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.