Bounding boxes, polygons, pixel-level segmentation, and keypoints — labeled by trained annotators and checked through three layers of review before it reaches your training set.
We match the annotation type to what your model actually has to learn — not a one-size-fits-all label set.
Fast, precise rectangular boxes for object detection — vehicles, products, people, defects, and more.
Tight, irregular-shape outlines for objects that boxes can't capture accurately, from packaging to anatomy.
Every pixel classified into a category — roads, sky, tissue, terrain — for scene-understanding models.
Pixel masks that separate individual object instances, even when they overlap or touch.
Joint and landmark labeling for pose estimation, motion capture, and biomechanical analysis.
Line-based labeling for lane markings, cables, edges, and other linear features.
Product recognition, shelf and planogram compliance, visual search catalogs.
Anatomical structure and anomaly labeling for diagnostic and research models.
Person and object detection across static and multi-camera imagery.
We work with JPEG, PNG, TIFF, DICOM for medical imagery, and most common raster formats, delivered in COCO, Pascal VOC, YOLO, or your custom JSON schema.
Every asset passes through annotator, reviewer, and senior QA sign-off, with accuracy tracked against your acceptance thresholds throughout the project.
Yes. Medical and other sensitive image sets are handled under NDA with restricted access controls — see our Medical Annotation page for specifics.
Share your requirements to discuss a suitable pilot and proposal scope.