https://github.com/znxlwm/pytorch-cartoongan
Pytorch implementation of CartoonGAN (CVPR 2018)
https://github.com/znxlwm/pytorch-cartoongan
Last synced: 7 months ago
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Pytorch implementation of CartoonGAN (CVPR 2018)
- Host: GitHub
- URL: https://github.com/znxlwm/pytorch-cartoongan
- Owner: znxlwm
- License: mit
- Created: 2018-07-24T01:57:46.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-06-17T09:44:32.000Z (over 4 years ago)
- Last Synced: 2025-03-30T11:11:14.423Z (7 months ago)
- Language: Python
- Homepage:
- Size: 3.05 MB
- Stars: 389
- Watchers: 10
- Forks: 73
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pytorch-CartoonGAN
Pytorch implementation of CartoonGAN [1] (CVPR 2018)
* Parameters without information in the paper were set arbitrarily.
* I used face-cropped celebA (src) and anime (tgt) collected from the web data because I could not find the author's data.
## Tensorflow version
[CartoonGAN-tensorflow](https://github.com/taki0112/CartoonGAN-Tensorflow)
## Usage
### 1.Download VGG19
[VGG19](https://download.pytorch.org/models/vgg19-dcbb9e9d.pth)
### 2.Train
```
python CartoonGAN.py --name your_project_name --src_data src_data_path --tgt_data tgt_data_path --vgg_model pre_trained_VGG19_model_path
```
### Folder structure
The following shows basic folder structure.
```
├── data
│ ├── src_data # src data (not included in this repo)
│ │ ├── train
│ │ └── test
│ └── tgt_data # tgt data (not included in this repo)
│ ├── train
│ └── pair # edge-promoting results to be saved here
│
├── CartoonGAN.py # training code
├── edge_promoting.py
├── utils.py
├── networks.py
└── name_results # results to be saved here
```
## Resutls
### paper results
### celebA2anime face
### Initialization phase (reconstruction)
Input - Result (this repo)
### Cartoonization
* I got the author's results from [CaroonGAN-Test-Pytorch-Torch](https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch).
Input - Result (this repo)
Author's pre-trained model (Hayao)
Author's pre-trained model (Hosoda)
## Development Environment
* NVIDIA GTX 1080 ti
* cuda 8.0
* python 3.5.3
* pytorch 0.4.0
* torchvision 0.2.1
* opencv 3.2.0
## Reference
[1] Chen, Yang, Yu-Kun Lai, and Yong-Jin Liu. "CartoonGAN: Generative Adversarial Networks for Photo Cartoonization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
(Full paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf)