Large-scale object detection, segmentation, and captioning dataset from Microsoft Research.
Browse commercial Computer Vision → Visit original source ↗COCO is one of the most widely-used object detection benchmarks in computer vision. Released by Microsoft Research in 2014 and expanded since, it contains 330K images with 2.5M labeled object instances across 80 common object categories, plus 250K people with keypoints and 1.5M object segmentation masks. It's the de facto benchmark for object detection, instance segmentation, and image captioning models.
LQS is our 7-dimension quality score, computed from the dataset's published statistics. See methodology →
Composite score computed from the 7 dimensions below: completeness, uniqueness, validation health, size adequacy, format compliance, label density, and class balance.
Common tasks and benchmarks where COCO — Common Objects in Context is the default or competitive choice.
What's actually in the dataset — from the maintainer's published stats.
COCO — Common Objects in Context is distributed under CC BY 4.0. This is a third-party public dataset; LabelSets indexes and scores it but does not host or redistribute the data. Always verify current license terms with the maintainer before commercial use.
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Other entries in the Computer Vision catalog.