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https://github.com/LiheYoung/ST-PlusPlus
[CVPR 2022] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
https://github.com/LiheYoung/ST-PlusPlus
cvpr2022 self-training semi-supervised-learning semi-supervised-segmentation
Last synced: about 2 months ago
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[CVPR 2022] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
- Host: GitHub
- URL: https://github.com/LiheYoung/ST-PlusPlus
- Owner: LiheYoung
- License: mit
- Created: 2020-12-19T13:36:28.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2023-03-03T07:12:50.000Z (over 1 year ago)
- Last Synced: 2024-06-15T00:48:41.401Z (3 months ago)
- Topics: cvpr2022, self-training, semi-supervised-learning, semi-supervised-segmentation
- Language: Python
- Homepage: https://arxiv.org/abs/2106.05095
- Size: 3.78 MB
- Stars: 225
- Watchers: 6
- Forks: 32
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Semi-Supervised-Semantic-Segmentation - Code - PlusPlus)|[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_ST_Make_Self-Training_Work_Better_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf)| (2022)
README
# ST++
This is the official PyTorch implementation of our CVPR 2022 paper:
> [**ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation**](https://arxiv.org/abs/2106.05095)
> Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao
> *In Conference on Computer Vision and Pattern Recognition (CVPR), 2022*
We have another simple yet stronger end-to-end framework **UniMatch** accepted by CVPR 2023:
> **[Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://arxiv.org/abs/2208.09910)** [[Code](https://github.com/LiheYoung/UniMatch)]
> Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi
> *In Conference on Computer Vision and Pattern Recognition (CVPR), 2023*
## Getting Started
### Data Preparation
#### Pre-trained Model
[ResNet-50](https://download.pytorch.org/models/resnet50-0676ba61.pth) | [ResNet-101](https://download.pytorch.org/models/resnet101-63fe2227.pth) | [DeepLabv2-ResNet-101](https://drive.google.com/file/d/14be0R1544P5hBmpmtr8q5KeRAvGunc6i/view?usp=sharing)
#### Dataset
[Pascal JPEGImages](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) | [Pascal SegmentationClass](https://drive.google.com/file/d/1ikrDlsai5QSf2GiSUR3f8PZUzyTubcuF/view?usp=sharing) | [Cityscapes leftImg8bit](https://www.cityscapes-dataset.com/file-handling/?packageID=3) | [Cityscapes gtFine](https://drive.google.com/file/d/1E_27g9tuHm6baBqcA7jct_jqcGA89QPm/view?usp=sharing)
#### File Organization
```
├── ./pretrained
├── resnet50.pth
├── resnet101.pth
└── deeplabv2_resnet101_coco_pretrained.pth
├── [Your Pascal Path]
├── JPEGImages
└── SegmentationClass
├── [Your Cityscapes Path]
├── leftImg8bit
└── gtFine
```
### Training and Testing
```
export semi_setting='pascal/1_8/split_0'
CUDA_VISIBLE_DEVICES=0,1 python -W ignore main.py \
--dataset pascal --data-root [Your Pascal Path] \
--batch-size 16 --backbone resnet50 --model deeplabv3plus \
--labeled-id-path dataset/splits/$semi_setting/labeled.txt \
--unlabeled-id-path dataset/splits/$semi_setting/unlabeled.txt \
--pseudo-mask-path outdir/pseudo_masks/$semi_setting \
--save-path outdir/models/$semi_setting
```
This script is for our ST framework. To run ST++, add ```--plus --reliable-id-path outdir/reliable_ids/$semi_setting```.
## Acknowledgement
The DeepLabv2 MS COCO pre-trained model is borrowed and converted from **AdvSemiSeg**.
The image partitions are borrowed from **Context-Aware-Consistency** and **PseudoSeg**.
Part of the training hyper-parameters and network structures are adapted from **PyTorch-Encoding**. The strong data augmentations are borrowed from **MoCo v2** and **PseudoSeg**.
+ AdvSemiSeg: [https://github.com/hfslyc/AdvSemiSeg](https://github.com/hfslyc/AdvSemiSeg).
+ Context-Aware-Consistency: [https://github.com/dvlab-research/Context-Aware-Consistency](https://github.com/dvlab-research/Context-Aware-Consistency).
+ PseudoSeg: [https://github.com/googleinterns/wss](https://github.com/googleinterns/wss).
+ PyTorch-Encoding: [https://github.com/zhanghang1989/PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding).
+ MoCo: [https://github.com/facebookresearch/moco](https://github.com/facebookresearch/moco).
+ OpenSelfSup: [https://github.com/open-mmlab/OpenSelfSup](https://github.com/open-mmlab/OpenSelfSup).
Thanks a lot for their great works!
## Citation
If you find this project useful, please consider citing:
```bibtex
@inproceedings{st++,
title={ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation},
author={Yang, Lihe and Zhuo, Wei and Qi, Lei and Shi, Yinghuan and Gao, Yang},
booktitle={CVPR},
year={2022}
}
@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}
}
```