Shortcuts

Repvgg: Making vgg-style convnets great again

Abstract

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet.

Citation

@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}
}

Pretrain model

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.

Reparameterize RepVGG

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}
Read the Docs v: latest
Versions
master
latest
stable
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.