FrOoDo: Framework for Out-of-Distribution Detection

TL;DR

FrOoDo is a flexible, modular framework designed to simplify Out-of-Distribution detection in digital pathology, compatible with PyTorch models, and aims to streamline OoD evaluation for research focus.

Contribution

It introduces an easy-to-use, extendable framework for OoD detection in digital pathology compatible with existing PyTorch models.

Findings

Facilitates OoD detection in digital pathology

Supports classification and segmentation models

Enhances research efficiency in OoD evaluation

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.