1k 288 82.8 96.32 44.6 13.0 26.8 1291
resnet152.a1_in1k 288 82.74 95.71 60.2 19.1 37.3 905
resnext101_32x8d.fb_wsl_ig1b_ft_in1k 224 82.69 96.63 88.8 16.5 31.2 1100
resnet152.a2_in1k 288 82.62 95.75 60.2 19.1 37.3 904
resnetaa50d.sw_in12k_ft_in1k 288 82.61 96.49 25.6 8.9 20.6 1729
resnet61q.ra2_in1k 288 82.53 96.13 36.8 9.9 21.5 1773
wide_resnet101_2.tv2_in1k 224 82.5 96.02 126.9 22.8 21.2 1078
resnext101_64x4d.c1_in1k 224 82.46 95.92 83.5 15.5 31.2 987
resnet51q.ra2_in1k 288 82.36 96.18 35.7 8.1 20.9 1964
ecaresnet50t.ra2_in1k 320 82.35 96.14 25.6 8.8 24.1 1386
resnet101.a1_in1k 288 82.31 95.63 44.6 13.0 26.8 1291
resnetrs101.tf_in1k 288 82.29 96.01 63.6 13.6 28.5 1078
resnet152.tv2_in1k 224 82.29 96.0 60.2 11.6 22.6 1484
wide_resnet50_2.racm_in1k 288 82.27 96.06 68.9 18.9 23.8 1176
resnet101d.ra2_in1k 256 82.26 96.07 44.6 10.6 22.2 1542
resnet101.a2_in1k 288 82.24 95.73 44.6 13.0 26.8 1290
seresnext50_32x4d.racm_in1k 288 82.2 96.14 27.6 7.0 23.8 1547
ecaresnet101d.miil_in1k 224 82.18 96.05 44.6 8.1 17.1 1771
resnext50_32x4d.fb_swsl_ig1b_ft_in1k 224 82.17 96.22 25.0 4.3 14.4 2943
ecaresnet50t.a1_in1k 288 82.12 95.65 25.6 7.1 19.6 1704
resnext50_32x4d.a1h_in1k 288 82.03 95.94 25.0 7.0 23.8 1745
ecaresnet101d_pruned.miil_in1k 288 82.0 96.15 24.9 5.8 12.7 1787
resnet61q.ra2_in1k 256 81.99 95.85 36.8 7.8 17.0 2230
resnext101_32x8d.tv2_in1k 176 81.98 95.72 88.8 10.3 19.4 1768
resnet152.a1_in1k 224 81.97 95.24 60.2 11.6 22.6 1486
resnet101.a1h_in1k 224 81.93 95.75 44.6 7.8 16.2 2122
resnet101.tv2_in1k 224 81.9 95.77 44.6 7.8 16.2 2118
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k 224 81.84 96.1 194.0 36.3 51.2 583
resnet51q.ra2_in1k 256 81.78 95.94 35.7 6.4 16.6 2471
resnet152.a2_in1k 224 81.77 95.22 60.2 11.6 22.6 1485
resnetaa50d.sw_in12k_ft_in1k 224 81.74 96.06 25.6 5.4 12.4 2813
ecaresnet50t.a2_in1k 288 81.65 95.54 25.6 7.1 19.6 1703
ecaresnet50d.miil_in1k 288 81.64 95.88 25.6 7.2 19.7 1694
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k 224 81.62 96.04 88.8 16.5 31.2 1101
wide_resnet50_2.tv2_in1k 224 81.61 95.76 68.9 11.4 14.4 1930
resnetaa50.a1h_in1k 288 81.61 95.83 25.6 8.5 19.2 1868
resnet101.a1_in1k 224 81.5 95.16 44.6 7.8 16.2 2125
resnext50_32x4d.a1_in1k 288 81.48 95.16 25.0 7.0 23.8 1745
gcresnet50t.ra2_in1k 288 81.47 95.71 25.9 6.9 18.6 2071
wide_resnet50_2.racm_in1k 224 81.45 95.53 68.9 11.4 14.4 1929
resnet50d.a1_in1k 288 81.44 95.22 25.6 7.2 19.7 1908
ecaresnet50t.ra2_in1k 256 81.44 95.67 25.6 5.6 15.4 2168
ecaresnetlight.miil_in1k 288 81.4 95.82 30.2 6.8 13.9 2132
resnet50d.ra2_in1k 288 81.37 95.74 25.6 7.2 19.7 1910
