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Lenet
MNIST handwritten digit classification model based on LeNet-5 architecture, officially provided by MindSpore
Downloads 19
Release Time : 3/2/2022
Model Overview
This is a convolutional neural network model trained on the MNIST dataset using the LeNet-5 architecture for handwritten digit recognition tasks.
Model Features
Classic Architecture
Adopts the classic LeNet-5 convolutional neural network architecture, which is concise and efficient.
Lightweight Model
The model has fewer parameters, making it suitable for deployment in resource-constrained environments.
High Accuracy
Achieves over 99% classification accuracy on the MNIST test set.
Model Capabilities
Handwritten Digit Recognition
Image Classification
Use Cases
Education
Handwritten Digit Recognition Teaching
Used for introductory deep learning education to demonstrate the basic principles of convolutional neural networks.
Industrial Applications
Postal Code Recognition
Can be used for automatic postal code recognition in mail sorting systems.
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