A practical way to evaluate computer vision models before you commit

If you work on a vision project, model choice is rarely just about the biggest name on the leaderboard. You usually need to answer a more specific question:

Which model handles my prompt or image style well?

Which one is better for the task I actually need?

Which option should I benchmark more deeply before I build around it?