Bounding Box Annotation Services

Each box can store multiple attributes, such as class, subclass, pose, and state, enabling multi‑label classification at the instance level. Taxonomies are co‑designed with clients so that downstream detection and classification heads can consume the same schema without additional mapping.

2D bounding boxes are rectangles drawn on flat images, defining object location with X and Y coordinates. 3D bounding boxes provide depth perception by encoding height, width, and length in three-dimensional space, and are commonly used in LiDAR data and autonomous driving applications. 3D boxes provide spatial understanding.

Retail uses it for product recognition and self-checkout systems. Autonomous vehicles rely on it for detecting cars, pedestrians, and obstacles. Healthcare applies it to medical scan analysis. Insurance uses it for damage assessment. Security systems use it for surveillance and threat detection. Agriculture benefits for crop monitoring.

Our annotators follow domain‑specific boxing rules (object extent, occlusion handling, minimum pixel size) and use calibrated examples for each project. Consistency is enforced via multi‑stage review plus automated checks for overlap, aspect‑ratio outliers, and missed objects.

We often start with model‑generated boxes and have annotators correct their positions, sizes, and classes rather than label from scratch. This HITL loop reduces cost while systematically removing systemic model errors and biases in edge cases.

Multi-label classification assigns multiple tags to a single bounding box when an object belongs to several categories. For example, a vehicle might be labeled as “car,” “sedan,” and “red vehicle.” This provides richer data for training AI models to simultaneously understand multiple object attributes.