Why AI Results Can Be Wrong

Computer-vision models learn patterns from limited training data. Results can fail because of darkness, glare, motion blur, distance, partial visibility, unfamiliar objects, biased examples, compression, or a label vocabulary that does not fit the scene.

Tracking and experimental scoring add further assumptions. OrganicVision preserves model/version/settings and supports multiple runs so researchers can compare outputs. Human verification remains necessary.