PyTorch for Semantic Segmentation
Branch: master
Clone or download
Latest commit 4a1721f Nov 19, 2017
Type Name Latest commit message Commit time
Failed to load latest commit information.
datasets Merge remote-tracking branch 'origin/master' Nov 19, 2017
eval running 2to3 Nov 6, 2017
models Merge remote-tracking branch 'origin/master' Nov 19, 2017
train update training code Nov 19, 2017
utils Merge remote-tracking branch 'origin/master' Nov 19, 2017
LICENSE Create LICENSE Oct 5, 2017 update training codes of PSPNet Aug 31, 2017

PyTorch for Semantic Segmentation

This repository contains some models for semantic segmentation and the pipeline of training and testing models, implemented in PyTorch


  1. Vanilla FCN: FCN32, FCN16, FCN8, in the versions of VGG, ResNet and DenseNet respectively (Fully convolutional networks for semantic segmentation)
  2. U-Net (U-net: Convolutional networks for biomedical image segmentation)
  3. SegNet (Segnet: A deep convolutional encoder-decoder architecture for image segmentation)
  4. PSPNet (Pyramid scene parsing network)
  5. GCN (Large Kernel Matters)
  6. DUC, HDC (understanding convolution for semantic segmentation)


  1. PyTorch 0.2.0
  2. TensorBoard for PyTorch. Here to install
  3. Some other libraries (find what you miss when running the code :-P)


  1. Go to models directory and set the path of pretrained models in
  2. Go to datasets directory and do following the README


  1. DeepLab v3
  2. RefineNet
  3. More dataset (e.g. ADE)