Annotation

Annotation is the process of labeling raw data with structured information that tells a machine learning model what to learn. In computer vision, this means drawing bounding boxes around objects for detection, tracing polygon or mask outlines for segmentation, placing keypoints on body joints for pose estimation, or assigning class tags to entire images for classification. The quality of annotations directly determines how well the model performs.

Different tasks require different annotation types. Bounding boxes are the fastest to create (10-30 seconds each) and work for object detection. Polygon and pixel masks take longer (1-5 minutes per object) but are needed for instance and semantic segmentation. Keypoint annotation marks specific landmarks and is used for pose estimation, facial recognition, and gesture tracking. Classification labels are the simplest, requiring just a tag per image.

Annotation quality control matters as much as quantity. Inconsistent labels, missed objects, and imprecise boundaries introduce noise that the model learns from. Production teams use review queues, inter-annotator agreement scores, and model-assisted pre-labeling to maintain consistency. Datature Nexus provides annotation tools for bounding boxes, polygons, keypoints, and classification with built-in review workflows.