Skip to content

Latest commit

 

History

History
executable file
·
56 lines (31 loc) · 1.43 KB

README.md

File metadata and controls

executable file
·
56 lines (31 loc) · 1.43 KB
Raw

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

  • not use cross-replica BatchNorm (Ioffe & Szegedy, 2015) in G

Compatability

  • CPU
  • GPU

Pretrained Models

LSUN Pretrained model Download

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

  • iter 82200 (128x128) batch_size 64
  • iter 128200
  • iter 365000
  • iter 800000
  • iter 900000