CleanPatrick: New Benchmark for Evaluating Image Data Cleaning Methods
Researchers have introduced CleanPatrick, the first large-scale benchmark designed to evaluate image data cleaning methods for machine learning, built on the publicly available Fitzpatrick17k dermatology dataset. The benchmark draws on nearly 500,000 binary annotations from 933 medical crowd workers and uses an item-response theory-inspired aggregation model to establish ground truth. It addresses a gap in the field where existing benchmarks rely on synthetic noise or narrow human studies, limiting real-world applicability.
CleanPatrick is a newly published benchmark accepted at the Journal of Data-centric Machine Learning Research (DMLR) that aims to systematically evaluate strategies for cleaning image datasets used in machine learning. Built on the Fitzpatrick17k dermatology dataset, it incorporates 496,377 binary annotations collected from 933 medical crowd workers, identifying off-topic samples (4%), near-duplicates (21%), and label errors (32%) within the data. Ground truth is derived through an aggregation model inspired by item-response theory, followed by expert review, to ensure high-quality labels. The benchmark formalizes data issue detection as a ranking task and uses standard ranking metrics intended to mirror real-world audit workflows. In evaluations, self-supervised representations performed best at near-duplicate detection, classical anomaly detection methods were competitive for off-topic sample identification under constrained review budgets, and detecting implausible labels in fine-grained medical classification remained difficult for all tested methods. The researchers release both the dataset and evaluation framework publicly to enable reproducible, systematic comparisons of image-cleaning approaches.
What's missing
It is unclear how well performance on the dermatology-specific Fitzpatrick17k dataset would transfer to image data cleaning benchmarks in other domains.
What different sources said
- arXiv cs.LGCenter
CleanPatrick: A Benchmark for Image Data Cleaning
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