CartoonGAN - my attempt to implement it
Within this repo, I try to implement a cartoon GAN [Chen et al., CVPR18].
I created a github page for detailed documentation, please see https://tobiassunderdiek.github.io/cartoon-gan/ for details.
Usage
Step 1: Generate datasets
All scripts to create the images are resumeable. It is possible to run make cartoons
and make photos
in parallel by calling them manually in separate terminals.
Cartoon images
- download
all_data.csv
from safebooru dataset [2] - point to
all_data.csv
inPATH_TO_SAFEBOORU_ALL_DATA_CSV
ofcartoon_image_downloader.py
- run
make install
to install necessary libraries - run
make cartoons
to download configurable amount of medium size images
Edge-smoothed version of cartoon images
- run
make cartoons-smooth
to create the images
Photos
- download and unzip coco annotations from [3]
- configure annotations dir location in
PATH_TO_COCO_ANNOTATIONS_ROOT_FOLDER
ofphoto_downloader.py
- run
make photos
to download configurable amount of photos of persons
Step 2: Train model
All the steps are described in a jupyter notebook on colab, please see here for details.
Step 3: Test
- run
make install-transform
- download pre-trained weights, they are available for download as part of the release here..
- run
make transform IMAGE=some_example_image_path
Additional information about how to load the pre-trained weights and transform images can be found in the project documentation here: https://tobiassunderdiek.github.io/cartoon-gan/ .
Credits
Thanks to the authors [Chen et al., CVPR18] of the paper for their great work.
References
[Chen et al., CVPR18] http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf
[2] https://www.kaggle.com/alamson/safebooru/download
[3] http://images.cocodataset.org/annotations/annotations_trainval2017.zip