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If anyone is interested in the technical details, the database itself is a 4GB SQLite file which we are hosting with Datasette running on Fly.
More details in our repo: https://github.com/simonw/laion-aesthetic-datasette
Search is provided by SQLite FTS5.
I recommend looking into "transfer learning".
That's where you start with an existing large model, and train a new model on top of it by feeding in new images.
What's fascinating about transfer learning is that you don't need to give it a lot of new images, at all. Just a few hundred extras can create a model that's frighteningly accurate for tasks like image labeling.
This is pretty much how all AI models work today. Take a look at the Stable Diffusion model card: https://github.com/CompVis/stable-diffusion/blob/main/Stable...
They ran multiple training sessions with progressively smaller (and higher quality) images to get the final result.
Done https://github.com/rom1504/clip-retrieval/commit/53e3383f58b...
Using clip for searching is better than direct text indexing for a variety of reasons but here for example because it matches better what stable diffusion sees
