  Despite powering sensitive systems like autonomous vehicles, object detection
remains fairly brittle in part due to annotation errors that plague most
real-world training datasets. We propose ObjectLab, a straightforward algorithm
to detect diverse errors in object detection labels, including: overlooked
bounding boxes, badly located boxes, and incorrect class label assignments.
ObjectLab utilizes any trained object detection model to score the label
quality of each image, such that mislabeled images can be automatically
prioritized for label review/correction. Properly handling erroneous data
enables training a better version of the same object detection model, without
any change in existing modeling code. Across different object detection
datasets (including COCO) and different models (including Detectron-X101 and
Faster-RCNN), ObjectLab consistently detects annotation errors with much better
precision/recall compared to other label quality scores.
