RepVGG: Making VGG-Style ConvNets Great Again

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inferencetime body composed of nothing but a stack of 3 × 3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the trainingtime 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 stateof-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github . com/megvii-model/RepVGG.