V

Vit Large Patch32 384 Melanoma

Developed by UnipaPolitoUnimore
A melanoma image classification model fine-tuned based on Google's ViT-Large model, achieving 82.73% accuracy on the evaluation set
Downloads 100
Release Time : 11/16/2022

Model Overview

This model is a medical image classification model based on the Vision Transformer architecture, specifically fine-tuned for melanoma detection tasks.

Model Features

High-precision Classification
Achieves 82.73% accuracy on melanoma classification tasks
Transformer-based Architecture
Utilizes advanced Vision Transformer architecture, suitable for processing medical images
Mini-batch Training Optimization
Implements effective training process through gradient accumulation techniques

Model Capabilities

Skin lesion image classification
Medical image analysis
Melanoma detection

Use Cases

Medical Diagnosis
Skin Cancer Screening
Used to assist doctors in preliminary screening of skin lesions
82.73% accuracy
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase