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.
@article{arxiv.2309.00832,
title = {ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data},
author = {Ulyana Tkachenko and Aditya Thyagarajan and Jonas Mueller},
journal= {arXiv preprint arXiv:2309.00832},
year = {2023}
}
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ICML Workshop on Data-centric Machine Learning Research