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Major advancements in computer vision can primarily be attributed to the useof labeled datasets. However, acquiring labels for datasets often results inerrors which can harm model performance. Recent works have proposed methods toautomatically identify mislabeled images, but developing strategies toeffectively implement them in real world datasets has been sparsely explored.Towards improved data-centric methods for cleaning real world vision datasets,we first conduct more than 200 experiments carefully benchmarking recentlydeveloped automated mislabel detection methods on multiple datasets under avariety of synthetic and real noise settings with varying noise levels. Wecompare these methods to a Simple and Efficient Mislabel Detector (SEMD) thatwe craft, and find that SEMD performs similarly to or outperforms priormislabel detection approaches. We then apply SEMD to multiple real worldcomputer vision datasets and test how dataset size, mislabel removal strategy,and mislabel removal amount further affect model performance after retrainingon the cleaned data. With careful design of the approach, we find that mislabelremoval leads per-class performance improvements of up to 8% of a retrainedclassifier in smaller data regimes.

An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets