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.

Downloads last month: 1

Safetensors

Model size

304M params

Tensor type

F32

Inference API

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

Collection including ClementP/FundusDRGrading-vit_large_patch14_dinov2

Fundus Grading

Collection

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

Evaluation results

Quadratic Kappa on EYEPACS
self-reported

0.766
Quadratic Kappa on IDRID
self-reported

0.800
Quadratic Kappa on DDR
self-reported

0.727

View on Papers With Code