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ecaresnet269d.ra2_in1k

Image Classification
timm
PyTorch
Safetensors
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Model card for ecaresnet269d.ra2_in1k

A ECA-ResNet-D image classification model with Efficient Channel Attention.

This model features:

  • ReLU activations
  • 3-layer stem of 3x3 convolutions with pooling
  • 2x2 average pool + 1x1 convolution shortcut downsample
  • Efficient Channel Attention

Trained on ImageNet-1k in timm using recipe template described below.

Recipe details:

  • RandAugment RA2 recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as B recipe in ResNet Strikes Back.
  • RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
  • Step (exponential decay w/ staircase) LR schedule with warmup

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('ecaresnet269d.ra2_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(
    'ecaresnet269d.ra2_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, 160, 160])
    #  torch.Size([1, 256, 80, 80])
    #  torch.Size([1, 512, 40, 40])
    #  torch.Size([1, 1024, 20, 20])
    #  torch.Size([1, 2048, 10, 10])

    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(
    'ecaresnet269d.ra2_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, 10, 10) 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 

@inproceedings{wightman2021resnet,
  title={ResNet strikes back: An improved training procedure in timm},
  author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
  booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
}

@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}}
}

@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}
}

@InProceedings{wang2020eca,
  title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
  author={Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo and Qinghua Hu},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

@article{He2018BagOT,
  title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
  author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018},
  pages={558-567}
}
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