FrOoDo: Framework for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2208.00963v2
• Date: Thu, 15 Feb 2024 13:33:33 GMT
• Title: FrOoDo: Framework for Out-of-Distribution Detection
• Authors: Jonathan Stieber, Moritz Fuchs, Anirban Mukhopadhyay
• Abstract summary: FrOoDo is an easy-to-use 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.
• Score: 1.3270838622986498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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