AI Wheel Hub Image Classification: Validation and Exception Review
Define whether the system is identifying product family, view angle, asset quality, possible mismatch or a different task. Build a labeled dataset from controlled wheel hub records, keeping duplicate shots and near-identical variants from leaking across training and acceptance sets. Include difficult lighting, packaging, occlusion, mirrored views, similar flanges and products with different encoder or spline details. Review false accepts and false rejects separately, require human escalation for uncertain or consequential cases, and bind release to the tested model, preprocessing and camera workflow. Never convert a visual label into fitment approval unless independent authoritative data confirms it.