Computer vision annotation turns raw images and video into structured training signals for detection, segmentation, and classification models. InfoBay supports the full task range — bounding boxes, key points, and object tracking — through scalable pipelines built for real-world datasets, including egocentric and medical data.
Vision models fail on the messy, real-world scenes production systems actually see when their annotations come from clean, benchmark-style imagery. InfoBay annotates the hard cases: dense scenes, first-person capture, and clinical imaging.
Across 15+ vision categories for annotation workflows.
First-person data for embodied-AI annotation.
DICOM imaging and clinical records for medical vision tasks.
InfoBay annotates images and video for detection, segmentation, and classification, including the complex task types that break generalist annotation vendors.
Annotation pipelines scale to the datasets production vision systems actually train on — not just clean benchmark imagery, but first-person capture and clinical files with their own review requirements.
Answers for buyers
Detection, segmentation, and classification across images and video, including bounding boxes, polygons, key points, and object tracking across frames.
Yes. Video annotation includes frame-level labels and object tracking, so an entity keeps a consistent identity across a clip rather than being re-detected per frame.
Yes. Pipelines support DICOM-aware medical annotation on de-identified inputs and first-person egocentric data with spatial and hand-object interaction labels.
The same multi-tier QA model used across InfoBay's labeling services applies: annotation guidelines agreed up front, reviewer calibration, and layered validation before delivery.