The thing is, either you wait for the TF support or you code it yourself. Research is fast paced so researchers want a boilerplate, or just to build on top of another repo.

For deployment, there's a lot of deployment tools for Pytorch nowadays. I use BentoML for deploying compvision models. If you want a lighter model, there are libraries and repositories that support sparsification, pruning, etc. of your models. A lot of newer repos for industrial use (mmdet, YOLOv5/6/7) have sparsification and pruning support. Some of them even have distillation built-in. Again, I am not a TF guy so I havent seen such rich support for model deployment than these Pytorch-based repos.

While it is true that research and deployment are different, it is up to your team how to do MLOps on those SOTA methods. New SSL method, new loss functions, new stuff -- either you borrow it from a repo or you code it yourself. In the industry, the prototyping procedure for experimentatio is critical for a fast-moving product. You dont have time to convert codes. So you test repositories, then build on top of it if it is successful.