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ImageNet

ImageNet has multiple versions, but the most commonly used one is ILSVRC 2012. The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.

Model Params(M) Flops(G) Top-1 (%) Top-5 (%) Config Download
VGG-11 132.86 7.63 68.75 88.87 config model | log
VGG-13 133.05 11.34 70.02 89.46 config model | log
VGG-16 138.36 15.5 71.62 90.49 config model | log
VGG-19 143.67 19.67 72.41 90.80 config model | log
VGG-11-BN 132.87 7.64 70.75 90.12 config model | log
VGG-13-BN 133.05 11.36 72.15 90.71 config model | log
VGG-16-BN 138.37 15.53 73.72 91.68 config model | log
VGG-19-BN 143.68 19.7 74.70 92.24 config model | log
RepVGG-A0* 9.11(train) | 8.31 (deploy) 1.52 (train) | 1.36 (deploy) 72.41 90.50 config (train) | config (deploy) model
RepVGG-A1* 14.09 (train) | 12.79 (deploy) 2.64 (train) | 2.37 (deploy) 74.47 91.85 config (train) | config (deploy) model
RepVGG-A2* 28.21 (train) | 25.5 (deploy) 5.7 (train) | 5.12 (deploy) 76.48 93.01 config (train) | config (deploy) model
RepVGG-B0* 15.82 (train) | 14.34 (deploy) 3.42 (train) | 3.06 (deploy) 75.14 92.42 config (train) | config (deploy) model
RepVGG-B1* 57.42 (train) | 51.83 (deploy) 13.16 (train) | 11.82 (deploy) 78.37 94.11 config (train) | config (deploy) model
RepVGG-B1g2* 45.78 (train) | 41.36 (deploy) 9.82 (train) | 8.82 (deploy) 77.79 93.88 config (train) | config (deploy) model
RepVGG-B1g4* 39.97 (train) | 36.13 (deploy) 8.15 (train) | 7.32 (deploy) 77.58 93.84 config (train) | config (deploy) model
RepVGG-B2* 89.02 (train) | 80.32 (deploy) 20.46 (train) | 18.39 (deploy) 78.78 94.42 config (train) | config (deploy) model
RepVGG-B2g4* 61.76 (train) | 55.78 (deploy) 12.63 (train) | 11.34 (deploy) 79.38 94.68 config (train) | config (deploy) model
RepVGG-B3* 123.09 (train) | 110.96 (deploy) 29.17 (train) | 26.22 (deploy) 80.52 95.26 config (train) | config (deploy) model
RepVGG-B3g4* 83.83 (train) | 75.63 (deploy) 17.9 (train) | 16.08 (deploy) 80.22 95.10 config (train) | config (deploy) model
RepVGG-D2se* 133.33 (train) | 120.39 (deploy) 36.56 (train) | 32.85 (deploy) 81.81 95.94 config (train) | config (deploy) model
ResNet-18 11.69 1.82 70.07 89.44 config model | log
ResNet-34 21.8 3.68 73.85 91.53 config model | log
ResNet-50 (rsb-a1) 25.56 4.12 80.12 94.78 config model | log
ResNet-101 44.55 7.85 78.18 94.03 config model | log
ResNet-152 60.19 11.58 78.63 94.16 config model | log
Res2Net-50-14w-8s* 25.06 4.22 78.14 93.85 config model
Res2Net-50-26w-8s* 48.40 8.39 79.20 94.36 config model
Res2Net-101-26w-4s* 45.21 8.12 79.19 94.44 config model
ResNeSt-50* 27.48 5.41 81.13 95.59 config model
ResNeSt-101* 48.28 10.27 82.32 96.24 config model
ResNeSt-200* 70.2 17.53 82.41 96.22 config model
ResNeSt-269* 110.93 22.58 82.70 96.28 config model
ResNetV1D-50 25.58 4.36 77.54 93.57 config model | log
ResNetV1D-101 44.57 8.09 78.93 94.48 config model | log
ResNetV1D-152 60.21 11.82 79.41 94.7 config model | log
ResNeXt-32x4d-50 25.03 4.27 77.90 93.66 config model | log
