Evaluating Visual Models With Floor Reality, Not Kaggle

Published 2026-05-31 by Faraz Rahimi

A leaderboard score on clean images will not tell you if aisle 3 still radios for help. Build an evaluation set from the week you actually had.

Evaluating Visual Models With Floor Reality, Not Kaggle

Tags: Machine Learning, Evaluation, Computer Vision, Warehouse, unlisted

I keep a holdout set that is not pretty. It is last week's production queries with the eventual truth, including unknowns and rejects.

If a new model wins on the pretty set and loses on last week, it does not ship. That rule has saved me from cleverness.

Include the humans in the metric

A model that is slightly less accurate but much faster can win on the floor. I look at time-to-identity and abandonment alongside hit rate.

A slower, slightly smarter model that people skip is not smarter.

Refresh the holdout

A frozen eval set from launch day becomes a souvenir. I rotate in new weeks so we evaluate against current packaging and current lighting, not the warehouse we imagined.

Original post: https://farazrahimi.com/posts/evaluating-visual-models-with-floor-reality-not-kaggle