This architecture is based on arXiv:1802.07934, 2018 and its official code.
And this repository was implemented to perform semantic segmentation for pixiv anime illust.
GAN architectures are placed in generator.py and discriminator.py, training architecture are been updater.py or loss.py, and hyper-parameter is been options.py.
The details of this architecture exist my blog in Japanese.
This result is obtained by training with Pretrained-ResNet101-DeepLab-v3 and it is output of unannotated anime illust.
Additionaly, parameters of the upper result is almost same as default value of options.py.
I prepared pre-trained weights of Generator and Discriminator and added scripts in order to get these weights.
You can get them by executing a following command.
python get_pretrained_weight.py
Totally about 200MB, so it may take a few minutes.
If you want pre-trained model to predict, please do a next python script.
python predict.py
predict.py creates predicted images from predict_from directory to predict_to.
In addition, sources are assumed 256 x 256 white-background png.
You are able to download a sample image from safebooru.org.
python get_sample_data.py
Please create 'dataset' directory and prepare dataset. Next, you can set dataset path to option of command.
Example)
Python3 train.py --dataset_dir dataset/example --unlabel_dataset_dir dataset/unlabel_example
| details | |
|---|---|
| OS | Windows10 Home 1909 |
| CPU | AMD Ryzen 2600 |
| GPU | MSI GTX 960 4GB |
| language | Python 3.7.1 |
| framework | Chainer 7.0.0, cupy-cuda91 5.3.0 |
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