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Resnet Tiny Mnist
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Resnet Tiny Mnist

Developed by fxmarty
A small ResNet model for MNIST handwritten digit recognition, achieving an accuracy of 0.985 on the validation set.
Image Classification
Transformers
Open Source License:Gpl-3.0#MNIST High Accuracy#Lightweight Image Classification#Handwritten Digit Recognition
Downloads 1,181
Release Time : 4/27/2022

Model Overview

This model is based on the ResNet architecture, specifically designed for the MNIST handwritten digit recognition task, featuring high accuracy and lightweight characteristics.

Model Features

High Accuracy
Achieves an accuracy of 0.985 on the MNIST validation set.
Lightweight Design
As a small ResNet model, it has a low parameter count and computational requirements.
Specialized Optimization
The architecture is specifically optimized for the MNIST dataset.

Model Capabilities

Handwritten Digit Recognition
Image Classification

Use Cases

Education
Handwritten Digit Recognition Teaching
Used in computer vision courses to demonstrate basic image classification capabilities.
Students can quickly understand the application of deep learning in simple image recognition tasks.
Prototype Development
OCR System Prototype
Serves as the digit recognition component for more complex OCR systems.
Provides foundational digit recognition capabilities for complete OCR systems.
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