Nitay

I run !python train.py --outdir=./results --cbase=16384 --snap=10 --img-snap=10 --cfg=stylegan2 --data=./datasets/FH.zip --augpipe=bgc --gpus=2 --metrics=None --gamma=12 --batch=16 --resume='https://api.ngc.nvidia.com/v2/models/org/nvidia/team/research/stylegan2/1/files?redirect=true&path=stylegan2-ffhq-256x256.pkl' and got the same issue

I changed "z_dim" and "w_dim" to 256, thinking it might help but it didn't. However, I believe the problem was with the dataset I used where for some reason some...

Actually, it seems the reason it was fixed was thanks to adding --cbase=16384

I applied the fixes I mentioned here: #177 These are my library versions: torch : 2.6.0+cu124 torchvision : 0.21.0+cu124 pyvene : 0.1.8 transformers : 4.52.4 protobuf : 3.20.3 matplotlib :...

I tried running train.py in examples with the same arguments and got more stable scores of {'test_accuracy': 0.8644264194669756, 'test_f1': 0.8997429305912596, 'test_combined_score': 0.8820846750291176} {'test_accuracy': 0.8586326767091541, 'test_f1': 0.8993399339933994, 'test_combined_score': 0.8789863053512768} {'test_accuracy': 0.8679026651216686,...