Bounding Box Annotation

Bounding box annotation is the foundational computer vision labeling task: drawing the tightest rectangle that fully contains a target object in an image, then tagging it with a class label. It's the entry point into object detection datasets and typically the first technique annotators learn before moving to more precise tasks like segmentation.

Quality here comes down to tightness and consistency — boxes that are too loose include background noise that confuses the model during training, while boxes that clip part of the object teach it an incomplete shape. Partially occluded objects are a common edge case that guidelines need to address explicitly (box the visible portion? estimate the full extent?).

More complex spatial annotation tasks — 3D point cloud annotation, video event annotation — build on the same core skill of precisely bounding an object in space or time.