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https://github.com/yyliu01/ps-mt

[CVPR'22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
https://github.com/yyliu01/ps-mt

cvpr2022 semantic-segmentation semi-supervised-learning semi-supervised-segmentation

Last synced: 2 months ago
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[CVPR'22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

Host: GitHub
URL: https://github.com/yyliu01/ps-mt
Owner: yyliu01
License: mit
Created: 2022-03-25T02:16:11.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-06-27T01:45:36.000Z (over 1 year ago)
Last Synced: 2024-06-15T00:48:10.707Z (4 months ago)
Topics: cvpr2022, semantic-segmentation, semi-supervised-learning, semi-supervised-segmentation
Language: Python
Homepage: https://arxiv.org/pdf/2111.12903.pdf
Size: 1.08 MB
Stars: 180
Watchers: 5
Forks: 16
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE.md

Awesome Lists containing this project

Awesome-Semi-Supervised-Semantic-Segmentation - Code - mt)|[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Perturbed_and_Strict_Mean_Teachers_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf)| (2022)

README

        # PS-MT 

> **[CVPR'22]** [Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation](https://arxiv.org/abs/2111.12903)

>

> by Yuyuan Liu, [Yu Tian](https://yutianyt.com/), Yuanhong Chen, [Fengbei Liu](https://fbladl.github.io/), [Vasileios Belagiannis](https://campar.in.tum.de/Main/VasileiosBelagiannis) and [Gustavo Carneiro](https://cs.adelaide.edu.au/~carneiro/)

> 

> Computer Vision and Pattern Recognition Conference (CVPR), 2022

![image](https://user-images.githubusercontent.com/102338056/167279043-362e1405-db45-4355-b92b-0993312fe461.png)

### Installation

Please install the dependencies and dataset based on this [***installation***](./docs/installation.md) document.

### Getting start

Please follow this [***instruction***](./docs/before_start.md) document to reproduce our results.

## Update

* blender setting results in VOC12 dataset (under deeplabv3+ with resnet101)

  

  | Approach  | 1/16 (662)| 1/8 (1323)| 1/4 (2646)| 1/2 (5291)|

  |:--------:	|:-----:	|:-----:	|:-----:	|:-----:	|

  | PS-MT  [(wandb_log)](https://wandb.ai/pyedog1976/blender-exps?workspace=user-pyedog1976)    | 78.79     | 80.29     | 80.66     | 80.87        |

  

  * please note that, we update the blender splits list end with an extra 0 (e.g., 6620 for 662 labels) in [the original directory](https://github.com/yyliu01/PS-MT/tree/main/VocCode/DataLoader/voc_splits). 

  * you can find the related launching scripts in [here](https://github.com/yyliu01/PS-MT/blob/main/scripts/train_voc_blender.sh). 

  * **In case you are using blender experiments (which are built on top of the high-quality labels), please compare with the results in this table**.

  

## Results

### Pascal VOC12 dataset

1. augmented set 

    | Backbone 	| 1/16 (662)| 1/8 (1323)| 1/4 (2646)| 1/2 (5291)|

    |:--------:	|:-----:	|:-----:	|:-----:	|:-----:	|

    | 50       	| 72.83 	| 75.70 	| 76.43 	| 77.88 	|

    | 101      	| 75.50 	| 78.20 	| 78.72 	| 79.76 	|

2. high quality set (based on res101)

   | 1/16 (92)| 1/8 (183)| 1/4 (366)| 1/2 (732)| full (1464)|

   |:-----:	|:-----:	|:-----:	|:-----:	|:-----:	|

   | 65.80 	| 69.58 	| 76.57 	| 78.42 	|80.01|

### CityScape dataset

1. following the setting of [CAC](https://arxiv.org/pdf/2106.14133.pdf) (720x720, CE supervised loss)

   

    | Backbone 	| slid. eval| 1/8 (372)| 1/4 (744)| 1/2 (1488)|

    |:--------:	|:-----:	|:-----:	|:-----:	|:-----:	|

    | 50       	| ✗	        |74.37 	    | 75.15 	| 76.02 	| 

    | 50       	| ✓	        |75.76 	    | 76.92 	| 77.64 	| 

    | 101      	| ✓	        |76.89	    | 77.60 	| 79.09 	|       

2. following the setting of [CPS](https://arxiv.org/pdf/2106.01226.pdf) (800x800, OHEM supervised loss)

   

   | Backbone 	| slid. eval| 1/8 (372)| 1/4 (744)| 1/2 (1488)|

   |:--------:	|:-----:	|:-----:	|:-----:	|:-----:	|

   | 50       	| ✓		    |77.12 	    | 78.38 	| 79.22 	|

## Training details

Some examples of training details, including:

1) VOC12 dataset in this [wandb](https://wandb.ai/pyedog1976/PS-MT(VOC12)?workspace=user-pyedog1976) link.

2) Cityscapes dataset in this [wandb](https://wandb.ai/pyedog1976/PS-MT(City)?workspace=user-pyedog1976) link **(w/ 1-teacher inference)**.

In details, after clicking the run (e.g., [1323_voc_rand1](https://wandb.ai/pyedog1976/PS-MT(VOC12)/runs/177s76t6?workspace=user-pyedog1976)), you can checkout:

1)  overall information (e.g., training command line, hardware information and training time).

2)  training details (e.g., loss curves, validation results and visualization)

3)  output logs (well, sometimes might crash ...)

## Acknowledgement & Citation

The code is highly based on the [CCT](https://github.com/yassouali/CCT). Many thanks for their great work.

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

```bibtex

@article{liu2021perturbed,

  title={Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation},

  author={Liu, Yuyuan and Tian, Yu and Chen, Yuanhong and Liu, Fengbei and Belagiannis, Vasileios and Carneiro, Gustavo},

  journal={arXiv preprint arXiv:2111.12903},

  year={2021}

}

```

#### TODO

- [x] Code of deeplabv3+ for voc12

- [x] Code of deeplabv3+ for cityscapes