Convolutional Neural Networks


Introduced by Ding et al. in RepVGG: Making VGG-style ConvNets Great Again

RepVGG is a VGG-style convolutional architecture. It has the following advantages:

  • The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes the output of its only preceding layer as input and feeds the output into its only following layer.
  • The model’s body uses only 3 × 3 conv and ReLU.
  • The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic search, manual refinement, compound scaling, nor other heavy designs.
Source: RepVGG: Making VGG-style ConvNets Great Again


Paper Code Results Date Stars


Task Papers Share
Network Pruning 1 33.33%
Image Classification 1 33.33%
Semantic Segmentation 1 33.33%