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

PyTorch for Semantic Segmentation
https://github.com/ZijunDeng/pytorch-semantic-segmentation

deep-learning fully-convolutional-networks pytorch semantic-segmentation

Last synced: 3 months ago
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PyTorch for Semantic Segmentation

• Host: GitHub
• URL: https://github.com/ZijunDeng/pytorch-semantic-segmentation
• Owner: zijundeng
• License: mit
• Created: 2017-06-02T06:45:33.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-10-25T09:05:41.000Z (over 5 years ago)
• Last Synced: 2024-08-04T00:12:01.746Z (6 months ago)
• Topics: deep-learning, fully-convolutional-networks, pytorch, semantic-segmentation
• Language: Python
• Homepage:
• Size: 1.55 MB
• Stars: 1,720
• Watchers: 41
• Forks: 395
• Open Issues: 49
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - pytorch-semantic-segmentation
• Awesome-pytorch-list - pytorch-semantic-segmentation

README

        
# 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](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf))

2. U-Net ([U-net: Convolutional networks for biomedical image segmentation](https://arxiv.org/pdf/1505.04597))

3. SegNet ([Segnet: A deep convolutional encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561))

4. PSPNet ([Pyramid scene parsing network](https://arxiv.org/pdf/1612.01105))

5. GCN ([Large Kernel Matters](https://arxiv.org/pdf/1703.02719))

6. DUC, HDC ([understanding convolution for semantic segmentation](https://arxiv.org/pdf/1702.08502.pdf))

## Requirement

1. PyTorch 0.2.0

2. TensorBoard for PyTorch. [Here](https://github.com/lanpa/tensorboard-pytorch)  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)