data-efficient-gans VS YOLO_Object_Detection

Code for https://arxiv.org/abs/2006.10738 found: https://github.com/mit-han-lab/data-efficient-gans

I would like to make a tiny project with GANs using some high quality pictures of a single individual. I am planning to get around 500 of these and then x-flip them, however I am not sure what model I should consider for the training. I have used StyleGan2 ADA for another project which ended quite well, but I had around 14k pictures, here now the training size is much smaller and I was therefore thinking about using DiffAugment which has seemingly promising results with just 100 images.

I used a modified version of this repo, and there's also the official NVIDIA implementation, though neither have official notebooks. You can Google 'StyleGAN2 ADA Colab' and find a few starting points that way, but wait a few hours and I can clean up my notebook and post it here!

Paper: https://arxiv.org/abs/2006.10738

There are some repos which I've looked at to add deepspeed to such as DiffAugment-stylegan2-pytorch, lucidrains/stylegan2-pytorch and eps696/stylegan2 (which is in tensorflow so it would need to be translated to pytorch as deepspeed only works with pytorch right now).

Here is the paper using GANs with few data points https://arxiv.org/abs/2006.10738

Hey, for image gans, you can use smooth data aumentation https://github.com/mit-han-lab/data-efficient-gans in case you have a reasonable sized dataset.

But using GANs for a newperson is maybe asking too much. See if you can find weights for yolo and just finetune it https://github.com/llSourcell/YOLO_Object_Detection

is a blatant lie. He didn't modify anything but the README as far as I am aware. He uses the exact same sentence in many of his cloned repos, see also this and this. But that's beside the point because the right thing to do would to fork a repository if you just want to make minor changes, or star it and share the original repo link on his YouTube channel if all he does is doing quick walkthroughs through the source code with arguably bad explanations.