Charmve / Semantic-Segmentation-PyTorch

PyTorch implementation for Semantic Segmentation, include FCN, U-Net, SegNet, GCN, PSPNet, Deeplabv3, Deeplabv3+, Mask R-CNN, DUC, GoogleNet, and more dataset

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}},
}