RepVGG: Making VGG-style ConvNets Great Again
This work provides a more efficient and accurate convolutional neural network architecture for researchers and practitioners in computer vision, particularly those focused on image classification.
This paper introduces RepVGG, a convolutional neural network architecture that achieves over 80% top-1 accuracy on ImageNet with a simple VGG-like inference structure. It also demonstrates significant speed improvements, running 83% faster than ResNet-50 and 101% faster than ResNet-101 while maintaining higher accuracy.
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.