I trained an object detection model on a mixed dataset containing people, vehicles, and small objects like phones and traffic signs.The model detects large objects such as cars and people very reliably.However, it almost completely ignores smaller objects, ...
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Different teams trained models independently.Each performs well in certain cases.Now deployment is messy.Choosing one feels arbitrary.
Teams slow down intentionally. I want to understand why.
I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.
