Using Unreliable Pseudo Labels

Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022.

Please refer to our project page for qualitative results.

Abstract. The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability. We argue that every pixel matters to the model training, even its prediction is ambiguous. Intuitively, an unreliable prediction may get confused among the top classes (i.e., those with the highest probabilities), however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative sample to those most unlikely categories. Based on this insight, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative samples, and manage to train the model with all candidate pixels. Considering the training evolution, where the prediction becomes more and more accurate, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.

Results

PASCAL VOC 2012

Labeled images are selected from the train set of original VOC, 1,464 images in total. And the remaining 9,118 images are all considered as unlabeled ones.

For instance, 1/2 (732) represents 732 labeled images and remaining 9,850 (9,118 + 732) are unlabeled.

Method	1/16 (92)	1/8 (183)	1/4 (366)	1/2 (732)	Full (1464)
SupOnly	45.77	54.92	65.88	71.69	72.50
U2PL (w/ CutMix)	67.98	69.15	73.66	76.16	79.49

Labeled images are selected from the train set of augmented VOC, 10,582 images in total.

Following results are all trained under our own splits. Training a model on different splits is recommended to measure the performance of a method. You can train our U²PL on splits provided by CPS or ST++.

Method	1/16 (662)	1/8 (1323)	1/4 (2646)	1/2 (5291)
SupOnly	67.87	71.55	75.80	77.13
U2PL (w/ CutMix)	77.21	79.01	79.30	80.50

Cityscapes

Labeled images are selected from the train set, 2,975 images in total.

Method	1/16 (186)	1/8 (372)	1/4 (744)	1/2 (1488)
SupOnly	65.74	72.53	74.43	77.83
U2PL (w/ CutMix)	70.30	74.37	76.47	79.05
U2PL (w/ AEL)	74.90	76.48	78.51	79.12

Checkpoints

• Models on PASCAL VOC 2012 (ResNet101-DeepLabv3+) can be found here.

• Models on Cityscapes with AEL (ResNet101-DeepLabv3+)

1/16 (186)	1/8 (372)	1/4 (744)	1/2 (1488)
Google Drive	Google Drive	Google Drive	Google Drive
Baidu Drive Fetch Code: rrpd	Baidu Drive Fetch Code: welw	Baidu Drive Fetch Code: qwcd	Baidu Drive Fetch Code: 4p8r

Installation

git clone https://github.com/Haochen-Wang409/U2PL.git && cd U2PL
conda create -n u2pl python=3.6.9
conda activate u2pl
pip install -r requirements.txt
pip install pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html

Usage

U²PL is evaluated on both Cityscapes and PASCAL VOC 2012 dataset.

Prepare Data

For Cityscapes

Download "leftImg8bit_trainvaltest.zip" from: https://www.cityscapes-dataset.com/downloads/

Download "gtFine.zip" from: https://drive.google.com/file/d/10tdElaTscdhojER_Lf7XlytiyAkk7Wlg/view?usp=sharing

Next, unzip the files to folder data and make the dictionary structures as follows:

data/cityscapes
├── gtFine
│   ├── test
│   ├── train
│   └── val
└── leftImg8bit
    ├── test
    ├── train
    └── val

For PASCAL VOC 2012

Refer to this link and download PASCAL VOC 2012 augmented with SBD dataset.

And unzip the files to folder data and make the dictionary structures as follows:

data/VOC2012
├── Annotations
├── ImageSets
├── JPEGImages
├── SegmentationClass
├── SegmentationClassAug
└── SegmentationObject

Finally, the structure of dictionary data should be as follows:

data
├── cityscapes
│   ├── gtFine
│   └── leftImg8bit
├── splits
│   ├── cityscapes
│   └── pascal
└── VOC2012
    ├── Annotations
    ├── ImageSets
    ├── JPEGImages
    ├── SegmentationClass
    ├── SegmentationClassAug
    └── SegmentationObject

Prepare Pretrained Backbone

Before training, please download ResNet101 pretrained on ImageNet-1K from one of the following:

• Google Drive
• Baidu Drive Fetch Code: 3p9h

After that, modify model_urls in semseg/models/resnet.py to </path/to/resnet101.pth>

Train a Fully-Supervised Model

For instance, we can train a model on PASCAL VOC 2012 with only 1464 labeled data for supervision by:

cd experiments/pascal/1464/suponly
# use torch.distributed.launch
sh train.sh <num_gpu> <port>

# or use slurm
# sh slurm_train.sh <num_gpu> <port> <partition>

Or for Cityscapes, a model supervised by only 744 labeled data can be trained by:

cd experiments/cityscapes/744/suponly
# use torch.distributed.launch
sh train.sh <num_gpu> <port>

# or use slurm
# sh slurm_train.sh <num_gpu> <port> <partition>

After training, the model should be evaluated by

sh eval.sh

Train a Semi-Supervised Model

We can train a model on PASCAL VOC 2012 with 1464 labeled data and 9118 unlabeled data for supervision by:

cd experiments/pascal/1464/ours
# use torch.distributed.launch
sh train.sh <num_gpu> <port>

# or use slurm
# sh slurm_train.sh <num_gpu> <port> <partition>

Or for Cityscapes, a model supervised by 744 labeled data and 2231 unlabeled data can be trained by:

cd experiments/cityscapes/744/ours
# use torch.distributed.launch
sh train.sh <num_gpu> <port>

# or use slurm
# sh slurm_train.sh <num_gpu> <port> <partition>

After training, the model should be evaluated by

sh eval.sh

Train a Semi-Supervised Model on Cityscapes with AEL

First, you should switch the branch:

git checkout with_AEL

Then, we can train a model supervised by 744 labeled data and 2231 unlabeled data by:

cd experiments/city_744
# use torch.distributed.launch
sh train.sh <num_gpu> <port>

# or use slurm
# sh slurm_train.sh <num_gpu> <port> <partition>

After training, the model should be evaluated by

sh eval.sh

Note

<num_gpu> means the number of GPUs for training.

To reproduce our results, we recommend you follow the settings:

• Cityscapes: 4 * V100 (32G) for SupOnly and 8 * V100 (32G) for Semi-Supervised
• PASCAL VOC 2012: 2 * V100 (32G) for SupOnly and 4 * V100 (32G) for Semi-Supervised

If you got CUDA Out of Memory error, please try training our method in fp16 mode. Or, change the lr in config.yaml in a linear manner (e.g., if you want to train a SupOnly model on Cityscapes with 8 GPUs, you are recommended to change the lr to 0.02).

If you want to train a model on other split, you need to modify data_list and n_sup in config.yaml.

Due to the randomness of function torch.nn.functional.interpolate when mode="bilinear", the results of semantic segmentation will not be the same EVEN IF a fixed random seed is set.

Therefore, we recommend you run 3 times and get the average performance.

License

This project is released under the Apache 2.0 license.

Acknowledgement

The contrastive learning loss and strong data augmentation (CutMix, CutOut, and ClassMix) are borrowed from ReCo. We reproduce our U²PL based on AEL on branch with_AEL.

• ReCo: https://github.com/lorenmt/reco
• AEL: https://github.com/hzhupku/SemiSeg-AEL

Thanks a lot for their great work!

Citation

@inproceedings{wang2022semi,
    title={Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels},
    author={Wang, Yuchao and Wang, Haochen and Shen, Yujun and Fei, Jingjing and Li, Wei and Jin, Guoqiang and Wu, Liwei and Zhao, Rui and Le, Xinyi},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2022}
}

Contact

• Yuchao Wang, [email protected]
• Haochen Wang, [email protected]
• Jingjing Fei, [email protected]
• Wei Li, [email protected]