Build and run an image classification NN model on an STM32L4 Discovery board
Introduction
Prepare environment
Build an image classification NN model trained with the CIFAR-10 dataset
Deploy the image classification NN model on STM32
Run the image classification NN model on STM32
Next Steps
Build and run an image classification NN model on an STM32L4 Discovery board
Who is this for?
This is an advanced topic for embedded software developers interested in building neural network models for microcontrollers.
What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Build a convolution neural network(CNN) model for image classification.
- Run the CNN model on an STM32 B-L475E-IOT01A2 board using STM Cube AI
Prerequisites
Before starting, you'll need the following prerequisites:
- Familiarity with ML concepts
- Familiarity with C programming on microcontrollers
- STM32 B-L475E-IOT01A2 board
Summary
This summary was 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.
Frequently asked questions
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.
jupyter notebook. In the browser, navigate to the extracted project files and open lab.ipynb.In[] before running, In[*] while running, and In[N] (a number) when complete.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.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.