This is a pytorch implementation of 3 different GAN models on LSUN-Bridge dataset.
- DCGAN
- WGAN
- WGAN with Gradient Penalty
This implementation is for LSUN Bridge dataset. Clone the repo: https://github.com/fyu/lsun.git
Download the whole latest data set
python3 download.py
Download the whole latest data set to <data_dir>
python3 download.py -o <data_dir>
Download data for bridge
python3 download.py -c bridge
Download testing set
python3 download.py -c test
Detailed instructions: https://github.com/fyu/lsun
python main.py --dataroot PATH TO DATASET --outf PATH FOR SAVING TRAINED MODEL --outimg PATH FOR SAVING IMAGES --niter EPOCHS --model PICK ONE: 'WGAN','WGAN_GP','DCGAN'
Use this to resume training:
python main.py --dataroot PATH TO DATASET --outf PATH FOR SAVING TRAINED MODEL --outimg PATH FOR SAVING IMAGES --niter EPOCHS --model PICK ONE: 'WGAN','WGAN_GP','DCGAN' --netG Path to trained generator --netD Path to trained discriminator
This repository contains 5 trained models with following config:
- WGAN - 25 epochs , 40 epochs
- WGAN_GP - 25 epochs
- DCGAN - 25 epochs , 40 epochs
Models | Images | Epochs | FID |
---|---|---|---|
WGAN | 500 | 25 | 105.426 |
WGAN | 500 | 40 | 101.813 |
WGAN with Gradient Penalty | 500 | 25 | 97.768 |
DCGAN | 500 | 25 | 95.433 |
DCGAN | 500 | 40 | 85.663 |