SSC-GAN_repo
Pytorch implementation for 'Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation'.PDF
SSC-GAN:Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation
Authors : Tianyi Chen, Yi Liu, Yunfei Zhang, Si Wu, Yong Xu, Feng Liangbing and Hau San Wong
Requirements
- Linux or Windows
- Python 3.6+
- Pytorch 1.2.0+
Getting started
Clone the repository
git clone https://github.com/bravotty/SSC-GAN_repo
cd SSC-GAN_repo
Setting up the data
Note: You need to download the data if you wish to train your own model.
Download the formatted CUB data from this link[BaiDuYunDisk] and its extracted code: xbq4 and extract it inside the data
directory
cd data
unzip birds.zip
cd ..
Downloading pretrained models
Pretrained generator models for CUB are available at this link[BaiDuYunDisk] and its extracted code:4ko5. Download and extract them in the models_pth
directory.
Evaluating the model
In cfg/eval.yml
:
- Specify the model path in
TRAIN.NET_G
. - Specify the output directory to save the generated images in
SAVE_DIR
. - Run
python main.py --cfg cfg/eval.yml
Training your own model
In cfg/train.yml
:
- Specify the dataset location in
DATA_DIR
. - Specify the number of fine-grained categories that you wish for SReGAN, in
CLASSES
. - Specify the training hyperparameters in
TRAIN
. - Run
python main.py --cfg cfg/train.yml
Acknowledgement
We thank the authors of FineGAN: Unsupervised Hierarchical Disentanglement for Fine-grained Object Generation and Discovery for releasing their source code.