A car mask can pass review and still teach the model to keep the wrong pixels.
The outline looks clean. The bumper is inside. The wheels are inside. Then the trained network holds onto the dark patch under the tires, because the label treated that patch as part of the vehicle's visual neighborhood. Training stays quiet. Production gets loud the first time a listing photo drags a strip of the old lot onto a new backdrop.
AutoLensAI turns dealer photography into listing-ready vehicle media. This installment follows the earlier pieces on segmentation and image provenance, then narrows to one question: how do I teach a matting model that the shadow touching a tire is evidence against foreground rather than a faint version of it?
1. The failure arrives without an error message
Vehicle matting estimates which pixels belong to the vehicle, at finer boundary resolution than segmentation gives. Tires, rocker panels, glossy showroom floors, and the halo under a lowered front lip are where a pretty binary mask does its damage.
