some SBC w/ an industrial camera that is doing pick-place or go/no-go operations on a conveyor belt against a singular object type doesn't need a huge image-gen/llm model governing it.

I mean have you even considered the kind of performance an opencv function can get w/ just mask-matching? I mean even with a fancy YOLO model these answers get thrown out in 1.5-50ms ; this is just a wholly different time scaling.

We're not going to fit Nano Banana or anything like it on a device with 512MB RAM and a GPU old enough to be irrelevant, and again, API calls just aren't on the menu.

Like, the AI model tools already exist, all that would be accomplished if OpenCV pivoted would be to take it away for people who want to do low-level vision programming. It wouldn't add anything useful to the world, just destroy an excellent library.

I might be on board about LLMs being the future of OCR (though many would disagree), but for general CV they are very inefficient for very limited benefit

Large general models have taken over in NLP, and (outside of embedded/low latency applications) it seems like they are coming for CV next.

So you should soon be able to have large generic model that can detect whatever for you.

It's already pretty much possible with open-vocabulary detectors like SAM3, where you could just prompt it with "Apple": https://ai.meta.com/research/sam3/

If you need something less restricted to existing labels (say wanting all the red apples, or all cardboard signs) SAM3 is great, as the sibling comment says

Sure, running models on the CPU is very much a thing in computer vision (the benchmarked YOLOv8n has 37M params). But this whole announcement feels more like OpenCV catching up to the modern world, not "The Biggest Leap in Years for Computer Vision"

Still great, needing fewer libraries is a good thing, but maybe a bit oversold