ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data

Such Swapped errors are also common vehicles, object detection remains fairly in many classification datasets (Northcutt et al., 2021a), brittle in part due to annotation errors that plague but the increased complexity of object detection annotation most real-world training datasets. We propose introduces potential for more varied types of label errors ObjectLab, a straightforward algorithm to detect than encountered in classification. We propose an algorithm, diverse errors in object detection labels, including: ObjectLab, that utilizes any trained object detection model overlooked bounding boxes, badly located boxes, to estimate the incorrect labels in such a dataset, regardless and incorrect class label assignments. Object-which of these 3 types of mistake the data annotators made. Lab utilizes any trained object detection model to score the label quality of each image, such that Training and evaluating models with incorrect bounding box mislabeled images can be automatically prioritized annotations is clearly worrisome.