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 prioritized for label review/correction. Properly handling the erroneous data enables training a better version of the same object detection model, without any change in existing modeling code. Benchmarks on SYNTHIA and naturally-occurring annotation errors in COCO reveal that across different object detection models/datasets, ObjectLab consistently detects error with much better precision/recall compared to other label quality scores.