Out-of-Distribution (OOD) detection is critical for the reliable operation of
open-world intelligent systems. Despite the emergence of an increasing number
of OOD detection methods, the evaluation inconsistencies present challenges for
tracking the progress in this field. OpenOOD v1 initiated the unification of
the OOD detection evaluation but faced limitations in scalability and
usability. In response, this paper presents OpenOOD v1.5, a significant
improvement from its predecessor that ensures accurate, standardized, and
user-friendly evaluation of OOD detection methodologies. Notably, OpenOOD v1.5
extends its evaluation capabilities to large-scale datasets such as ImageNet,
investigates full-spectrum OOD detection which is important yet underexplored,
and introduces new features including an online leaderboard and an easy-to-use
evaluator. This work also contributes in-depth analysis and insights derived
from comprehensive experimental results, thereby enriching the knowledge pool
of OOD detection methodologies. With these enhancements, OpenOOD v1.5 aims to
drive advancements and offer a more robust and comprehensive evaluation
benchmark for OOD detection research.

本研究介绍了 OpenOOD v1.5，它是 OpenOOD v1 的显著改进，旨在确保精确、标准化和用户友好的 Out-of-Distribution 检测方法评估。OpenOOD v1.5 将其评估能力扩展到大规模数据集，研究全谱 Out-of-Distribution 检测，并引入了在线排行榜和易于使用的评估器等新功能，从而为 OOD 检测研究提供更加稳健和全面的评估基准。