  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.
