# UniMatch **Repository Path**: yeqiuyi/UniMatch ## Basic Information - **Project Name**: UniMatch - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-04-28 - **Last Updated**: 2025-04-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # UniMatch [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-21)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-21?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-4)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-4?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-27)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-27?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-29)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-29?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-10)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-10?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-22)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-22?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-2)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-2?p=revisiting-weak-to-strong-consistency-in-semi) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/revisiting-weak-to-strong-consistency-in-semi/semi-supervised-semantic-segmentation-on-1)](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-1?p=revisiting-weak-to-strong-consistency-in-semi) [**News: Check our latest UniMatch V2 framework!**](https://github.com/LiheYoung/UniMatch-V2) This codebase contains a strong re-implementation of FixMatch in the field of semi-supervised semantic segmentation, as well as the official PyTorch implementation of our UniMatch in the **[natural](https://github.com/LiheYoung/UniMatch), [remote sensing](https://github.com/LiheYoung/UniMatch/tree/main/more-scenarios/remote-sensing), and [medical](https://github.com/LiheYoung/UniMatch/tree/main/more-scenarios/medical) scenarios**. > **[Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://arxiv.org/abs/2208.09910)**
> Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi
> *In Conference on Computer Vision and Pattern Recognition (CVPR), 2023* **We provide a list of [Awesome Semi-Supervised Semantic Segmentation](./docs/SemiSeg.md) works.**

## Results **You can check our [training logs](https://github.com/LiheYoung/UniMatch/blob/main/training-logs) for convenient comparisons during reproducing.** **Note: we have added and updated some results in our camera-ready version. Please refer to our [latest version](https://arxiv.org/abs/2208.09910)**. ### Pascal VOC 2012 Labeled images are sampled from the **original high-quality** training set. Results are obtained by DeepLabv3+ based on ResNet-101 with training size 321. | Method | 1/16 (92) | 1/8 (183) | 1/4 (366) | 1/2 (732) | Full (1464) | | :-------------------------: | :-------: | :-------: | :-------: | :-------: | :---------: | | SupBaseline | 45.1 | 55.3 | 64.8 | 69.7 | 73.5 | | U²PL | 68.0 | 69.2 | 73.7 | 76.2 | 79.5 | | ST++ | 65.2 | 71.0 | 74.6 | 77.3 | 79.1 | | PS-MT | 65.8 | 69.6 | 76.6 | 78.4 | 80.0 | | **UniMatch (Ours)** | **75.2** | **77.2** | **78.8** | **79.9** | **81.2** | ### Cityscapes Results are obtained by DeepLabv3+ based on ResNet-50/101. We reproduce U²PL results on ResNet-50. **Note: the results differ from our arXiv-V1 because we change the confidence threshold from 0.95 to 0, and change the ResNet output stride from 8 to 16. Therefore, it is currently more efficient to run.** *You can click on the numbers to be directed to corresponding checkpoints.* | ResNet-50 | 1/16 | 1/8 | 1/4 | 1/2 | ResNet-101 | 1/16 | 1/8 | 1/4 | 1/2 | | :-------------------------: | :-------: | :-------: | :-------: | :-------: | :------------------: | :---------: | :---------: | :---------: | :---------: | | SupBaseline | 63.3 | 70.2 | 73.1 | 76.6 | SupBaseline | 66.3 | 72.8 | 75.0 | 78.0 | | U²PL | 70.6 | 73.0 | 76.3 | 77.2 | U²PL | 74.9 | 76.5 | 78.5 | 79.1 | | **UniMatch (Ours)** | [**75.0**](https://drive.google.com/file/d/1J-GjeZRhIhnbxtD8f_lDflXB24S1E995/view?usp=sharing) | [**76.8**](https://drive.google.com/file/d/1pA-enIDGWSVyhJg7SFIjFQ-nlxetj6-m/view?usp=sharing) | [**77.5**](https://drive.google.com/file/d/1EEh8XMljUf40wzMblnv9Ez9_dfXYqO7P/view?usp=sharing) | [**78.6**](https://drive.google.com/file/d/18Bd43RsXhTw9RL3F9Vn9lz_Gs5KQWaTE/view?usp=sharing) | **UniMatch (Ours)** | [**76.6**](https://drive.google.com/file/d/1qmCBLC9aj57kz1_OptvK6YTo4GwxTsiK/view?usp=sharing) | [**77.9**](https://drive.google.com/file/d/14LrPkWC8QIMO44da5pGflyOrW_Fdxo0U/view?usp=sharing) | [**79.2**](https://drive.google.com/file/d/1cL-p2_FIwEe9Y4AapSjlLmt4hdAZzX7a/view?usp=sharing) | [**79.5**](https://drive.google.com/file/d/1ve2BAYoh8wzQxhKD-CE7bsjcR5KqQEa3/view?usp=sharing) | ### COCO Results are obtained by DeepLabv3+ based on Xception-65. *You can click on the numbers to be directed to corresponding checkpoints.