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Diabetic Retinopathy 224 Procnorm Vit

Developed by rafalosa
Fine-tuned based on Google's ViT model for the diabetic retinopathy classification task, with an accuracy of 74.31%.
Downloads 110
Release Time : 4/30/2023

Model Overview

This model is a diabetic retinopathy classifier based on the Vision Transformer (ViT) architecture. It helps identify retinal lesions caused by diabetes by analyzing fundus images.

Model Features

High-precision classification
Achieved an accuracy of 74.31% on the diabetic retinopathy dataset.
Based on the ViT architecture
Adopts the Vision Transformer architecture and uses the self-attention mechanism to process images.
Transfer learning
Fine-tuned based on the pre-trained ViT model to effectively utilize large-scale pre-trained knowledge.

Model Capabilities

Diabetic retinopathy classification
Medical image analysis
Fundus image recognition

Use Cases

Medical diagnosis assistance
Diabetic retinopathy screening
Automatically analyze fundus images to identify retinal lesions caused by diabetes.
Accuracy of 74.31% on the validation set.
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