This is the code (in PyTorch) for our paper “Semantic-aware Automatic Image Colorization via Unpaired Cycle-Consistent Self-supervised Network”，accepted in International Jounral of Intelligent Systems.
Linux
Python 3
CPU or NVIDIA GPU + CUDA CuDNN
The color domain data in the paper is randomly selected from the PASCAL VOC 2012, and grayscaled color domain data to gray domain data. You can build your own dataset by setting up the following directory structure:
├── data
| ├── src_data # gray
| | ├── JPEGImages
| | ├── SegmentationClass
| ├── tgt_data # color
| | ├── JPEGImages
| | ├── SegmentationClass
- For train
python colorization.py
- For test
python test.py
If you find the code useful, please cite:
@article{https://doi.org/10.1002/int.22667,
author = {Xiao, Yuxuan and Jiang, Aiwen and Liu, Changhong and Wang, Mingwen},
title = {Semantic-aware automatic image colorization via unpaired cycle-consistent self-supervised network},
journal = {International Journal of Intelligent Systems},
volume = {37},
number = {2},
pages = {1222-1238},
keywords = {CycleGAN, image colorization, image editing, unpaired training, unsupervised learning},
doi = {https://doi.org/10.1002/int.22667},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.22667},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22667},
year = {2022}
}