Make sure to have Python 3.10 installed. At the time TinyMaix, used for CNN, only supports Tensorflow <2.14, which is not supported on Python 3.12 or later.

Make sure to have the Unix port of MicroPython setup. On Windows you can use Windows Subsystem for Linux (WSL), or Docker.

Install the dependencies of this example:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

This will train a Convolutional Neural Network using Keras. The process will output .tmdl that is our built model.

python mnist_train.py

micropython -m mip install https://emlearn.github.io/emlearn-micropython/builds/master/x64_6.3/emlearn_cnn_int8.mpy

micropython mnist_cnn_run.py

The fastest and easiest to to install on your device is to use Viper IDE. This will install the library and the example code:

!Make sure you have it running successfully on host first.

Flash your device with a standard MicroPython firmware, from the MicroPython.org downloads page.

mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_cnn_int8.mpy

mpremote cp mnist_cnn_int8.tmdl :
mpremote cp -r test_data/ :
mpremote run mnist_cnn_run.py

This example requires hardware with an ESP32 chip, and an OV2640 camera module. It has been tested on Lilygo T-Camera Mic v1.6.

You need to build a custom ESP32 firmware, and add this C module for camera access: https://github.com/cnadler86/micropython-camera-API

mpremote run mnist_cnn_camera.py