BigGAN-PyTorch
Pytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)
train imagenet
for 128*128*3 resolution
python main.py --batch_size 64 --dataset imagenet --adv_loss hinge --version biggan_imagenet --image_path /data/datasets
python main.py --batch_size 64 --dataset lsun --adv_loss hinge --version biggan_lsun --image_path /data1/datasets/lsun/lsun
python main.py --batch_size 64 --dataset lsun --adv_loss hinge --version biggan_lsun --parallel True --gpus 0,1,2,3 --use_tensorboard True
Different
Compatability
Pretrained Models
LSUN Pretrained modelDownload
Some methods in the paper to avoid model collapse, please see the paper and retrain your model.
Performance
Infact, as mentioned in the paper, the model will collapse
I use LSUN datasets to train this model maybe cause bad performance due to the class of classroom is more complex than �ImageNet
Results
LSUN DATASETS(two classes): classroom and church_outdoor