CyEDA: Cycle-object Edge Consistency Domain Adaptation

Official pytorch implementation of the paper: "CyEDA: Cycle-object Edge Consistency Domain Adaptation"

ICIP 2022 | Paper

Released 29 August 2023

Results

Architecture

Environment

1. Clone this repository

   git clone https://github.com/bjc1999/CyEDA.git
   

2. Access the repository folder

   cd CyEDA
   

3. Create virtual environment python 3.7 recommended

   python -m virtualenv env
   

4. Activate the environment

   env/Scripts/activate
   

5. Install dependencies

   pip install -r requirement.txt
   

Training

• Run train.sh script

  bash train.sh
  

• Execute train.py file within environment

  python train.py [--parameters]
  

Testing

• Execute predict.py file within environment

  python predict.py [--parameters]
  

Citation

If you find this work useful for your research, please cite

@INPROCEEDINGS{9897493,
  author = {Beh, Jing Chong and Ng, Kam Woh and Kew, Jie Long and Lin, Che-Tsung and Chan, Chee Seng and Lai, Shang-Hong and Zach, Christopher},
  booktitle = {2022 IEEE International Conference on Image Processing (ICIP)}, 
  title = {CyEDA: Cycle-Object Edge Consistency Domain Adaptation}, 
  year = {2022},
  pages = {2986-2990},
  doi = {10.1109/ICIP46576.2022.9897493}}

Feedback

Suggestions and opinions on this work (both positive and negative) are greatly welcomed. Please contact the authors by sending an email to cjbeh1999 at gmail.com or cs.chan at um.edu.my.

License and Copyright

The project is open source under BSD-3 license (see the LICENSE file).

©2023 Universiti Malaya.