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Semantic Segmentation in PyTorch

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)
  7. Mask-RCNN (paper, code from FAIR, code PyTorch)

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

I'm going to implement The Image Segmentation Paper Top10 Net in PyTorch firstly.

  • DeepLab v3
  • RefineNet
  • ImageNet
  • GoogleNet
  • More dataset (e.g. ADE)

Citation

Use this bibtex to cite this repository:

@misc{PyTorch for Semantic Segmentation in Action,
  title={Some Implementation of Semantic Segmentation in PyTorch},
  author={Charmve},
  year={2020.10},
  publisher={Github},
  journal={GitHub repository},
  howpublished={\url{https://github.com/Charmve/Semantic-Segmentation-PyTorch}},
}