Pytorch implementation for our paper Domain-Specific Mappings for Generative Adversarial Style Transfer.
Domain-Specific Mappings for Generative Adversarial Style Transfer
Hsin-Yu Chang, Zhixiang Wang, and Yung-Yu Chuang
European Conference on Computer Vision (ECCV), 2020
[arxiv]
If you find the work is useful in your research, please consider citing:
@inproceedings{chang2020dsmap,
author = {Chang, Hsin-Yu and Wang, Zhixiang and Chuang, Yung-Yu},
title = {Domain-Specific Mappings for Generative Adversarial Style Transfers},
booktitle = {European Conference on Computer Vision},
year = {2020}
- Python 3.6 or higher
- Pytorch 1.2.0 or higher, torchvision 0.4.0 or higher
- Tensorboard, TensorboardX, Pyyaml, pillow
Download dataset from the following github repo.
CUDA_VISIBLE_DEVICES=[gpu] python3 train.py --config [config_path] --save_name [path_to_save]
- Download model here!
CUDA_VISIBLE_DEVICES=[gpu] python3 test.py --config [config_path] --checkpoint [checkpoint_path] --test_path [test_folder_path] --output_path [path_to_output_images] --a2b [1 or 0]
Copyright (C) 2020 Hsin-Yu Chang. Licensed under the CC BY-NC-SA 4.0 license.
Code inspired from: