Awesome

BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation

Install

This repo requires Python 3.6+, Pytorch >= 0.4.1, and CUDA 9.0+.

git clone https://github.com/uark-cviu/BiMaL
cd BiMaL
pip install -e ADVENT

Training

cd ADVENT/advent/scripts
python train.py --cfg nvp_config/gta2cityscape_advent_nvp.yaml

Datasets

• GTA5: Please follow the instructions here to download images and semantic segmentation annotations. The GTA5 dataset directory should have this basic structure:

<root_dir>/data/GTA5/                               % GTA dataset root
<root_dir>/data/GTA5/images/                        % GTA images
<root_dir>/data/GTA5/labels/                        % Semantic segmentation labels
...

• Cityscapes: Please follow the instructions in Cityscape to download the images and validation ground-truths. The Cityscapes dataset directory should have this basic structure:

<root_dir>/data/Cityscapes/                         % Cityscapes dataset root
<root_dir>/data/Cityscapes/leftImg8bit              % Cityscapes images
<root_dir>/data/Cityscapes/leftImg8bit/val
<root_dir>/data/Cityscapes/gtFine                   % Semantic segmentation labels
<root_dir>/data/Cityscapes/gtFine/val
...

Acknowledgements

This codebase is heavily borrowed from ADVENT.

Citation

If you find this code useful for your research, please consider citing:

@inproceedings{truong2021bimal,
  title={BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation},
  author={Truong, Thanh-Dat and Duong, Chi Nhan and Le, Ngan and Phung, Son Lam and Rainwater, Chase and Luu, Khoa},
  booktitle={International Conference on Computer Vision},
  year={2021}
}