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The quality of training datasets for deep neural networks is a key factorcontributing to the accuracy of resulting models. This effect is amplified indifficult tasks such as object detection. Dealing with errors in datasets isoften limited to accepting that some fraction of examples are incorrect,estimating their confidence, and either assigning appropriate weights orignoring uncertain ones during training. In this work, we propose a differentapproach. We introduce the Confident Learning for Object Detection (CLOD)algorithm for assessing the quality of each label in object detection datasets,identifying missing, spurious, mislabeled, and mislocated bounding boxes andsuggesting corrections. By focusing on finding incorrect examples in thetraining datasets, we can eliminate them at the root. Suspicious bounding boxescan be reviewed to improve the quality of the dataset, leading to better modelswithout further complicating their already complex architectures. The proposedmethod is able to point out nearly 80% of artificially disturbed bounding boxeswith a false positive rate below 0.1. Cleaning the datasets by applying themost confident automatic suggestions improved mAP scores by 16% to 46%,depending on the dataset, without any modifications to the networkarchitectures. This approach shows promising potential in rectifyingstate-of-the-art object detection datasets.

Combating noisy labels in object detection datasets