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---
language: en
license: mit
tags:
- fundus
- diabetic retinopathy
- classification
datasets:
- APTOS
- EYEPACS
- IDRID
- DDR
library: timm
model-index:
- name: efficientnet_b0
results:
- task:
type: image-classification
dataset:
name: EYEPACS
type: EYEPACS
metrics:
- type: kappa
value: 0.7514463067054749
name: Quadratic Kappa
- task:
type: image-classification
dataset:
name: IDRID
type: IDRID
metrics:
- type: kappa
value: 0.7511317133903503
name: Quadratic Kappa
- task:
type: image-classification
dataset:
name: DDR
type: DDR
metrics:
- type: kappa
value: 0.7121561765670776
name: Quadratic Kappa
---
# Fundus DR Grading
[![Rye](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/rye/main/artwork/badge.json)](https://rye-up.com)
[![PyTorch](https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/docs/stable/index.html)
[![Lightning](https://img.shields.io/badge/Lightning-792ee5?logo=lightning&logoColor=white)](https://lightning.ai/docs/pytorch/stable/)
## 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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