YOLO has basically solved that for my use cases for a couple years now. If you want labels that are not in the pretrained labels it's also easy to fine-tune, provided you're willing to label 200 or so images
replyIf 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
> provided you're willing to label 200 or so images
replyA quick note to say that this is also a task you can hand to things like gemini.
How do you handle object disambiguation with YOLO? All the examples I've played with have the problem where if two "cars" get too close to each other then the tracking IDs keep switching between them, meaning we'd need an additional kinetic model for disambiguation.
replyThat seems to be the way things are going.
replyLarge 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/
Roboflow is your friend.
replymoondream is a beast
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