ResNeXt-32x4d-101 44.18 8.03 78.71 94.12 config model | log
ResNeXt-32x8d-101 88.79 16.5 79.23 94.58 config model | log
ResNeXt-32x4d-152 59.95 11.8 78.93 94.41 config model | log
SE-ResNet-50 28.09 4.13 77.74 93.84 config model | log
SE-ResNet-101 49.33 7.86 78.26 94.07 config model | log
RegNetX-400MF 5.16 0.41 72.56 90.78 config model | log
RegNetX-800MF 7.26 0.81 74.76 92.32 config model | log
RegNetX-1.6GF 9.19 1.63 76.84 93.31 config model | log
RegNetX-3.2GF 15.3 3.21 78.09 94.08 config model | log
RegNetX-4.0GF 22.12 4.0 78.60 94.17 config model | log
RegNetX-6.4GF 26.21 6.51 79.38 94.65 config model | log
RegNetX-8.0GF 39.57 8.03 79.12 94.51 config model | log
RegNetX-12GF 46.11 12.15 79.67 95.03 config model | log
ShuffleNetV1 1.0x (group=3) 1.87 0.146 68.13 87.81 config model | log
ShuffleNetV2 1.0x 2.28 0.149 69.55 88.92 config model | log
MobileNet V2 3.5 0.319 71.86 90.42 config model | log
ViT-B/16* 86.86 33.03 85.43 97.77 config model
ViT-B/32* 88.3 8.56 84.01 97.08 config model
ViT-L/16* 304.72 116.68 85.63 97.63 config model
Swin-Transformer tiny 28.29 4.36 81.18 95.61 config model | log
Swin-Transformer small 49.61 8.52 83.02 96.29 config model | log
Swin-Transformer base 87.77 15.14 83.36 96.44 config model | log
Transformer in Transformer small* 23.76 3.36 81.52 95.73 config model
T2T-ViT_t-14 21.47 4.34 81.83 95.84 config model | log
T2T-ViT_t-19 39.08 7.80 82.63 96.18 config model | log
T2T-ViT_t-24 64.00 12.69 82.71 96.09 config model | log
Mixer-B/16* 59.88 12.61 76.68 92.25 config model
Mixer-L/16* 208.2 44.57 72.34 88.02 config model
DeiT-tiny 5.72 1.08 74.50 92.24 config model | log
DeiT-tiny distilled* 5.72 1.08 74.51 91.90 config model
DeiT-small 22.05 4.24 80.69 95.06 config model | log
DeiT-small distilled* 22.05 4.24 81.17 95.40 config model
DeiT-base 86.57 16.86 81.76 95.81 config model | log
DeiT-base distilled* 86.57 16.86 83.33 96.49 config model
DeiT-base 384px* 86.86 49.37 83.04 96.31 config model
DeiT-base distilled 384px* 86.86 49.37 85.55 97.35 config model
Conformer-tiny-p16* 23.52 4.90 81.31 95.60 config model
Conformer-small-p32* 38.85 7.09 81.96 96.02 config model
Conformer-small-p16* 37.67 10.31 83.32 96.46 config model
Conformer-base-p16* 83.29 22.89 83.82 96.59 config model
PCPVT-small* 24.11 3.67 81.14 95.69 config model
PCPVT-base* 43.83 6.45 82.66 96.26 config model
PCPVT-large* 60.99 9.51 83.09 96.59 config model
SVT-small* 24.06 2.82 81.77 95.57 config model
SVT-base* 56.07 8.35 83.13 96.29 config model
SVT-large* 99.27 14.82 83.60 96.50 config model
EfficientNet-B0* 5.29 0.02 76.74 93.17 config model
EfficientNet-B0 (AA)* 5.29 0.02 77.26 93.41 config model
EfficientNet-B0 (AA + AdvProp)* 5.29 0.02 77.53 93.61 config model
EfficientNet-B1* 7.79 0.03 78.68 94.28 config model
EfficientNet-B1 (AA)* 7.79 0.03 79.20 94.42 config model
EfficientNet-B1 (AA + AdvProp)* 7.79 0.03 79.52 94.43 config model
EfficientNet-B2* 9.11 0.03 79.64 94.80 config model
EfficientNet-B2 (AA)* 9.11 0.03 80.21 94.96 config model
EfficientNet-B2 (AA + AdvProp)* 9.11 0.03 80.45 95.07 config model
