argan
[Open Source]. ARGAN - The improved version of AnimeGAN. Landscape photos/videos to anime
Science Score: 39.0%
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○CITATION.cff file
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✓.zenodo.json file
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○Scientific vocabulary similarity
Low similarity (11.6%) to scientific vocabulary
Keywords
Repository
[Open Source]. ARGAN - The improved version of AnimeGAN. Landscape photos/videos to anime
Basic Info
Statistics
- Stars: 13
- Watchers: 2
- Forks: 3
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
ARGANv1
[Open Sourc]. The improved version of AnimeGANv2.
AnimeGANv2 | Landscape
photos/videos to anime
Focus:
| Anime style | Film | Picture Number | Quality | Download Style Dataset |
|---|---|---|---|---|
| Miyazaki Hayao | The Wind Rises | 1752 | 1080p | Link |
| Makoto Shinkai | Your Name & Weathering with you | 1445 | BD | |
| Kon Satoshi | Paprika | 1284 | BDRip |
Results
Requirements
You Can Use requirements file to install all packages that you need.
Usage
1. Download Pretrained Model
2. Download Train/Val Photo dataset
3. Do edge_smooth
bash
python edge_smooth.py --dataset Hayao --img_size 256
4. Calculate the three-channel(BGR) color difference
bash
python data_mean.py --dataset Hayao
5. Train
bash
python main.py --phase train --dataset Hayao --data_mean [13.1360,-8.6698,-4.4661] --epoch 101 --init_epoch 10
For light version:
bash
python main.py --phase train --dataset Hayao --data_mean [13.1360,-8.6698,-4.4661] --light --epoch 101 --init_epoch 10
6. Extract the weights of the generator
bash
python get_generator_ckpt.py --checkpoint_dir ../checkpoint/ARGANv1_Hayao_lsgan_300_300_1_2_10_1 --style_name Hayao
7. Inference
bash
python test.py --checkpoint_dir checkpoint/generator_Hayao_weight --test_dir dataset/test/HR_photo --style_name Hayao/HR_photo
8. Convert video to anime
bash
python video2anime.py --video input.mp4 --checkpoint_dir checkpoint/generator_Paprika_weight
License
This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, and scientific publications. Permission is granted to use the ARGANv1 given that you agree to my license terms. Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter.
Citation
If you find our work useful in your research or publications, please consider citing the main paper:
bash
@INPROCEEDINGS{9738752,
author={Zenoozi, Amirhossein Douzandeh and Navi, Keivan and Majidi, Babak},
booktitle={2022 International Conference on Machine Vision and Image Processing (MVIP)},
title={ARGAN: Fast Converging GAN for Animation Style Transfer},
year={2022},
volume={},
number={},
pages={1-5},
doi={10.1109/MVIP53647.2022.9738752}
}
And This Repository:
bash
@software{Douzandeh_Zenoozi_ARGAN_GitHub_Repository_2023,
author = {Douzandeh Zenoozi, Amirhossein},
doi = {10.5281/zenodo.8075534},
month = jun,
title = {{ARGAN GitHub Repository}},
url = {https://github.com/amirzenoozi/ARGAN},
version = {1.0.0},
year = {2023}
}
Author
Amirhossein Douzandeh Zenoozi
Owner
- Name: Amirhossein Douzandeh Zenoozi
- Login: amirzenoozi
- Kind: user
- Location: Bolzano, Italy
- Website: https://amirdouzandeh.me/
- Repositories: 56
- Profile: https://github.com/amirzenoozi
🐍 Python Lover 🧠 AI Student 💻 Front-End Engineer
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