resnet101.a2_in1k 224 81.32 95.19 44.6 7.8 16.2 2125
seresnet50.ra2_in1k 288 81.3 95.65 28.1 6.8 18.4 1803
resnext50_32x4d.a2_in1k 288 81.3 95.11 25.0 7.0 23.8 1746
seresnext50_32x4d.racm_in1k 224 81.27 95.62 27.6 4.3 14.4 2591
ecaresnet50t.a1_in1k 224 81.26 95.16 25.6 4.3 11.8 2823
gcresnext50ts.ch_in1k 288 81.23 95.54 15.7 4.8 19.6 2117
senet154.gluon_in1k 224 81.23 95.35 115.1 20.8 38.7 545
resnet50.a1_in1k 288 81.22 95.11 25.6 6.8 18.4 2089
resnet50_gn.a1h_in1k 288 81.22 95.63 25.6 6.8 18.4 676
resnet50d.a2_in1k 288 81.18 95.09 25.6 7.2 19.7 1908
resnet50.fb_swsl_ig1b_ft_in1k 224 81.18 95.98 25.6 4.1 11.1 3455
resnext50_32x4d.tv2_in1k 224 81.17 95.34 25.0 4.3 14.4 2933
resnext50_32x4d.a1h_in1k 224 81.1 95.33 25.0 4.3 14.4 2934
seresnet50.a2_in1k 288 81.1 95.23 28.1 6.8 18.4 1801
seresnet50.a1_in1k 288 81.1 95.12 28.1 6.8 18.4 1799
resnet152s.gluon_in1k 224 81.02 95.41 60.3 12.9 25.0 1347
resnet50.d_in1k 288 80.97 95.44 25.6 6.8 18.4 2085
gcresnet50t.ra2_in1k 256 80.94 95.45 25.9 5.4 14.7 2571
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k 224 80.93 95.73 44.2 8.0 21.2 1814
resnet50.c1_in1k 288 80.91 95.55 25.6 6.8 18.4 2084
seresnext101_32x4d.gluon_in1k 224 80.9 95.31 49.0 8.0 21.3 1585
seresnext101_64x4d.gluon_in1k 224 80.9 95.3 88.2 15.5 31.2 918
resnet50.c2_in1k 288 80.86 95.52 25.6 6.8 18.4 2085
resnet50.tv2_in1k 224 80.85 95.43 25.6 4.1 11.1 3450
ecaresnet50t.a2_in1k 224 80.84 95.02 25.6 4.3 11.8 2821
ecaresnet101d_pruned.miil_in1k 224 80.79 95.62 24.9 3.5 7.7 2961
seresnet33ts.ra2_in1k 288 80.79 95.36 19.8 6.0 14.8 2506
ecaresnet50d_pruned.miil_in1k 288 80.79 95.58 19.9 4.2 10.6 2349
resnet50.a2_in1k 288 80.78 94.99 25.6 6.8 18.4 2088
resnet50.b1k_in1k 288 80.71 95.43 25.6 6.8 18.4 2087
resnext50_32x4d.ra_in1k 288 80.7 95.39 25.0 7.0 23.8 1749
resnetrs101.tf_in1k 192 80.69 95.24 63.6 6.0 12.7 2270
resnet50d.a1_in1k 224 80.68 94.71 25.6 4.4 11.9 3162
eca_resnet33ts.ra2_in1k 288 80.68 95.36 19.7 6.0 14.8 2637
resnet50.a1h_in1k 224 80.67 95.3 25.6 4.1 11.1 3452
resnext50d_32x4d.bt_in1k 288 80.67 95.42 25.0 7.4 25.1 1626
resnetaa50.a1h_in1k 224 80.63 95.21 25.6 5.2 11.6 3034
ecaresnet50d.miil_in1k 224 80.61 95.32 25.6 4.4 11.9 2813
resnext101_64x4d.gluon_in1k 224 80.61 94.99 83.5 15.5 31.2 989
gcresnet33ts.ra2_in1k 288 80.6 95.31 19.9 6.0 14.8 2578
gcresnext50ts.ch_in1k 256 80.57 95.17 15.7 3.8 15.5 2710
resnet152.a3_in1k 224 80.56 95.0 60.2 11.6 22.6 1483
resnet50d.ra2_in1k 224 80.53 95.16 25.6 4.4 11.9 3164
resnext50_32x4d.a1_in1k 224 80.53 94.46 25.0 4.3 14.4 2930
wide_resnet101_2.tv2_in1k 176 80.48 94.98 126.9 14.3 13.2 1719
resnet152d.gluon_in1k 224 80.47 95.2 60.2 11.8 23.4 1428
resnet50.b2k_in1k 288 80.45 95.32 25.6 6.8 18.4 2086
ecaresnetlight.miil_in1k 224 80.45 95.24 30.2 4.1 8.4 3530
resnext50_32x4d.a2_in1k 224 80.45 94.63 25.0 4.3 14.4 2936
wide_resnet50_2.tv2_in1k 176 80.43 95.09 68.9 7.3 9.0 3015
resnet101d.gluon_in1k 224 80.42 95.01 44.6 8.1 17.0 2007
resnet50.a1_in1k 224 80.38 94.6 25.6 4.1 11.1 3461
seresnet33ts.ra2_in1k 256 80.36 95.1 19.8 4.8 11.7 3267
resnext101_32x4d.gluon_in1k 224 80.34 94.93 44.2 8.0 21.2 1814
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k 224 80.32 95.4 25.0 4.3 14.4 2941
resnet101s.gluon_in1k 224 80.28 95.16 44.7 9.2 18.6 1851
seresnet50.ra2_in1k 224 80.26 95.08 28.1 4.1 11.1 2972
resnetblur50.bt_in1k 288 80.24 95.24 25.6 8.5 19.9 1523
resnet50d.a2_in1k 224 80.22 94.63 25.6 4.4 11.9 3162
resnet152.tv2_in1k 176 80.2 94.64 60.2 7.2 14.0 2346
seresnet50.a2_in1k 224 80.08 94.74 28.1 4.1 11.1 2969
eca_resnet33ts.ra2_in1k 256 80.08 94.97 19.7 4.8 11.7 3284
gcresnet33ts.ra2_in1k 256 80.06 94.99 19.9 4.8 11.7 3216
resnet50_gn.a1h_in1k 224 80.06 94.95 25.6 4.1 11.1 1109
seresnet50.a1_in1k 224 80.02 94.71 28.1 4.1 11.1 2962
resnet50.ram_in1k 288 79.97 95.05 25.6 6.8 18.4 2086
resnet152c.gluon_in1k 224 79.92 94.84 60.2 11.8 23.4 1455
seresnext50_32x4d.gluon_in1k 224 79.91 94.82 27.6 4.3 14.4 2591
resnet50.d_in1k 224 79.91 94.67 25.6 4.1 11.1 3456
resnet101.tv2_in1k 176 79.9 94.6 44.6 4.9 10.1 3341
resnetrs50.tf_in1k 224 79.89 94.97 35.7 4.5 12.1 2774
resnet50.c2_in1k 224 79.88 94.87 25.6 4.1 11.1 3455
ecaresnet26t.ra2_in1k 320 79.86 95.07 16.0 5.2 16.4 2168
resnet50.a2_in1k 224 79.85 94.56 25.6 4.1 11.1 3460
resnet50.ra_in1k 288 79.83 94.97 25.6 6.8 18.4 2087
resnet101.a3_in1k 224 79.82 94.62 44.6 7.8 16.2 2114
resnext50_32x4d.ra_in1k 224 79.76 94.6 25.0 4.3 14.4 2943
resnet50.c1_in1k 224 79.74 94.95 25.6 4.1 11.1 3455
ecaresnet50d_pruned.miil_in1k 224 79.74 94.87 19.9 2.5 6.4 3929
resnet33ts.ra2_in1k 288 79.71 94.83 19.7 6.0 14.8 2710
resnet152.gluon_in1k 224 79.68 94.74 60.2 11.6 22.6 1486
resnext50d_32x4d.bt_in1k 224 79.67 94.87 25.0 4.5 15.2 2729
resnet50.bt_in1k 288 79.63 94.91 25.6 6.8 18.4 2086
ecaresnet50t.a3_in1k 224 79.56 94.72 25.6 4.3 11.8 2805
resnet101c.gluon_in1k 224 79.53 94.58 44.6 8.1 17.0 2062
resnet50.b1k_in1k 224 79.52 94.61 25.6 4.1 11.1 3459
resnet50.tv2_in1k 176 79.42 94.64 25.6 2.6 6.9 5397
resnet32ts.ra2_in1k 288 79.4 94.66 18.0 5.9 14.6 2752
resnet50.b2k_in1k 224 79.38 94.57 25.6 4.1 11.1 3459
resnext50_32x4d.tv2_in1k 176 79.37 94.3 25.0 2.7 9.0 4577
resnext50_32x4d.gluon_in1k 224 79.36 94.43 25.0 4.3 14.4 2942
resnext101_32x8d.tv_in1k 224 79.31 94.52 88.8 16.5 31.2 1100
resnet101.gluon_in1k 224 79.31 94.53 44.6 7.8 16.2 2125
resnetblur50.bt_in1k 224 79.31 94.63 25.6 5.2 12.0 2524
resnet50.a1h_in1k 176 79.27 94.49 25.6 2.6 6.9 5404
resnext50_32x4d.a3_in1k 224 79.25 94.31 25.0 4.3 14.4 2931
resnet50.fb_ssl_yfcc100m_ft_in1k 224 79.22 94.84 25.6 4.1 11.1 3451
resnet33ts.ra2_in1k 256 79.21 94.56 19.7 4.8 11.7 3392
resnet50d.gluon_in1k 224 79.07 94.48 25.6 4.4 11.9 3162
resnet50.ram_in1k 224 79.03 94.38 25.6 4.1 11.1 3453
resnet50.am_in1k 224 79.01 94.39 25.6 4.1 11.1 3461
resnet32ts.ra2_in1k 256 79.01 94.37 18.0 4.6 11.6 3440
ecaresnet26t.ra2_in1k 256 78.9 94.54 16.0 3.4 10.5 3421
resnet152.a3_in1k 160 78.89 94.11 60.2 5.9 11.5 2745
wide_resnet101_2.tv_in1k 224 78.84 94.28 126.9 22.8 21.2 1079
seresnext26d_32x4d.bt_in1k 288 78.83 94.24 16.8 4.5 16.8 2251
resnet50.ra_in1k 224 78.81 94.32 25.6 4.1 11.1 3454
seresnext26t_32x4d.bt_in1k 288 78.74 94.33 16.8 4.5 16.7 2264
resnet50s.gluon_in1k 224 78.72 94.23 25.7 5.5 13.5 2796
resnet50d.a3_in1k 224 78.71 94.24 25.6 4.4 11.9 3154
wide_resnet50_2.tv_in1k 224 78.47 94.09 68.9 11.4 14.4 1934
resnet50.bt_in1k 224 78.46 94.27 25.6 4.1 11.1 3454
resnet34d.ra2_in1k 288 78.43 94.35 21.8 6.5 7.5 3291
gcresnext26ts.ch_in1k 288 78.42 94.04 10.5 3.1 13.3 3226
resnet26t.ra2_in1k 320 78.33 94.13 16.0 5.2 16.4 2391
resnet152.tv_in1k 224 78.32 94.04 60.2 11.6 22.6 1487
seresnext26ts.ch_in1k 288 78.28 94.1 10.4 3.1 13.3 3062
bat_resnext26ts.ch_in1k 256 78.25 94.1 10.7 2.5 12.5 3393
resnet50.a3_in1k 224 78.06 93.78 25.6 4.1 11.1 3450
resnet50c.gluon_in1k 224 78.0 93.99 25.6 4.4 11.9 3286
eca_resnext26ts.ch_in1k 288 78.0 93.91 10.3 3.1 13.3 3297
seresnext26t_32x4d.bt_in1k 224 77.98 93.75 16.8 2.7 10.1 3841
resnet34.a1_in1k 288 77.92 93.77 21.8 6.1 6.2 3609
resnet101.a3_in1k 160 77.88 93.71 44.6 4.0 8.3 3926
resnet26t.ra2_in1k 256 77.87 93.84 16.0 3.4 10.5 3772
seresnext26ts.ch_in1k 256 77.86 93.79 10.4 2.4 10.5 4263
resnetrs50.tf_in1k 160 77.82 93.81 35.7 2.3 6.2 5238
gcresnext26ts.ch_in1k 256 77.81 93.82 10.5 2.4 10.5 4183
ecaresnet50t.a3_in1k 160 77.79 93.6 25.6 2.2 6.0 5329
resnext50_32x4d.a3_in1k 160 77.73 93.32 25.0 2.2 7.4 5576
resnext50_32x4d.tv_in1k 224 77.61 93.7 25.0 4.3 14.4 2944
seresnext26d_32x4d.bt_in1k 224 77.59 93.61 16.8 2.7 10.2 3807
resnet50.gluon_in1k 224 77.58 93.72 25.6 4.1 11.1 3455
eca_resnext26ts.ch_in1k 256 77.44 93.56 10.3 2.4 10.5 4284
resnet26d.bt_in1k 288 77.41 93.63 16.0 4.3 13.5 2907
resnet101.tv_in1k 224 77.38 93.54 44.6 7.8 16.2 2125
resnet50d.a3_in1k 160 77.22 93.27 25.6 2.2 6.1 5982
resnext26ts.ra2_in1k 288 77.17 93.47 10.3 3.1 13.3 3392
resnet34.a2_in1k 288 77.15 93.27 21.8 6.1 6.2 3615
resnet34d.ra2_in1k 224 77.1 93.37 21.8 3.9 4.5 5436
seresnet50.a3_in1k 224 77.02 93.07 28.1 4.1 11.1 2952
resnext26ts.ra2_in1k 256 76.78 93.13 10.3 2.4 10.5 4410
resnet26d.bt_in1k 224 76.7 93.17 16.0 2.6 8.2 4859
resnet34.bt_in1k 288 76.5 93.35 21.8 6.1 6.2 3617
resnet34.a1_in1k 224 76.42 92.87 21.8 3.7 3.7 5984
resnet26.bt_in1k 288 76.35 93.18 16.0 3.9 12.2 3331
resnet50.tv_in1k 224 76.13 92.86 25.6 4.1 11.1 3457
resnet50.a3_in1k 160 75.96 92.5 25.6 2.1 5.7 6490
resnet34.a2_in1k 224 75.52 92.44 21.8 3.7 3.7 5991
resnet26.bt_in1k 224 75.3 92.58 16.0 2.4 7.4 5583
resnet34.bt_in1k 224 75.16 92.18 21.8 3.7 3.7 5994
seresnet50.a3_in1k 160 75.1 92.08 28.1 2.1 5.7 5513
resnet34.gluon_in1k 224 74.57 91.98 21.8 3.7 3.7 5984
resnet18d.ra2_in1k 288 73.81 91.83 11.7 3.4 5.4 5196
resnet34.tv_in1k 224 73.32 91.42 21.8 3.7 3.7 5979
resnet18.fb_swsl_ig1b_ft_in1k 224 73.28 91.73 11.7 1.8 2.5 10213
resnet18.a1_in1k 288 73.16 91.03 11.7 3.0 4.1 6050
resnet34.a3_in1k 224 72.98 91.11 21.8 3.7 3.7 5967
resnet18.fb_ssl_yfcc100m_ft_in1k 224 72.6 91.42 11.7 1.8 2.5 10213
resnet18.a2_in1k 288 72.37 90.59 11.7 3.0 4.1 6051
resnet14t.c3_in1k 224 72.26 90.31 10.1 1.7 5.8 7026
resnet18d.ra2_in1k 224 72.26 90.68 11.7 2.1 3.3 8707
resnet18.a1_in1k 224 71.49 90.07 11.7 1.8 2.5 10187
resnet14t.c3_in1k 176 71.31 89.69 10.1 1.1 3.6 10970
resnet18.gluon_in1k 224 70.84 89.76 11.7 1.8 2.5 10210
resnet18.a2_in1k 224 70.64 89.47 11.7 1.8 2.5 10194
resnet34.a3_in1k 160 70.56 89.52 21.8 1.9 1.9 10737
resnet18.tv_in1k 224 69.76 89.07 11.7 1.8 2.5 10205
resnet10t.c3_in1k 224 68.34 88.03 5.4 1.1 2.4 13079
resnet18.a3_in1k 224 68.25 88.17 11.7 1.8 2.5 10167
resnet10t.c3_in1k 176 66.71 86.96 5.4 0.7 1.5 20327
resnet18.a3_in1k 160 65.66 86.26 11.7 0.9 1.3 18229

Citation 

@article{DBLP:journals/corr/ZagoruykoK16,
  author    = {Sergey Zagoruyko and
               Nikos Komodakis},
  title     = {Wide Residual Networks},
  journal   = {CoRR},
  volume    = {abs/1605.07146},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.07146},
  archivePrefix = {arXiv},
  eprint    = {1605.07146},
  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{He2015,
  author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  title = {Deep Residual Learning for Image Recognition},
  journal = {arXiv preprint arXiv:1512.03385},
  year = {2015}
}

@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}}
}
Downloads last month
3,246
Safetensors
Model size
69M params
Tensor type
F32
·
Inference Examples
Image Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.