EfficientNet-B3* 12.23 0.06 81.01 95.34 config model
EfficientNet-B3 (AA)* 12.23 0.06 81.58 95.67 config model
EfficientNet-B3 (AA + AdvProp)* 12.23 0.06 81.81 95.69 config model
EfficientNet-B4* 19.34 0.12 82.57 96.09 config model
EfficientNet-B4 (AA)* 19.34 0.12 82.95 96.26 config model
EfficientNet-B4 (AA + AdvProp)* 19.34 0.12 83.25 96.44 config model
EfficientNet-B5* 30.39 0.24 83.18 96.47 config model
EfficientNet-B5 (AA)* 30.39 0.24 83.82 96.76 config model
EfficientNet-B5 (AA + AdvProp)* 30.39 0.24 84.21 96.98 config model
EfficientNet-B6 (AA)* 43.04 0.41 84.05 96.82 config model
EfficientNet-B6 (AA + AdvProp)* 43.04 0.41 84.74 97.14 config model
EfficientNet-B7 (AA)* 66.35 0.72 84.38 96.88 config model
EfficientNet-B7 (AA + AdvProp)* 66.35 0.72 85.14 97.23 config model
EfficientNet-B8 (AA + AdvProp)* 87.41 1.09 85.38 97.28 config model
ConvNeXt-T* 28.59 4.46 82.05 95.86 config model
ConvNeXt-S* 50.22 8.69 83.13 96.44 config model
ConvNeXt-B* 88.59 15.36 83.85 96.74 config model
ConvNeXt-B* 88.59 15.36 85.81 97.86 config model
ConvNeXt-L* 197.77 34.37 84.30 96.89 config model
ConvNeXt-L* 197.77 34.37 86.61 98.04 config model
ConvNeXt-XL* 350.20 60.93 86.97 98.20 config model
HRNet-W18* 21.30 4.33 76.75 93.44 config model
HRNet-W30* 37.71 8.17 78.19 94.22 config model
HRNet-W32* 41.23 8.99 78.44 94.19 config model
HRNet-W40* 57.55 12.77 78.94 94.47 config model
HRNet-W44* 67.06 14.96 78.88 94.37 config model
HRNet-W48* 77.47 17.36 79.32 94.52 config model
HRNet-W64* 128.06 29.00 79.46 94.65 config model
HRNet-W18 (ssld)* 21.30 4.33 81.06 95.70 config model
HRNet-W48 (ssld)* 77.47 17.36 83.63 96.79 config model
WRN-50* 68.88 11.44 81.45 95.53 config model
WRN-101* 126.89 22.81 78.84 94.28 config model
CSPDarkNet50* 27.64 5.04 80.05 95.07 config model
CSPResNet50* 21.62 3.48 79.55 94.68 config model
CSPResNeXt50* 20.57 3.11 79.96 94.96 config model
DenseNet121* 7.98 2.88 74.96 92.21 config model
DenseNet169* 14.15 3.42 76.08 93.11 config model
DenseNet201* 20.01 4.37 77.32 93.64 config model
DenseNet161* 28.68 7.82 77.61 93.83 config model
VAN-T* 4.11 0.88 75.41 93.02 config model
VAN-S* 13.86 2.52 81.01 95.63 config model
VAN-B* 26.58 5.03 82.80 96.21 config model
VAN-L* 44.77 8.99 83.86 96.73 config model
MViTv2-tiny* 24.17 4.70 82.33 96.15 config model
MViTv2-small* 34.87 7.00 83.63 96.51 config model
MViTv2-base* 51.47 10.20 84.34 96.86 config model
MViTv2-large* 217.99 42.10 85.25 97.14 config model
EfficientFormer-l1* 12.19 1.30 80.46 94.99 config model
EfficientFormer-l3* 31.41 3.93 82.45 96.18 config model
EfficientFormer-l7* 82.23 10.16 83.40 96.60 config model

Models with * are converted from other repos, others are trained by ourselves.

CIFAR10

Model Params(M) Flops(G) Top-1 (%) Config Download
ResNet-18-b16x8 11.17 0.56 94.82 config
ResNet-34-b16x8 21.28 1.16 95.34 config
ResNet-50-b16x8 23.52 1.31 95.55 config
ResNet-101-b16x8 42.51 2.52 95.58 config
ResNet-152-b16x8 58.16 3.74 95.76 config
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