PyTorch for Semantic Segmentation

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

Models

  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)

Requirement

  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)

Preparation

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

TODO

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

GitHub

https://github.com/ZijunDeng/pytorch-semantic-segmentation