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Vit Base Mnist

Developed by farleyknight-org-username
An image classification model fine-tuned on the MNIST dataset based on the ViT architecture, achieving an accuracy of 99.49%
Downloads 1,770
Release Time : 8/21/2022

Model Overview

This model is an image classification model fine-tuned on the MNIST handwritten digit dataset based on Google's ViT-base architecture, specifically designed to recognize handwritten digits from 0 to 9.

Model Features

High accuracy
Achieves 99.49% accuracy on the MNIST test set
Based on ViT architecture
Uses the Vision Transformer (ViT) architecture, which has advantages over traditional CNNs in image classification tasks
Lightweight fine-tuning
Fine-tuned based on a pre-trained model, resulting in high training efficiency

Model Capabilities

Handwritten digit recognition
Image classification
Digit recognition

Use Cases

Education
Handwritten digit recognition system
Used for automatically grading handwritten digit assignments or exams
Recognition accuracy of 99.49%
Finance
Check digit recognition
Automatically recognizes handwritten amount digits on checks
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