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arXiv · 2510.27263

ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction

Han Yu·Kehan Li·Dongbai Li·Yue He·Xingxuan Zhang·Peng Cui
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Abstract

Recently, there has been gradually more attention paid to Out-of-Distribution (OOD) performance prediction, whose goal is to predict the performance of trained models on unlabeled OOD test datasets, so that we could better leverage and deploy off-the-shelf trained models in risk-sensitive scenarios. Although progress has been made in this area, evaluation protocols in previous literature are inconsistent, and most works cover only a limited number of real-world OOD datasets and types of distribution shifts. To provide convenient and fair comparisons for various algorithms, we propose Out-of-Distribution Performance Prediction Benchmark (ODP-Bench), a comprehensive benchmark that includes most commonly used OOD datasets and existing practical performance prediction algorithms. We provide our trained models as a testbench for future researchers, thus guaranteeing the consistency of comparison and avoiding the burden of repeating the model training process. Furthermore, we also conduct in-depth experimental analyses to better understand their capability boundary.

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Han Yu, Kehan Li, Dongbai Li, Yue He, Xingxuan Zhang, Peng Cui. 2025-10-31. ODP-Bench: Benchmarking Out-of-Distribution Performance Prediction. https://arxiv.org/abs/2510.27263

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