mnist-digit-classification-2022-09-04 Open-source Model - Accurately Recognize Handwritten Digits with an Accuracy of 99.23%

Mnist Digit Classification 2022 09 04

Developed by farleyknight

This is a MNIST handwritten digit classification model based on the Vision Transformer (ViT) architecture, achieving 99.23% accuracy after fine-tuning on the MNIST dataset.

Image Classification

Transformers

Open Source License:Apache-2.0 #Handwritten digit recognition #High-precision classification #ViT fine-tuning

Downloads 740

Release Time : 9/4/2022

Model Overview

The model uses google/vit-base-patch16-224-in21k as the base model and is fine-tuned on the MNIST handwritten digit dataset, specifically designed for image classification tasks of digits 0-9.

Model Features

High accuracy

Achieves 99.23% classification accuracy on the MNIST test set

ViT-based architecture

Uses the Vision Transformer architecture, which may have better feature extraction capabilities compared to traditional CNNs

Lightweight fine-tuning

Fine-tuned on a pre-trained model with high training efficiency

Model Capabilities

Handwritten digit recognition

Image classification

Digit 0-9 classification

Use Cases

Education

Automated grading of handwritten digit assignments

Used in educational settings to automatically recognize students' handwritten digit answers

Recognition accuracy of 99.23%

Finance

Check digit recognition

Recognize handwritten amount digits on checks

Property	Details
Model Type	Fine - tuned version of google/vit-base-patch16-224-in21k for image classification
Training Data	mnist

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Mnist Digit Classification 2022 09 04

Model Overview

Model Features

Model Capabilities

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