Character recognition using LeNet-5

Version 1.0.1 (370 ko) par Amir Ebrahimi
A deep model (LeNet-5) trained on the MNIST dataset is used for character recognition.
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Mise à jour 6 mai 2021

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The LeNet-5 model implemented in this project has 3 convolutional layers and 2 fully-connected layers. It has 62,000 training parameters, and the image input size is 32*32. This model achieved 98.48% accuracy on the MNIST test set after training on its train set. MNIST is a dataset of handwritten digits with 70,000 centred fixed-size grey-scale images. More details about the dataset are available in:

http://yann.lecun.com/exdb/mnist

Run the GUI and select your image.

Citation pour cette source

Ebrahimi, Amir, et al. “Convolutional Neural Networks for Alzheimer’s Disease Detection on MRI Images.” Journal of Medical Imaging, vol. 8, no. 02, SPIE-Intl Soc Optical Eng, Apr. 2021, doi:10.1117/1.jmi.8.2.024503.

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Compatibilité avec les versions de MATLAB
Créé avec R2020b
Compatible avec les versions R2019b et ultérieures
Plateformes compatibles
Windows macOS Linux
Remerciements

Inspiré par : Pre-trained 2D LeNet-5

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Version Publié le Notes de version
1.0.1

The relevant paper is published.
1.0.0