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

• This project is a demo implementation for SR.
• If you have any questions, don't hesitate to contact: [email protected]

• Clone this repo:

git clone [email protected]: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

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:

• 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'])

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.

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.

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.

This project heavily relies on the following projects:

• isht7/pytorch-deeplab-resnet
• kazuto1011/deeplab-pytorch
• DrSleep/tensorflow-deeplab-resnet
• jwyang/faster-rcnn.pytorch
• zhanghang1989/PyTorch-Encoding

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}
}