Since this is for the mac you really should be using apple's vision framework for this. It smokes tesseract in both speed and accuracy.

Why chose CLIP to do this. Have you tried small VLMs like Qwen-VL? I believe those models have video encoders can better perform at this scenario.

Slightly offtopic, but made me wonder.

Can you copywrite things like this now that LLMs exist? I mean, up until now if a small startup has a great idea they will get bought out by big tech which will integrate (or kill) their tech. But now with LLMs can the likes of OpenAI just tell their model to make something that works similar to X (such as this project) and then get round copying laws and negate being behind the curve?

Why is this a JS bloatware instead of native or Rust which is easier than ever now with LLM coding tools.

How well do you think this would work on stock photography on m1 mac with 32GB ram? For example I'd like to be able to search a folder of ~2k photos for houses with palm trees. Or find photos of kitchens, or find photos of desert southwest landscapes.

Having built something similar with CLIP on an M1, frame sampling rate is the whole ballgame. One frame a second on 12k videos is days, keyframes only got me to an overnight run.

would be lovely if picture embeddings were attached to the file by the camera but one can only dream of such futures

I like the entire premise, the one thing stopping me from trying this is not knowing the time scales that I will need to set my computer aside for the processing of large folders of video frames, or my photos library's videos, some 12,000 videos

possibly off-topic, but for anyone interested in this on a more cross-platform / holistic basis, Immich does this

(& by "this" I mean an approximate AI search for photos & videos - I can't account for the "every frame", nor for the comparative search quality)

This is cool. Any way to search for People / faces / pets? Like on iOS?

How do you do that? What's the architecture? Can you guide on that?