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.

翻译：分布外（Out-of-Distribution, OOD）检测对于开放世界智能系统的可靠运行至关重要。尽管涌现出越来越多的OOD检测方法，但评估标准不一致的问题给该领域的进展追踪带来了挑战。OpenOOD v1启动了OOD检测评估的统一化工作，但在可扩展性和易用性方面存在局限性。为此，本文提出了OpenOOD v1.5，这是对其前身的重要改进，旨在确保对OOD检测方法进行准确、标准化且用户友好的评估。值得注意的是，OpenOOD v1.5将评估能力扩展至ImageNet等大规模数据集，探究了重要但尚未充分研究的全谱OOD检测，并引入了新特性，包括一个在线排行榜和易于使用的评估器。本工作还基于全面的实验结果进行了深入分析并提炼出见解，从而丰富了OOD检测方法的知识库。借助这些改进，OpenOOD v1.5旨在推动OOD检测研究取得进展，并提供更稳健、更全面的评估基准。