I'm curious how you're implementing the image similarity matching. I recently reverse engineered the Apple Neural Hash model and wrote an API to use it in my app for doing image similarity calculations. I found it to be extremely quick compared to some of the other more computationally intensive methods that I was trying to use before.
We're using redis vector modules for cosine similarity. I'm sure there's more to optimize there. Your project sounds cool. How'd you reverse engineer apple's model?