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Model Zoo of Supervised Learning

Some of the current results of supervised learning benchmarks are based on MMClassification. We will rerun the experiments and update more reliable results soon!

Note

  • We summarize benchmark results in Markdown tables. You can convert them into other formats (e.g., LaTeX) with online tools.

  • Models with * are converted from the corresponding official repos, others are trained by ourselves.

  • For MogaNet [config], * denotes the refined training setting of lightweight models with 3-Augment.

    Currently supported backbones

ImageNet

ImageNet has multiple versions, but the most commonly used one is ILSVRC 2012. We summarize image classification results of the official settings. You can download model files from OpenMMLab or OpenMixup.

Model Params(M) Flops(G) Top-1 (%) Top-5 (%) Config Download
AlexNet 61.1 0.72 62.5 83.0 config
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 70.67 90.16 config model | log
VGG-13-BN 133.05 11.36 72.12 90.66 config model | log
VGG-16-BN 138.37 15.53 73.74 91.66 config model | log
VGG-19-BN 143.68 19.7 74.68 92.27 config model | log
Inception V3* 23.83 5.75 77.57 93.58 config model
ResNet-18 11.69 1.82 69.90 89.43 config model | log
ResNet-34 21.8 3.68 73.62 91.59 config model | log
ResNet-50 25.56 4.12 76.55 93.06 config model | log
ResNet-101 44.55 7.85 77.97 94.06 config model | log
ResNet-152 60.19 11.58 78.48 94.13 config model | log
ResNetV1C-50 25.58 4.36 77.01 93.58 config model | log
ResNetV1C-101 44.57 8.09 78.30 94.27 config model | log
ResNetV1C-152 60.21 11.82 78.76 94.41 config model | log
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 94.70 config model | log
ResNet-50 (fp16) 25.56 4.12 76.30 93.07 config model | log
Wide-ResNet-50* 68.88 11.44 78.48 94.08 config model
Wide-ResNet-101* 126.89 22.81 78.84 94.28 config model
ResNet-50 (rsb-a1) 25.56 4.12 80.12 94.78 config model | log
ResNet-50 (rsb-a2) 25.56 4.12 79.55 94.37 config model | log
ResNet-50 (rsb-a3) 25.56 4.12 78.30 93.80 config model | log
ShuffleNetV1 1.0x 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
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
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
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
RegNetX-400MF* 5.16 0.41 72.55 90.91 config model
RegNetX-800MF* 7.26 0.81 75.21 92.37 config model
RegNetX-1.6GF* 9.19 1.63 77.04 93.51 config model
RegNetX-3.2GF* 15.3 3.21 78.26 94.20 config model
RegNetX-4.0GF* 22.12 4.0 78.72 94.22 config model
RegNetX-6.4GF* 26.21 6.51 79.22 94.61 config model
RegNetX-8.0GF* 39.57 8.03 79.31 94.57 config model
RegNetX-12GF* 46.11 12.15 79.91 94.78 config model
ViT-B16* 86.86 33.03 85.43 97.77 config model
ViT-B32* 88.30 8.56 84.01 97.08 config model
ViT-L16* 304.72 116.68 85.63 97.63 config model
Swin-T 28.29 4.36 81.18 95.61 config model | log
Swin-S 49.61 8.52 83.02 96.29 config model | log
Swin-B 87.77 15.14 83.36 96.44 config model | log
PVT-Tiny* 13.2 1.60 75.1 - config model / log
PVT-Small* 24.5 3.80 79.8 - config model / log
PVT-Medium* 44.2 6.70 81.2 - config model / log
PVT-Large* 61.2 9.80 81.7 - config model / log
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
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-D2se* 133.33 (train) | 120.39 (deploy) 36.56 (train) | 32.85 (deploy) 81.81 95.94 config (train) |config (deploy) model
DeiT-T 5.72 1.08 73.56 91.16 config model
DeiT-T distilled* 5.72 1.08 72.20 91.10 config model
DeiT-S 22.05 4.24 79.93 95.14 config model
DeiT-S distilled* 22.05 4.24 79.90 95.10 config model
DeiT-B 86.57 16.86 81.82 95.57 config model
DeiT-B distilled* 86.57 16.86 81.80 95.60 config model
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
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
ConvMixer-768/32* 21.11 19.62 80.16 95.08 config model
ConvMixer-1024/20* 24.38 5.55 76.94 93.36 config model
ConvMixer-1536/20* 51.63 48.71 81.37 95.61 config model
UniFormer-T 5.55 0.88 78.02 - config model / log
UniFormer-S 21.5 3.44 82.56 - config model / log
UniFormer-S* 21.5 3.44 82.90 - config model
UniFormer-S + ConvStem* 24.0 4.21 83.40 - config model
UniFormer-B* 49.8 8.27 83.90 - config model
UniFormer-S + Token Labeling* 21.5 3.44 83.40 - config model
UniFormer-B + Token Labeling* 49.8 8.27 85.10 - config model
PoolFormer-S12* 11.92 1.87 77.24 93.51 config model
PoolFormer-S24* 21.39 3.51 80.33 95.05 config model
PoolFormer-S36* 30.86 5.15 81.43 95.45 config model
PoolFormer-M36* 56.17 8.96 82.14 95.71 config model
PoolFormer-M48* 73.47 11.80 82.51 95.95 config model
ConvNeXt-T 28.59 4.46 82.16 95.91 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-L* 197.77 34.37 84.30 96.89 config model
ConvNeXt-XL* 350.20 60.93 86.97 98.20 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
RepMLP-B224* 68.24 6.71 80.41 95.12 train_cfg | deploy_cfg model
RepMLP-B256* 96.45 9.69 81.11 95.5 train_cfg | deploy_cfg model
VAN-T 4.11 0.88 75.77 92.99 config model / log
VAN-T* 4.11 0.88 75.41 93.02 config model
VAN-S 13.86 2.52 81.03 95.56 config model / log
VAN-S* 13.86 2.52 81.01 95.63 config model
VAN-B 26.58 5.03 82.65 96.17 config model / log
VAN-B* 26.58 5.03 82.80 96.21 config model
VAN-L* 44.77 8.99 83.86 96.73 config model
VAN-B4* 60.28 12.22 84.13 96.86 config model
LITv2-S 28 3.7 81.7 - config model / log
LITv2-S* 28 3.7 82.0 - config model / log
LITv2-M* 49 7.5 83.3 - config model / log
LITv2-B* 87 13.2 84.7 - config model / log
HorNet-T* 22.41 3.98 82.84 96.24 config model
HorNet-T-GF* 22.99 3.9 82.98 96.38 config model
HorNet-S* 49.53 8.83 83.79 96.75 config model
HorNet-S-GF* 50.4 8.71 83.98 96.77 config model
HorNet-B* 87.26 15.59 84.24 96.94 config model
HorNet-B-GF* 88.42 15.42 84.32 96.95 config model
EdgeNeXt-Base* 18.51 3.84 82.48 96.2 config model
EdgeNeXt-Small* 5.59 1.26 79.41 94.53 config model
EdgeNeXt-X-Small* 2.34 0.538 74.86 92.31 config model
EdgeNeXt-XX-Small* 1.33 0.261 71.2 89.91 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
MogaNet-XT 2.97 0.80 76.5 - config model / log
MogaNet-XT 2.97 1.04 77.2 - config model / log
MogaNet-XT* 2.97 1.04 77.6 - config model / log
MogaNet-T 5.20 1.10 79.0 - config model / log
MogaNet-T 5.20 1.44 79.6 - config model / log
MogaNet-T* 5.20 1.44 80.0 - config model / log
MogaNet-S 25.3 4.97 83.4 - config model / log
MogaNet-B 43.9 9.93 84.2 - config model / log
MogaNet-L 82.5 15.9 84.6 - config model / log

