Self-Regulation for Semantic Segmentation

This is the PyTorch implementation for paper Self-Regulation for Semantic Segmentation, ICCV 2021.

Contact

This project is a demo implementation for SR.
If you have any questions, don't hesitate to contact: dongzhang@njust.edu.cn

Installation Instructions

Clone this repo:

git clone git@github.com:dongzhang89/SR-SS.git
cd SR-SS

Install PyTorch 0.4.0:
```
pip install pytorch==0.4.0
```
GPU is not necessary, but it will be very slow without GPU.
Install Python Package
```
pip install -r requirements
```
Tensorflow is necessary for tensorboardX. If you don't want to use tensorboardX for visualization, delete it.
Dataset preparation We use the PASCAL VOC 2012 dataset in SR. Go to the official webpage and download the training images. We use the augmented pseudo-labels in experiments. The final file structure is shown below：
```
data
|- VOC2012
|- SegmentationClassAug_pseudo_label #label path
|- JPEGImages #image path
```

Usage

Demo

Test on a single image could be done by running:

python demo.py --img_path path --model path/weight

You can get a similar image as:

input

Result image could be saved as result.jpg.

To be noticed, the trained model can be loaded by:

checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['model'])

Train

Training your own model could be done by running:

python trainval.py

Change the command line arguments if necessary.
To speed up the training process, we use the loss for the presences of object categories instead of classification, but the performance of these two methods is quite similar.
Please first train the model by 10000 iterations using only MEA loss and switch on the SR loss for the remaining iterations.

Test

Testing your trained model could be done by running:

python test.py --model=path/model

Then you can see the final result on PASCAL VOC2012 after a while.
We believe designing more complicated training weight for loss items also benefits for the model performance.

TensorboardX

Monitor your training process with tensorboardX. Run:

tensorboard --logdir=$DEEPLAB_V2_PYTORCH/logs/loss_lr/lr --port=7001

Then open your firefox or chrome, and visit localhost:7001.

Acknowledgement

This project heavily relies on the following projects:

Citing SR-SS

You may want to cite:

@article{zhang2021sr,
  title={Self-Regulation for Semantic Segmentation},
  author={Dong, Zhang and Hanwang, Zhang and Jinhui, Tang and Xiansheng, Hua and Qianru, Sun},
  journal={International Conference on Computer Vision (ICCV)},
  year={2021}
}

dongzhang89 / SR-SS

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