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(with code)","2021"],"sub_categories":[],"readme":"# TorchSemiSeg\n\u003cbr\u003e\n\n[![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)\n[![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)\n[![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)\n[![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)\n[![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)\n\n\u003e [[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://arxiv.org/abs/2106.01226)\n\u003e\n\u003e by [Xiaokang Chen](https://charlescxk.github.io)\u003csup\u003e1\u003c/sup\u003e, [Yuhui Yuan](https://scholar.google.com/citations?user=PzyvzksAAAAJ\u0026hl=zh-CN)\u003csup\u003e2\u003c/sup\u003e, [Gang Zeng](https://www.cis.pku.edu.cn/info/1177/1378.htm)\u003csup\u003e1\u003c/sup\u003e, [Jingdong Wang](https://jingdongwang2017.github.io/)\u003csup\u003e2\u003c/sup\u003e.\n\u003e \n\u003e \u003csup\u003e1\u003c/sup\u003e Key Laboratory of Machine Perception (MOE), Peking University\n\u003e\u003csup\u003e2\u003c/sup\u003e Microsoft Research Asia.\n\u003e \n\u003e [[Poster](https://charlescxk.github.io/papers/CVPR2021_CPS/00446-poster.pdf)] [[Video (YouTube)](https://www.youtube.com/watch?v=5HKitm0O27w)]\n\u003e\n\u003e ***Simpler Is Better !***\n\n\u003cbr\u003e\n\n\u003cimg src=ReadmePic/cps.png width=\"600\"\u003e\n\n## News\n- **[July 9  2021] We have released some SOTA methods (Mean Teacher, CCT, GCT).**  \n- **[June 3 2021] Please check our paper in [Arxiv](https://arxiv.org/abs/2106.01226). Data and code have been released.**  \n\n\n## Installation\nPlease refer to the [Installation](./docs/installation.md) document.\n\n## Getting Started\nPlease follow the [Getting Started](./docs/getting_started.md) document.\n\n\n## Citation\n\nPlease consider citing this project in your publications if it helps your research.\n\n```bibtex\n@inproceedings{chen2021-CPS,\n  title={Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision},\n  author={Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong},\n  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n  year={2021}\n}\n```\n\n#### TODO\n- [x] Dataset release\n- [x] Code for CPS + CutMix\n- [x] Code for Cityscapes dataset\n- [x] Other SOTA semi-supervised segmentation methods\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FcharlesCXK%2FTorchSemiSeg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FcharlesCXK%2FTorchSemiSeg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FcharlesCXK%2FTorchSemiSeg/lists"}