We also provide fast training results using RSB A3 setting on ILSVRC 2012. You can download all files from GitHub / Baidu Cloud (ss3j).

Model Date Train / test size Params(M) Top-1 (\%) Top-5 (\%) Config Download
ResNet-50 CVPR'2016 160 / 224 26 78.1 93.8 config model | log
ResNet-101 CVPR'2016 160 / 224 45 79.9 94.9 config model | log
ResNet-152 CVPR'2016 160 / 224 60 80.7 95.2 config model | log
ViT-T ICLR'2021 160 / 224 6 66.7 87.7 config model | log
ViT-S ICLR'2021 160 / 224 22 73.8 91.2 config model | log
ViT-B ICLR'2021 160 / 224 87 76.0 91.8 config model | log
PVT-T ICCV'2021 160 / 224 13 71.5 89.8 config model | log
PVT-S ICCV'2021 160 / 224 25 72.1 90.2 config model | log
Swin-T ICCV'2021 160 / 224 28 77.7 93.7 config model | log
Swin-S ICCV'2021 160 / 224 50 80.2 95.1 config model | log
Swin-B ICCV'2021 160 / 224 50 80.5 95.4 config model | log
LITV2-T NIPS'2022 160 / 224 28 79.7 94.7 config model | log
LITV2-M NIPS'2022 160 / 224 49 80.5 95.2 config model | log
LITV2-B NIPS'2022 160 / 224 87 81.3 95.5 config model | log
ConvMixer-768-d32 arXiv'2022 160 / 224 21 77.6 93.5 config model | log
PoolFormer-S12 CVPR'2022 160 / 224 12 69.3 88.7 config model | log
PoolFormer-S24 CVPR'2022 160 / 224 21 74.1 91.8 config model | log
PoolFormer-S36 CVPR'2022 160 / 224 31 74.6 92.0 config model | log
PoolFormer-M36 CVPR'2022 160 / 224 56 80.7 95.2 config model | log
PoolFormer-M48 CVPR'2022 160 / 224 73 81.2 95.3 config model | log
ConvNeXt-T CVPR'2022 160 / 224 29 78.8 94.2 config model | log
ConvNeXt-S CVPR'2022 160 / 224 50 81.7 95.7 config model | log
ConvNeXt-B CVPR'2022 160 / 224 89 82.1 95.9 config model | log
ConvNeXt-L CVPR'2022 160 / 224 189 82.8 96.0 config model | log
VAN-B0 arXiv'2022 160 / 224 4 72.6 94.2 config model | log
VAN-B2 arXiv'2022 160 / 224 27 81.0 91.5 config model | log
VAN-B3 arXiv'2022 160 / 224 45 81.9 95.7 config model | log
HorNet-T (7×7) NIPS'2022 160 / 224 22 80.1 95.0 config model | log
HorNet-S (7×7) NIPS'2022 160 / 224 50 81.2 95.4 config model | log
MogaNet-XT arXiv'2022 160 / 224 3 72.8 91.3 config model | log
MogaNet-T arXiv'2022 160 / 224 5 75.4 92.6 config model | log
MogaNet-S arXiv'2022 160 / 224 25 81.1 95.5 config model | log
MogaNet-B arXiv'2022 160 / 224 44 82.2 95.9 config model | log
MogaNet-L arXiv'2022 160 / 224 83 83.2 96.4 config model | log
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