Diabetic - Retinopathy - 224 - Procnorm - Vit Open - source Model: Empowering the Classification and Diagnosis of Diabetic Retinopathy

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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%.

Image Classification

Transformers

Open Source License:Apache-2.0 #Medical image analysis #Diabetic retinopathy #ViT fine-tuning model

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.

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8619	1.0	50	0.8907	0.7143
0.7831	2.0	100	0.7858	0.7393
0.6906	3.0	150	0.7412	0.7531
0.5934	4.0	200	0.7528	0.7393
0.5276	5.0	250	0.7578	0.7431

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Diabetic Retinopathy 224 Procnorm Vit

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Diabetic Retinopathy 224 Procnorm ViT

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