In this thread I would like to discuss the interesting ways we've found to increase model accuracy when analyzing images. I have been learning about vision models and trying a few different strategies, to varying degrees of success, and would like to brainstorm a bit further.

To that end, I have a few questions for HN:

What's your experience with bounding boxes?

What's your experience with different models/what are your favorites?

What's your experience with triaging extraction errors while managing cost?