Image classification on an ARM Cortex M Microcontroller

版本 1.0.0 (13.7 MB) 作者: MathWorks Fixed Point Team
Prototyping and Deploying a neural network for image classification using MNIST data on an ARM Cortex M Microcontroller
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更新时间 2019/3/18

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In this example, we have a Simulink model based on the shallow network with five layers described in Loren’s blog below for image classification using MNIST data:
https://blogs.mathworks.com/loren/2015/08/04/artificial-neural-networks-for-beginners/
We have three versions of the model – in double precision, single precision and a fixed-point version. These models can then be tested using live data from an Arduino Due using the Simulink® Support Package for Arduino® Hardware and deployed to the Arduino board as a standalone application.
The single precision and fixed-point versions were generated using Fixed-Point Designer as described in the links below. The fixed-point model uses no more than 16 bits and the accuracy of the model is above 94%.
https://www.youtube.com/watch?v=sxSodI0pwPw
https://www.youtube.com/watch?v=zX44UvyLeAc
https://www.youtube.com/watch?v=nkZAB7LIRXI&t=12s

引用格式

MathWorks Fixed Point Team (2024). Image classification on an ARM Cortex M Microcontroller (https://www.mathworks.com/matlabcentral/fileexchange/68426-image-classification-on-an-arm-cortex-m-microcontroller), MATLAB Central File Exchange. 检索时间: .

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版本 已发布 发行说明
1.0.0