mlsanityData to 14 Oct 2026

FrOoDo: Framework for Out-of-Distribution Detection

arXiv 2208.00963 ↗First posted 1 Aug 2022cs.CVcited by 4

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Abstract (arXiv)

FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new methods or evaluating a new dataset. The code can be found at https://github.com/MECLabTUDA/FrOoDo.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 1 Aug 2022.

Builds on

3 of 5 references.

References · 3

  1. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  2. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  3. 2020Energy-based Out-of-distribution Detectioncited by 1,299

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.