Model card for ecaresnet101d.miil_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 by Alibaba MIIL.
Model Details
- Model Type: Image classification / feature backbone
- Model Stats:
- Params (M): 44.6
- GMACs: 8.1
- Activations (M): 17.1
- Image size: train = 224 x 224, test = 288 x 288
- Papers:
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks: https://arxiv.org/abs/1910.03151
- Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187
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('ecaresnet101d.miil_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(
'ecaresnet101d.miil_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(
'ecaresnet101d.miil_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{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}
}
@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}}
}
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