Year: 2026
Type: article
Abstract: Machine learning models, particularly deep neural networks, can produce unreliable predictions when they encounter data that differs from what they were trained on.Detecting such casescommonly referred to as out-of-distribution (OOD) detection -is particularly important in safety-critical applications (Yang et al., 2024).Despite rapid progress in the field, practical experimentation remains fragmented.Implementations are often tied to individual papers, interfaces differ across methods, and evaluation setups can be difficult to reproduce.pytorch-ood is a Python library that provides a unified framework for OOD detection in the PyTorch deep learning ecosystem (Paszke et al., 2019).It encompasses a broad range of detection methods, training objectives, datasets, pretrained models, and evaluation tools under a consistent interface.This allows users to apply different methods, compare them under shared conditions, and integrate them into existing workflows with minimal additional code.The library is designed to support flexible experimentation rather than a single fixed benchmark.It is accompanied by extensive documentation and unit tests, enabling reliable reuse and facilitating reproducible research.
DOI: 10.21105/joss.10806
Institution: Otto-von-Guericke-Universität Magdeburg
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7220414306