accyclegan's Introduction

ACCycleGAN

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.

Prerequisites

Linux

Python 3

CPU or NVIDIA GPU + CUDA CuDNN

Datasets

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

Running

For train

python colorization.py

For test

python test.py

Reference

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}
}

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