Transformers
PyTorch
Safetensors
English
fundus
diabetic retinopathy
classification
Eval Results (legacy)
Instructions to use ClementP/FundusDRGrading-resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClementP/FundusDRGrading-resnet50 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ClementP/FundusDRGrading-resnet50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language: en
license: mit
tags:
- fundus
- diabetic retinopathy
- classification
datasets:
- APTOS
- EYEPACS
- IDRID
- DDR
library: timm
model-index:
- name: resnet50
results:
- task:
type: image-classification
dataset:
name: EYEPACS
type: EYEPACS
metrics:
- type: kappa
value: 0.7331207990646362
name: Quadratic Kappa
- task:
type: image-classification
dataset:
name: IDRID
type: IDRID
metrics:
- type: kappa
value: 0.6275907754898071
name: Quadratic Kappa
- task:
type: image-classification
dataset:
name: DDR
type: DDR
metrics:
- type: kappa
value: 0.7102091312408447
name: Quadratic Kappa
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.