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https://github.com/kinux98/SSL_ELN

This repository contains the official implementation of Semi-supervised Semantic Segmentation with Error Localization Network that has been accepted to 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022).
https://github.com/kinux98/SSL_ELN

cvpr2022 pytorch semantic-segmentation semi-supervised-learning

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This repository contains the official implementation of Semi-supervised Semantic Segmentation with Error Localization Network that has been accepted to 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022).

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README 

        
# Semi-supervised Semantic Segmentation with Error Localization Network

[Our paper](https://arxiv.org/pdf/2204.02078.pdf), [Project page](http://cvlab.postech.ac.kr/research/ELN/)



*Donghyeon Kwon and Suha Kwak*



> [email protected]



This repository contains the official implementation of : 

> Semi-supervised Semantic Segmentation with Error Localization Network



that has been accepted to 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022).





  




## Highlight

- Segmentation networks trained by our method achieves the state of the art on two benchmark datasets, PASCAL VOC 2012 and Cityscapes, in every setting.

  

- We propose error localization, a new approach to dealing with errors on pseudo labels. It is simple yet effective and can be naturally incorporated with self-training and contrastive learning. 

  

- We develop a new strategy for generating diverse and plausible prediction errors intentionally during the training of ELN. This improves the generalization of ELN even using a small number of labeled data for training.



## Requirements

The repository is tested on Ubuntu 20.04.1 LTS, Python 3.8.8, PyTorch 1.11.0 and CUDA 11.4. We use a NVIDIA GeForce RTX3090 for the training.



After preparing virtual environment, download requirementes packages with :



> pip install requirements.txt



### wandb settings



Before starting, you should login wandb using your personal API key.



>wandb login PERSONAL_API_KEY



## Dataset



### 1. Set the base directories in "SSL_ELN/dataset/common.py"

> base_voc = "./dataset/pascal_voc_seg/VOCdevkit/VOC2012"

>

> base_city = "./dataset/cityscapes/"



### 2. Download and process dataset

For PASCAL VOC 2012, we also use additional SBD augmentation dataset. 



If you don't have SBD dataset, please refer to [this blog](https://www.sun11.me/blog/2018/how-to-use-10582-trainaug-images-on-DeeplabV3-code/).



For Cityscpaes, you can download the dataset from their official [website](https://www.cityscapes-dataset.com).



Afterwards, your data directory structure should have following scheme:



    ├── ./dataset/pascal_voc_seg/VOCdevkit/VOC2012                    

                                            ├── Annotations 

                                            ├── ImageSets

                                            │   ├── Segmentation

                                            │   │   ├── 1_labeled_0.txt

                                            │   │   ├── 1_labeled_1.txt

                                            │   │   └── ... 

                                            │   └── ... 

                                            ├── JPEGImages

                                            ├── SegmentationClass

                                            ├── SegmentationClassAug

                                            └── ...

    

    ├── ./dataset/cityscapes                     

                    ├── data_lists

                    │   ├── 1_labeled_0.txt

                    │   ├── 1_labeled_1.txt

                    │   └── ...  

                    ├── gtFine

                    └── leftImage8bit



## Training

### PASCAL VOC 2012



> Using ResNet101 Backbone

> 

> python3 train.py --exp-name=voc_20_res101 --train-split=4 --dataset=voc --pre_epoch=70 --eln_epoch=50 --backbone_name=101 --batch-size-labeled=6 --batch-size-unlabeled=6



> Using ResNet50 Backbone

> 

> python3 train.py --exp-name=voc_20_res50 --train-split=4 --dataset=voc --pre_epoch=70 --eln_epoch=50 --backbone_name=50 --batch-size-labeled=6 --batch-size-unlabeled=6



### Cityscapes



> Using ResNet50 Backbone

> 

> python3 train.py --exp-name=city_2_res50 --train-split=2 --dataset=city --pre_epoch=100 --eln_epoch=58 --backbone_name=50 --batch-size-labeled=4 --batch-size-unlabeled=4



---

You may change labeled-unlabeled data ratio by modifying "--train-split=".



For VOC, you can choose split value from (20, 8, 4).



For Cityscapes, you can choose split value from (8, 4, 2).



## Visualization

you can visualize segmentation ground truth/model's result, ELN's binary mask, and its filtered results with visualize.py:



> python3 visualize.py --dataset='voc' --pretrained-ckpt=./path/to/saved/model.pth --backbone_name=101 --image-path=./path/to/input/image --gt-path=./path/to/gt



Of course, you may change arguments to proper value.



## Citation

If you find this project useful, please consider citing as follows:

```

@InProceedings{Kwon_2022_CVPR,

    author    = {Kwon, Donghyeon and Kwak, Suha},

    title     = {Semi-Supervised Semantic Segmentation With Error Localization Network},

    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

    month     = {June},

    year      = {2022},

    pages     = {9957-9967}

}

```



### Acknowledgements



We borrow some codes from 



 - https://github.com/voldemortX/DST-CBC/



- https://github.com/jfzhang95/pytorch-deeplab-xception.