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COCO Dataset MCQ
Common Objects in Context—boxes, masks, captions, and person keypoints with rigorous AP metrics.
COCO
MS benchmark
Instances
Masks
Captions
Text
mAP
Evaluation
MS COCO in a nutshell
COCO provides instance segmentation masks, bounding boxes for 80 thing categories, person keypoints, captions, and panoptic labels. Evaluation uses COCO-style mAP with IoU thresholds and area ranges. The COCO API helps load annotations and compute metrics consistently.
Instance vs semantic
COCO instance segmentation separates individual objects with masks; semantic merges same-class regions without object IDs.
Key ideas
Detection
BBox + class with AP@[.5:.95].
Instance seg
Mask AP with mask IoU matching.
Captions
Image → multiple reference sentences; BLEU/CIDEr metrics.
Keypoints
Person skeleton annotations evaluated with OKS-AP.
JSON annotations
images + annotations with bbox, segmentation polygons/RLE, category ids