Convolutional Neural Networks
RepVGG
Introduced by Ding et al. in RepVGG: Making VGG-style ConvNets Great AgainRepVGG 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.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 3 | 27.27% |
| Quantization | 2 | 18.18% |
| Speaker Recognition | 1 | 9.09% |
| Classification | 1 | 9.09% |
| Image Segmentation | 1 | 9.09% |
| Multi-Task Learning | 1 | 9.09% |
| Network Pruning | 1 | 9.09% |
| Image Classification | 1 | 9.09% |
Batch Normalization
Convolution
Global Average Pooling
Linear Layer
ReLU
Residual Connection
