Run an image classification model on STM32L4 Discovery
Introduction
Prepare environment
Train a CIFAR-10 image classifier for STM32L4 Discovery
Deploy the image classification NN model on STM32
Run the image classification NN model on STM32
Next Steps

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Review frequently asked questions

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These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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How do I launch the Jupyter Notebook used for training?
Open Anaconda Prompt and run jupyter notebook. In the browser, navigate to the extracted project files and open lab.ipynb.
How do I know a notebook cell has finished running?
Check the indicator to the left of the cell: In[] before running, In[*] while running, and In[N] (a number) when complete.
Which STM32Cube.AI version should I select in STM32CubeMX?
Select X-CUBE-AI 7.0.0. Later versions might not connect successfully.
What should I do before running the Python test tool against the board?
Activate the Conda environment with conda activate ml_lab and install opencv-python, protobuf==3.20, and tqdm==4.50.2. Then, go to the working folder’s Misc directory.
How do I start the test tool, and what if the board is not detected?
From the Misc folder, run python ui_python_ai_runner.py. If the board isn’t detected, press the black button on the board to reset and try again.

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