Fundus DR Grading

Description

This project aims to evaluate the performance of different models for the classification of diabetic retinopathy (DR) in fundus images. The reported perfomance metrics are not always consistent in the literature. Our goal is to provide a fair comparison between different models using the same datasets and evaluation protocol.

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Safetensors

Model size

7.77M params

Tensor type

F32

Inference API

Unable to determine this model’s pipeline type. Check the docs .

Collection including ClementP/FundusDRGrading-efficientnet_b2

Fundus Grading

Collection

Trained with: Aptos, EyePACS, DDR and IDRiD. • 25 items • Updated Jun 4

Evaluation results

Quadratic Kappa on EYEPACS
self-reported

0.758
Quadratic Kappa on IDRID
self-reported

0.704
Quadratic Kappa on DDR
self-reported

0.735

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