Model: Retinocare/diabetic-retinopathy
Author: Retinocare
Library: keras
Tags: keras, region:us
Card metadata: Diabetic Retinopathy Detector Model: description: A machine learning model that detects diabetic retinopathy with 97.5% accuracy.
license: unknown
datasets: name: Diabetic Retinopathy Dataset
description: A dataset of retinal images annotated with diabetic retinopathy severity levels.
source: Kaggle (or specify dataset source if different)
language: Python
metrics: accuracy: 97.5%, f1_score: Specify if available, precision: Specify if available, recall: Specify if available
base_model: Custom-built CNN (or specify if based on a pre-trained model like ResNet or EfficientNet)
new_version: v1.0
pipeline_tag: image-classification
library_name: TensorFlow
tags: Diabetic Retinopathy, Medical Imaging, Healthcare AI, Image Classification
eval_results: test_set_accuracy: 97.5%
eval_dataset_size: Specify size if available
notes: Evaluated on a balanced test set of retinal images.
metrics: accuracy
Likes: 0 | All-time downloads: 109
Created: 2025-01-11 13:49:06