Metadata-Version: 2.4
Name: unicorn_eval
Version: 1.0
Summary: Evaluation and adaption method for the UNICORN Challenge
Home-page: https://github.com/DIAGNijmegen/unicorn_eval
Author: Joeran Bosma, Clément Grisi, Marina D`Amato, Luc Builtjes, Lena Philipp, Fennie van der Graaf, Judith Lefkes, Michelle Stegeman, Rianne Weber
Author-email: Clement.Grisi@radboudumc.nl
Project-URL: Bug Tracker, https://github.com/DIAGNijmegen/unicorn_eval/issues
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# 🧪 UNICORN Evaluation Toolkit

Welcome to the official evaluation repository for the [UNICORN Challenge](https://unicorn.grand-challenge.org/) — a benchmark for foundation models in pathology, radiology and medical language processing. This repository provides the code used to evaluate submissions using frozen foundation model features. It ships with a set of feature adaptors that convert features into predictions and expects to community to contribute with custom & more fancy adaptors.

## 🚀 Goal

The challenge evaluates how well foundation models generalize across tasks without extensive fine-tuning. For language and vision-language tasks, the model should yield the prediction. For vision tasks, we adapt features using light-weight methods (adaptors). Participants are invited to use built-in adaptors or propose their own!

## 🧩 Custom Adaptors

Want to use a custom method to convert vision features to predictions?

- add your adaptor under the adaptors/ directory
- submit a pull request with a short description of your method, giving it a unique name that can be selected at submission time

Once approved and merged, you’ll be able to submit your model using your custom adaptor.

⚠️ All adaptors must follow the base adaptor interface (see `adaptors/base.py`).
