Papers › RepVGG: Making VGG-style ConvNets Great Again
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun
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. The code and trained models are available at https://github.com/megvii-model/RepVGG.
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Code
Syntology Ran 13 of 16 code samples harvested from 8 repositories linked to this paper; 3 have no recorded run. Of those that ran: 4 ran · our draft was wrong; 1 ran · fixture could not drive it; 8 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | RepVGG-B2 | GFLOPs | 18.4 | #807 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | RepVGG-B2 | Number of params | 80.31M | #807 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | RepVGG-B2 | Top 1 Accuracy | 78.78% | #807 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | RepVGG-B2g4 | GFLOPs | 11.3 | #829 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | RepVGG-B2g4 | Number of params | 55.77M | #829 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | RepVGG-B2g4 | Top 1 Accuracy | 78.5% | #829 of 1060 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | RepVGG-B2 | mIoU | 80.57% | #49 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: RepVGG
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