This needs a lot more work to make it beautiful.

IIRC, CP extends Platt.

I have a classification model that uses conformal prediction. This has been helpful in working towards building out a high confidence dataset.

There are so many kinds of data and so many kinds of data annotations.

For deep learning, the quantity required is high. The question is always “how much is enough?”

You kind of need to narrow your question.

If I was you, I’d look at your competitors and see what they claim to do. Often it’s shipping customers’ image data off to somewhere for cheap labor. And requires the customer to look at the annotations to verify they’re as expected. In my limited experience, there’s a LOT of review.

Data is everything.

Settle down Veruca

“Say, I smell bacon, does anyone else smell bacon?” Wayne’s World

https://m.youtube.com/watch?v=CtXnIBMLFa4

This seems so heavily subjective

Good point. This test needs to be randomized to avoid the confounding factors.

This is interesting. My background is in Mechanical Engineering (weakly solid mechanics) and I’ve been semi on the lookout for a connection between mechanics and neural networks. This is kind of a missing link I’m going to investigate further.