Tuning parameters for logo dataset

Hi! First off, thank you for being so engaged and active with folks’ issues.

I’m training on a dataset of 3k Japanese flag logos, plus their rotations of 90, 180, and 270 degrees for 12k total images.

I generally experience very quick mode collapse, often within the first 5k iterations. The one result that it does produce is pretty nice, though! unet_stylegan2 --data jpegs/ --attn-layers [1,2] 39k iterations:

Increasing the batch size and gradient accumulate every has reduced it to partial mode collapse. The catch is I’m training on colab, and this can be unworkably slow depending on the GPU (30 secs per iteration) unet_stylegan2 --data jpegs/ --batch-size 32 --gradient-accumulate-every 8 --cl-reg --attn-layers [1,2]

24k iterations

Do you have any tuning suggestions for increasing speed and avoiding mode collapse? The results at 24k (above) are promising, but definitely need a lot more refining!

[erdavis1]

Haha, if only I could!

I ended up being pretty happy with the results of this run, even though it was fairly slow (~7 sec/iter if I got a V100)

!unet_stylegan2 --data jpegs/ --attn-layers [1,2] --batch-size 32 --gradient-accumulate-every 8 --aug-prob 0.25 --aug-types [translation,cutout,color]

Closing the issue since I’m satisfied with the output.

17k:

[erdavis1]

Thanks! I’ll try running that today and report back.