Asked about Detection and segmentation in practice
Explain mean average precision for object detection.
An answer that works
For each class, rank predictions by confidence, match them to ground truth boxes by IoU above a threshold, and compute the area under the precision-recall curve: that is average precision. Mean over classes gives mAP. COCO-style mAP also averages over IoU thresholds from 0.5 to 0.95.
Then they push further
Your detector has good mAP but misses small objects the business cares about. What do you do?
Measure mAP by object size to confirm, then try higher input resolution, tiling large images, anchor or architecture settings suited to small objects, and more labelled small examples.
What gets marked up
Marked up for IoU matching, the precision-recall curve, and breaking the metric down by slice.
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