BASeg

The code of paper "BASeg: Boundary Aware Semantic Segmentation for Autonomous Driving"

Introduction

This repository is a official PyTorch implementation for semantic segmentation.

Usage

1. Requirement:

   • Hardware: 4-8 GPUs (better with >=11G GPU memory)
   • Software: PyTorch>=1.1.0, Python3, tensorboardX

2. Clone the repository:

   git clone git@github.com:YangParky/BASeg.git

3. Data preparation

   • Download related datasets (ADE20K, Cityscapes, CamVid) and symlink the paths to them as follows (you can alternatively modify the relevant paths specified in folder config):

   • To boost the slow speed of the training, you're supposed to prepare the boundary ground truth from here.

   • The directory structure is the standard layout for the torchvision

     /Dataset/
       ADE20K/
         Scene-Parsing/
            ADEChallengeData2016/
              images/
              bound/
              annotations/
       Cityscapes/
         bound/
         gtFine/
         leftImg8bit/
       CamVid/
         bound/
         CamVid_Label/
         CamVid_RGB/
     /Model
     /Project
       /BASeg/
     

4. Train:

   • Download ImageNet pre-trained models and put them under folder model for weight initialization.
   • For full traning: ADE20K:

     sh tools/trainade.sh ade20k baseg101

     Cityscapes:

     sh tools/traincityscapes.sh cityscapes baseg101

     CamVid:

     sh tools/traincamvid.sh camvid baseg101

5. Test:

   • Download trained segmentation models and put them under folder specified in config or modify the specified paths.

   • For full testing (get listed performance): Validation on ADE20K

     sh tools/testade.sh ade20k baseg101

     Test on Cityscapes

     sh tools/testcityscapes.sh cityscapes baseg101

     Validation on CamVid

     sh tools/testcamvid.sh camvid baseg101

   • For boundary evaluation: Evaluation on boundary F1_score

     python util/f_boundary.py

     Evaluation on interior F1_score

     python util/f_interior.py

Citation

If you find the code or trained models useful, please consider citing:

@article{xiao2023baseg,
  title={BASeg: Boundary aware semantic segmentation for autonomous driving},
  author={Xiao, Xiaoyang and Zhao, Yuqian and Zhang, Fan and Luo, Biao and Yu, Lingli and Chen, Baifan and Yang, Chunhua},
  journal={Neural Networks},
  volume={157},
  pages={460--470},
  year={2023},
  publisher={Elsevier}
}

Acknowledgement

The code is from the first author of semseg.

License

This repository is released under MIT License (see LICENSE file for details).