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The iMet 2020 dataset is a valuable resource in the space of fine-grained artattribution recognition, but we believe it has yet to reach its true potential.We document the unique properties of the dataset and observe that many of theattribute labels are noisy, more than is implied by the dataset description.Oftentimes, there are also semantic relationships between the labels (e.g.,identical, mutual exclusion, subsumption, overlap with uncertainty) which webelieve are underutilized. We propose an approach to cleaning and structuringthe iMet 2020 labels, and discuss the implications and value of doing so.Further, we demonstrate the benefits of our proposed approach through severalexperiments. Our code and cleaned labels are available athttps://github.com/sunniesuhyoung/iMet2020cleaned.

Cleaning and Structuring the Label Space of the iMet Collection 2020