# Cleaning and Structuring the Label Space of the iMet Collection 2020

arXiv: https://arxiv.org/abs/2106.00815 · HTML: https://mlsanity.com/paper/2106.00815/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Evaluation Measures for Hierarchical Classification: a unified view and novel approaches](https://mlsanity.com/paper/1306.6802/) (2013)
- [BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography](https://mlsanity.com/paper/1704.08614/) (2017)
- [Learning Structured Inference Neural Networks with Label Relations](https://mlsanity.com/paper/1511.05616/) (2015)
- [The iMet Collection 2019 Challenge Dataset](https://mlsanity.com/paper/1906.00901/) (2019)
- [Probabilistic Label Relation Graphs with Ising Models](https://mlsanity.com/paper/1503.01428/) (2015)
- [Learning Representations For Images With Hierarchical Labels](https://mlsanity.com/paper/2004.00909/) (2020)

## Abstract (arXiv)

The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution 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 the attribute 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 we believe are underutilized. We propose an approach to cleaning and structuring the iMet 2020 labels, and discuss the implications and value of doing so. Further, we demonstrate the benefits of our proposed approach through several experiments. Our code and cleaned labels are available at https://github.com/sunniesuhyoung/iMet2020cleaned.