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License CC BY-NC-SA 4.0 Python 3.6 PyTorch version support

Domain-Specific Mappings for Generative Adversarial Style Transfer

[Project Page][Paper]

Pytorch implementation for our paper Domain-Specific Mappings for Generative Adversarial Style Transfer.

Example Results

Paper

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]

Citation

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}

Usage

Requirements

  1. Python 3.6 or higher
  2. Pytorch 1.2.0 or higher, torchvision 0.4.0 or higher
  3. Tensorboard, TensorboardX, Pyyaml, pillow

Dataset

Download dataset from the following github repo.

Train

CUDA_VISIBLE_DEVICES=[gpu] python3 train.py --config [config_path] --save_name [path_to_save]

Test

  1. 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]

LICENSE

Copyright (C) 2020 Hsin-Yu Chang. Licensed under the CC BY-NC-SA 4.0 license.

Reference

Code inspired from:

  1. https://github.com/NVlabs/MUNIT
  2. https://github.com/HelenMao/MSGAN