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What is Image Annotation?

Image annotation associates visual content with labels or geometric descriptions such as classes, points, bounding boxes, polygons, and masks. An annotation records what is present and, when applicable, where it appears.

How Image Annotation works

Annotation turns visual observations into structured records linked to an image, region, object, or pixel set. A schema defines permitted labels, geometry, attributes, relationships, and treatment of uncertain or obscured cases. Tools then serialize coordinates and identifiers for training, evaluation, search, moderation, or review. The work sits between asset preparation and model development, with quality control needed to keep human interpretation consistent.

Key facts

  1. Bounding-box coordinates may be stored as corners, origin plus dimensions, or normalized values; an undocumented convention can shift, invert, or rescale labels when datasets are converted.
  2. Instance masks distinguish separate objects of the same class, while semantic masks assign a class to pixels without necessarily preserving object identity; the formats support different model tasks.
  3. Agreement metrics can reveal ambiguous instructions, but consensus is not proof of correctness; gold examples, adjudication, and versioned guidelines are needed when the taxonomy evolves.

When Image Annotation matters

Select boxes when approximate location is sufficient, but use polygons or masks when training or review requires precise boundaries. Inconsistent labeling rules introduce ambiguity that can reduce model quality and reviewer agreement.

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