论文地址:RepVGG: Making VGG-style ConvNets Great Again

官方实现: DingXiaoH/RepVGG

自定义实现: ZJCV/ZCls


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 

我们实现了一个简单但是强大的卷积神经网络,在推导阶段,其实现类似于VGG类型,仅由3x3卷积和ReLU组成;在训练阶段,其实现是一个多分支拓扑结构。训练结构和推理结构的解耦是通过结构重参数化技术来实现的,因此该模型被命名为RepVGG。对于ImageNet数据集,RepVGG超过了80% top-1准确率,据我们所知,这是第一次使用简单的模型达到的效果。在NVIDIA 1080TI GPU上,RepVGG比ResNet-50快83%,比ResNet-101快101%(同时还有更高的精度)。相对于目前最好的EfficientNet/RegNet而言,RepVGG同样实现了一个很好的准确率-速度平衡。代码和预训练模型发布在: