@inproceedings{ding2021repvgg,
title={Repvgg: Making vgg-style convnets great again},
author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13733--13742},
year={2021}
}| Model | Epochs | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
|---|---|---|---|---|---|---|---|
| RepVGG-A0* | 120 | 9.11（train) | 8.31 (deploy) | 1.52 (train) | 1.36 (deploy) | 72.41 | 90.50 | config (train) | config (deploy) | model |
| RepVGG-A1* | 120 | 14.09 (train) | 12.79 (deploy) | 2.64 (train) | 2.37 (deploy) | 74.47 | 91.85 | config (train) | config (deploy) | model |
| RepVGG-A2* | 120 | 28.21 (train) | 25.5 (deploy) | 5.7 (train) | 5.12 (deploy) | 76.48 | 93.01 | config (train) |config (deploy) | model |
| RepVGG-B0* | 120 | 15.82 (train) | 14.34 (deploy) | 3.42 (train) | 3.06 (deploy) | 75.14 | 92.42 | config (train) |config (deploy) | model |
| RepVGG-B1* | 120 | 57.42 (train) | 51.83 (deploy) | 13.16 (train) | 11.82 (deploy) | 78.37 | 94.11 | config (train) |config (deploy) | model |
| RepVGG-B1g2* | 120 | 45.78 (train) | 41.36 (deploy) | 9.82 (train) | 8.82 (deploy) | 77.79 | 93.88 | config (train) |config (deploy) | model |
| RepVGG-B1g4* | 120 | 39.97 (train) | 36.13 (deploy) | 8.15 (train) | 7.32 (deploy) | 77.58 | 93.84 | config (train) |config (deploy) | model |
| RepVGG-B2* | 120 | 89.02 (train) | 80.32 (deploy) | 20.46 (train) | 18.39 (deploy) | 78.78 | 94.42 | config (train) |config (deploy) | model |
| RepVGG-B2g4* | 200 | 61.76 (train) | 55.78 (deploy) | 12.63 (train) | 11.34 (deploy) | 79.38 | 94.68 | config (train) |config (deploy) | model |
| RepVGG-B3* | 200 | 123.09 (train) | 110.96 (deploy) | 29.17 (train) | 26.22 (deploy) | 80.52 | 95.26 | config (train) |config (deploy) | model |
| RepVGG-B3g4* | 200 | 83.83 (train) | 75.63 (deploy) | 17.9 (train) | 16.08 (deploy) | 80.22 | 95.10 | config (train) |config (deploy) | model |
| RepVGG-D2se* | 200 | 133.33 (train) | 120.39 (deploy) | 36.56 (train) | 32.85 (deploy) | 81.81 | 95.94 | config (train) |config (deploy) | model |
Models with * are converted from other repos.
The checkpoints provided are all in train form. Use the reparameterize tool to switch them to more efficient deploy form, which not only has fewer parameters but also less calculations.
python ./tools/convert_models/reparameterize_repvgg.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH}${CFG_PATH} is the config file, ${SRC_CKPT_PATH} is the source chenpoint file, ${TARGET_CKPT_PATH} is the target deploy weight file path.
To use reparameterized repvgg weight, the config file must switch to the deploy config files as below:
python ./tools/test.py ${RapVGG_Deploy_CFG} ${CHECK_POINT}