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https://github.com/charlesCXK/TorchSemiSeg

[CVPR 2021] CPS: Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
https://github.com/charlesCXK/TorchSemiSeg

cvpr2021 semantic-segmentation semi-supervised-learning semi-supervised-segmentation

Last synced: 2 months ago
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[CVPR 2021] CPS: Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Host: GitHub
URL: https://github.com/charlesCXK/TorchSemiSeg
Owner: charlesCXK
License: mit
Created: 2021-06-02T06:04:20.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2023-05-21T14:07:27.000Z (over 1 year ago)
Last Synced: 2024-06-07T19:48:55.648Z (4 months ago)
Topics: cvpr2021, semantic-segmentation, semi-supervised-learning, semi-supervised-segmentation
Language: Python
Homepage:
Size: 956 KB
Stars: 486
Watchers: 7
Forks: 72
Open Issues: 20
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

Awesome-Semi-Supervised-Semantic-Segmentation - Code

README

        # TorchSemiSeg




[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semi-supervised-semantic-segmentation-with-3/semi-supervised-semantic-segmentation-on-2)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-2?p=semi-supervised-semantic-segmentation-with-3)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semi-supervised-semantic-segmentation-with-3/semi-supervised-semantic-segmentation-on-1)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-1?p=semi-supervised-semantic-segmentation-with-3)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semi-supervised-semantic-segmentation-with-3/semi-supervised-semantic-segmentation-on-8)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-8?p=semi-supervised-semantic-segmentation-with-3)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semi-supervised-semantic-segmentation-with-3/semi-supervised-semantic-segmentation-on-4)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-4?p=semi-supervised-semantic-segmentation-with-3)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semi-supervised-semantic-segmentation-with-3/semi-supervised-semantic-segmentation-on-9)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-9?p=semi-supervised-semantic-segmentation-with-3)

> [[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://arxiv.org/abs/2106.01226)

>

> by [Xiaokang Chen](https://charlescxk.github.io)¹, [Yuhui Yuan](https://scholar.google.com/citations?user=PzyvzksAAAAJ&hl=zh-CN)², [Gang Zeng](https://www.cis.pku.edu.cn/info/1177/1378.htm)¹, [Jingdong Wang](https://jingdongwang2017.github.io/)².

> 

> ¹ Key Laboratory of Machine Perception (MOE), Peking University

>² Microsoft Research Asia.

> 

> [[Poster](https://charlescxk.github.io/papers/CVPR2021_CPS/00446-poster.pdf)] [[Video (YouTube)](https://www.youtube.com/watch?v=5HKitm0O27w)]

>

> ***Simpler Is Better !***






## News

- **[July 9  2021] We have released some SOTA methods (Mean Teacher, CCT, GCT).**  

- **[June 3 2021] Please check our paper in [Arxiv](https://arxiv.org/abs/2106.01226). Data and code have been released.**  

## Installation

Please refer to the [Installation](./docs/installation.md) document.

## Getting Started

Please follow the [Getting Started](./docs/getting_started.md) document.

## Citation

Please consider citing this project in your publications if it helps your research.

```bibtex

@inproceedings{chen2021-CPS,

  title={Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision},

  author={Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong},

  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

  year={2021}

}

```

#### TODO

- [x] Dataset release

- [x] Code for CPS + CutMix

- [x] Code for Cityscapes dataset

- [x] Other SOTA semi-supervised segmentation methods