FrOoDo: Framework for Out-of-Distribution Detection

Anirban Mukhopadhyay; Jonathan Stieber; Moritz Fuchs

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FrOoDo: Framework for Out-of-Distribution Detection

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arxiv 2208.00963 v2 pith:DCCNLQF7 submitted 2022-08-01 cs.CV

FrOoDo: Framework for Out-of-Distribution Detection

Jonathan Stieber , Moritz Fuchs , Anirban Mukhopadhyay This is my paper

classification cs.CV

keywords froododetectionframeworkgoalmodelsout-of-distributionallowsautomate

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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.

FrOoDo: Framework for Out-of-Distribution Detection

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