* | Method | 1/512 (232) | 1/256 (463) | 1/128 (925) | 1/64 (1849) | 1/32 (3697) | | :-------------------------: | :---------: | :---------: | :---------: | :---------: | :---------: | | SupBaseline | 22.9 | 28.0 | 33.6 | 37.8 | 42.2 | | PseudoSeg | 29.8 | 37.1 | 39.1 | 41.8 | 43.6 | | PC²Seg | 29.9 | 37.5 | 40.1 | 43.7 | 46.1 | | **UniMatch (Ours)** | [**31.9**](https://drive.google.com/file/d/1kFgg0SGLzS7SJI8sYPQKGLnw8G060kjz/view?usp=sharing) | [**38.9**](https://drive.google.com/file/d/1scx1FanOcmaut8eVESLaSx7-DiT5JJA6/view?usp=sharing) | [**44.4**](https://drive.google.com/file/d/1oojVn12tgPW_m94tAOU5YYVZ7xJJitCj/view?usp=sharing) | [**48.2**](https://drive.google.com/file/d/1tI1AZ8rY6hYQrs216iz2NmlAfLl8f1uP/view?usp=sharing) | [**49.8**](https://drive.google.com/file/d/1hwRr0IIhdeKH2JYO--iOLl5y69sJ0UYm/view?usp=sharing) | ### More Scenarios We also apply our UniMatch in the scenarios of semi-supervised **remote sensing change detection** and **medical image segmentation**, achieving tremendous improvements over previous methods: - [Remote Sensing Change Detection](https://github.com/LiheYoung/UniMatch/blob/main/more-scenarios/remote-sensing) [[training logs]](https://github.com/LiheYoung/UniMatch/blob/main/more-scenarios/remote-sensing/training-logs) - [Medical Image Segmentation](https://github.com/LiheYoung/UniMatch/blob/main/more-scenarios/medical) [[training logs]](https://github.com/LiheYoung/UniMatch/blob/main/more-scenarios/medical/training-logs) ## Getting Started ### Installation ```bash cd UniMatch conda create -n unimatch python=3.10.4 conda activate unimatch pip install -r requirements.txt pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html ``` ### Pretrained Backbone [ResNet-50](https://drive.google.com/file/d/1mqUrqFvTQ0k5QEotk4oiOFyP6B9dVZXS/view?usp=sharing) | [ResNet-101](https://drive.google.com/file/d/1Rx0legsMolCWENpfvE2jUScT3ogalMO8/view?usp=sharing) | [Xception-65](https://drive.google.com/open?id=1_j_mE07tiV24xXOJw4XDze0-a0NAhNVi) ``` ├── ./pretrained ├── resnet50.pth ├── resnet101.pth └── xception.pth ``` ### Dataset - Pascal: [JPEGImages](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) | [SegmentationClass](https://drive.google.com/file/d/1ikrDlsai5QSf2GiSUR3f8PZUzyTubcuF/view?usp=sharing) - Cityscapes: [leftImg8bit](https://www.cityscapes-dataset.com/file-handling/?packageID=3) | [gtFine](https://drive.google.com/file/d/1E_27g9tuHm6baBqcA7jct_jqcGA89QPm/view?usp=sharing) - COCO: [train2017](http://images.cocodataset.org/zips/train2017.zip) | [val2017](http://images.cocodataset.org/zips/val2017.zip) | [masks](https://drive.google.com/file/d/166xLerzEEIbU7Mt1UGut-3-VN41FMUb1/view?usp=sharing) Please modify your dataset path in configuration files. **The groundtruth masks have already been pre-processed by us. You can use them directly.** ``` ├── [Your Pascal Path] ├── JPEGImages └── SegmentationClass ├── [Your Cityscapes Path] ├── leftImg8bit └── gtFine ├── [Your COCO Path] ├── train2017 ├── val2017 └── masks ``` ## Usage ### UniMatch ```bash # use torch.distributed.launch sh scripts/train.sh # to fully reproduce our results, the should be set as 4 on all three datasets # otherwise, you need to adjust the learning rate accordingly # or use slurm # sh scripts/slurm_train.sh ``` To train on other datasets or splits, please modify ``dataset`` and ``split`` in [train.sh](https://github.com/LiheYoung/UniMatch/blob/main/scripts/train.sh). ### FixMatch Modify the ``method`` from ``'unimatch'`` to ``'fixmatch'`` in [train.sh](https://github.com/LiheYoung/UniMatch/blob/main/scripts/train.sh). ### Supervised Baseline Modify the ``method`` from ``'unimatch'`` to ``'supervised'`` in [train.sh](https://github.com/LiheYoung/UniMatch/blob/main/scripts/train.sh), and double the ``batch_size`` in configuration file if you use the same number of GPUs as semi-supervised setting (no need to change ``lr``). ## Citation If you find this project useful, please consider citing: ```bibtex @inproceedings{unimatch, title={Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation}, author={Yang, Lihe and Qi, Lei and Feng, Litong and Zhang, Wayne and Shi, Yinghuan}, booktitle={CVPR}, year={2023} } ``` We have some other works on semi-supervised semantic segmentation: - [[CVPR 2022] ST++](https://github.com/LiheYoung/ST-PlusPlus) - [[CVPR 2023] AugSeg](https://github.com/ZhenZHAO/AugSeg) - [[CVPR 2023] iMAS](https://github.com/ZhenZHAO/iMAS)