Reading boxes and scores
A detection usually contains a class, bounding box and confidence score. That score is not a guarantee; choose decision thresholds using samples. Overlapping objects and a small part disappearing into the background require separate test cases. A box alone does not provide a precise physical measurement or an exact outline.
Data and evaluation
Document relevant classes, annotation rules for overlap and how incomplete views are labelled. Evaluate unseen samples. Precision, recall and mAP answer different questions; a single aggregate score does not explain errors in every class. Connect acceptance criteria to the cost of false alarms and missed detections in the intended use.
From detection to operational event
Counting conveyor boxes cannot simply sum detections across frames: one box appears many times. A crossing region, tracking and rules against duplicate counts are needed. Define behaviour for camera disconnection and delayed results separately. Review the selected model and software licences for their actual commercial-use requirements.