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
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We summarize benchmark results in Markdown tables. You can convert them into other formats (e.g., LaTeX) with online tools.
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Models with * are converted from the corresponding official repos, others are trained by ourselves.
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For MogaNet [config], * denotes the refined training setting of lightweight models with 3-Augment.
Currently supported backbones
- AlexNet (NIPS'2012) [config]
- VGG (ICLR'2015) [config]
- InceptionV3 (CVPR'2016) [config]
- ResNet (CVPR'2016) [config]
- ResNeXt (CVPR'2017) [config]
- SE-ResNet (CVPR'2018) [config]
- SE-ResNeXt (CVPR'2018) [config]
- ShuffleNetV1 (CVPR'2018) [config]
- ShuffleNetV2 (ECCV'2018) [config]
- MobileNetV2 (CVPR'2018) [config]
- MobileNetV3 (ICCV'2019)
- EfficientNet (ICML'2019) [config]
- Res2Net (ArXiv'2019) [config]
- RegNet (CVPR'2020) [config]
- Vision-Transformer (ICLR'2021) [config]
- Swin-Transformer (ICCV'2021) [config]
- PVT (ICCV'2021) [config]
- T2T-ViT (ICCV'2021) [config]
- RepVGG (CVPR'2021) [config]
- DeiT (ICML'2021) [config]
- MLP-Mixer (NIPS'2021) [config]
- Twins (NIPS'2021) [config]
- ConvMixer (Openreview'2021) [config]
- UniFormer (ICLR'2022) [config]
- PoolFormer (CVPR'2022) [config]
- ConvNeXt (CVPR'2022) [config]
- MViTV2 (CVPR'2022) [config]
- RepMLP (CVPR'2022) [config]
- VAN (ArXiv'2022) [config]
- DeiT-3 (ECCV'2022) [config]
- LITv2 (NIPS'2022) [config]
- HorNet (NIPS'2022) [config]
- EdgeNeXt (ECCVW'2022) [config]
- EfficientFormer (ArXiv'2022) [config]
- MogaNet (ArXiv'2022) [config]
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 |