  Out-of-distribution (OOD) detection is vital to safety-critical machine
learning applications and has thus been extensively studied, with a plethora of
methods developed in the literature. However, the field currently lacks a
unified, strictly formulated, and comprehensive benchmark, which often results
in unfair comparisons and inconclusive results. From the problem setting
perspective, OOD detection is closely related to neighboring fields including
anomaly detection (AD), open set recognition (OSR), and model uncertainty,
since methods developed for one domain are often applicable to each other. To
help the community to improve the evaluation and advance, we build a unified,
well-structured codebase called OpenOOD, which implements over 30 methods
developed in relevant fields and provides a comprehensive benchmark under the
recently proposed generalized OOD detection framework. With a comprehensive
comparison of these methods, we are gratified that the field has progressed
significantly over the past few years, where both preprocessing methods and the
orthogonal post-hoc methods show strong potential.
