I created a Python package to generate image embeddings from CLIP's vision model without requiring a ML framework (https://github.com/minimaxir/imgbeddings ), and a simple linear classifier on those embeddings does the trick, demo here: https://github.com/minimaxir/imgbeddings/blob/main/examples/...

Most software applications have low tolerance for error rates, the ones that do have big money being spent on ensuring their accuracy is better than everybody else’s.

So while you can make a classifier out of anything in Y time, that doesn’t say anything about whether it’s of any practical use.

EDIT: I raise this issue as over-promises are the death nell for software. Overpromising capability leads to disappointment.

It's definitely better performance than what you'd get working in 5 minutes from more conventional approaches.

I'm actually looking for a decent classifier / object recognition platform to sort on the order of millions of images coarsely - as it stands all of the ones i've tried can't determine if an image is drawn/painted or a photograph, for instance, which reduces my enthusiasm of the whole field.

On the other hand, audio AI/ML stuff - such as spleeter - impresses me, as i can't do that stuff by hand.

Did you consider using CLIP like parent comment said?