  Distribution shifts -- where the training distribution differs from the test
distribution -- can substantially degrade the accuracy of machine learning (ML)
systems deployed in the wild. Despite their ubiquity in the real-world
deployments, these distribution shifts are under-represented in the datasets
widely used in the ML community today. To address this gap, we present WILDS, a
curated benchmark of 10 datasets reflecting a diverse range of distribution
shifts that naturally arise in real-world applications, such as shifts across
hospitals for tumor identification; across camera traps for wildlife
monitoring; and across time and location in satellite imaging and poverty
mapping. On each dataset, we show that standard training yields substantially
lower out-of-distribution than in-distribution performance. This gap remains
even with models trained by existing methods for tackling distribution shifts,
underscoring the need for new methods for training models that are more robust
to the types of distribution shifts that arise in practice. To facilitate
method development, we provide an open-source package that automates dataset
loading, contains default model architectures and hyperparameters, and
standardizes evaluations. Code and leaderboards are available at
https://wilds.stanford.edu.
