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A Framework for Efficient Model Evaluation Through Stratification, Sampling, and Estimation

  • Conference paper
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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15146))

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Abstract

Model performance evaluation is a critical and expensive task in machine learning and computer vision. Without clear guidelines, practitioners often estimate model accuracy using a one-time completely random selection of the data. However, by employing tailored sampling and estimation strategies, one can obtain more precise estimates and reduce annotation costs. In this paper, we propose a statistical framework for model evaluation that includes stratification, sampling, and estimation components. We examine the statistical properties of each component and evaluate their efficiency (precision). One key result of our work is that stratification via \(k\)-means clustering based on accurate predictions of model performance yields efficient estimators. Our experiments on computer vision datasets show that this method consistently provides more precise accuracy estimates than the traditional simple random sampling, even with substantial efficiency gains of 10x. We also find that model-assisted estimators, which leverage predictions of model accuracy on the unlabeled portion of the dataset, are generally more efficient than the traditional estimates based solely on the labeled data.

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Notes

  1. 1.

    In preliminary experiments we assessed other model-assisted estimators in the class of “generalized” regression estimators [8, 83, 100] and found results comparable to \(\texttt{DF}\).

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Acknowledgments

We thank the anonymous reviewers for their encouraging comments and valuable suggestions that have improved our manuscript. We also thank Tijana Zrnic for highlighting the connections between prediction-powered inference and our work, as well as Georgy Noarov for pointing out the link between our results and decompositions for proper scoring rules.

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Authors and Affiliations

  1. Amazon Web Services, Seattle, USA

    Riccardo Fogliato, Mathew Monfort & Pietro Perona

  2. University of California Berkeley, Berkeley, USA

    Pratik Patil

  3. California Institute of Technology, Pasadena, USA

    Pietro Perona

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  1. Riccardo Fogliato
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  2. Pratik Patil
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  3. Mathew Monfort
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  4. Pietro Perona
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Correspondence to Riccardo Fogliato .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Fogliato, R., Patil, P., Monfort, M., Perona, P. (2025). A Framework for Efficient Model Evaluation Through Stratification, Sampling, and Estimation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15146. Springer, Cham. https://doi.org/10.1007/978-3-031-73223-2_9

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