Peerannot: classification for crowdsourced image datasets with Python

Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon 2024-05-07

Citation

Tanguy Lefort, Benjamin Charlier, Alexis Joly and Joseph Salmon (May 2024). Peerannot: classification for crowdsourced image datasets with Python. Computo. https://doi.org/10.57750/qmaz-gr91

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Authors’ affiliations

• Tanguy Lefort (IMAG, Univ Montpellier, CNRS, Inria, LIRMM)
• Benjamin Charlier (IMAG, Univ Montpellier, CNRS)
• Alexis Joly (Inria, LIRMM, Univ Montpellier, CNRS)
• Joseph Salmon (IMAG, Univ Montpellier, CNRS, IUF)

Abstract

Crowdsourcing is a quick and easy way to collect labels for large datasets, involving many workers. However, workers often disagree with each other. Sources of error can arise from the workers’ skills, but also from the intrinsic difficulty of the task. We present peerannot: a Python library for managing and learning from crowdsourced labels for classification. Our library allows users to aggregate labels from common noise models or train a deep learning-based classifier directly from crowdsourced labels. In addition, we provide an identification module to easily explore the task difficulty of datasets and worker capabilities.