# meta-st-stm32mpu-ai

**Repository Path**: mirrors_STMicroelectronics/meta-st-stm32mpu-ai

## Basic Information

- **Project Name**: meta-st-stm32mpu-ai
- **Description**: OpenEmbedded meta layer to install AI frameworks and tools for the STM32MPU series
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-18
- **Last Updated**: 2025-11-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">
    <img width="720" src="https://raw.githubusercontent.com/STMicroelectronics/meta-st-stm32mpu-ai/master/x-linux-ai-logo.png">
</p>

X-LINUX-AI version: v6.1.1

X-LINUX-AI is a free of charge open-source software package dedicated to AI.
It is a complete ecosystem that allow developers working with OpenSTLinux to create AI-based application very easily.
* **All-in-one AI solutions** for the entire STM32MPU series
* **Pre-integrated** into Linux distribution based on ST environment
* Include **AI frameworks** to execute Neural Network models
* Include **AI model benchmark** application tools for MPU
* **Easy** application **prototyping** using Python language and AI frameworks Python API
* **C++ API** for embedded high-performance applications
* Optimized **open-source solutions** provided with source codes that allow extensive **code reuse** and **time savings**

# meta-st-x-linux-ai
X-LINUX-AI OpenEmbedded meta layer to be integrated into OpenSTLinux distribution.
It contains recipes for AI frameworks, tools and application examples for STM32MPx series

## Compatibility
The X-LINUX-AI OpenSTLinux Expansion Package v6.1.1 is compatible with the Yocto Project™ build system Scarthgap.
It is validated over the OpenSTLinux Distribution v6.1.1 https://wiki.st.com/stm32mpu on STM32MP2x and STM32MP1x series.

## Versioning
Since its release v5.0.0, the major versioning of the X-LINUX-AI OpenSTLinux Expansion Package is aligned on the major versioning of the OpenSTLinux Distribution. This prevents painful backward compatibility attempts and makes dependencies straightforward.
The X-LINUX-AI generic versioning v**x**.**y**.**z** is built as follows:
* **x**: major version matching the OpenSTLinux Distribution major version. Each new major version is incompatible with previous OpenSTLinux Distribution versions.
* **y**: minor version, which is changed when new functionalities are added to the X-LINUX-AI OpenSTLinux Expansion Package in a backward compatible manner.
* **z**: patch version to introduce bug fixes. A patch version is implemented in a backward compatible manner.

## Available frameworks and tools within the meta-layer
[X-LINUX-AI v6.1.1 expansion package](https://wiki.st.com/stm32mpu/wiki/Category:X-LINUX-AI_expansion_package):
* AI Frameworks:
  * STAI_MPU Unified API based on OpenVX™(STM32MP25x and STM32MP23x only), TensorFlow™ Lite, and ONNX Runtime™ compatible with all STM32MPU series
  * TIM-VX™ 1.22.6 (STM32MP25x and STM32MP23x only)
  * TensorFlow™ Lite 2.18.0 with default delegate activated for CPU execution and VX-delegate External delegate to address STM32MP2 NPU
  * ONNX Runtime 1.19.2 with default execution engine activated for CPU execution and VSINPU Execution provider to address STM32MP2 NPU
  * ONNX 1.16.2 python version for On Device Learning
  * Pytorch 2.3.1 python version for On Device Learning

* Out of the box applications:
  * Image classification :
    * C++ / Python™ example using STAI_MPU Unified API based on the MobileNet v1 and v2 quantized models
  * Object detection :
    * C++ / Python™ example using STAI_MPU Unified API based on the SSD MobileNet v1 and v2 quantized models
  * Human pose estimation :
    * Python™ example using STAI_MPU Unified API based on YoloV8n pose quantized model
  * Semantic segmentation :
    * Python™ example using STAI_MPU Unified API based on DeepLabV3 quantized model
  * Face recognition:
    * C++ example using STAI_MPU unified API based on the BlazeFace and FaceNet quantized models
  * People Tracking and Heatmap
    * Python™ example using STAI_MPU Unified API based on the yolov8n quantized model.
  * On Device Learning for Object detection
    * Python™ example using STAI_MPU Unified API based on SSD MobileNet v2 as student and RT-DETR transformer model as teacher.
    * Step by step Jupyterlab™ notebook available for this application.
  * Note: applications are based on Gstreamer 1.22.x, GTK 3.x, OpenCV 4.9.x, Pillow, Python 3

* Utilities:
  * X-LINUX-AI tool suite provides tools for software information, AI packages management and Neural Network models benchmarking.
  * Support wide range of image sensors for ST MPU including IMX335 (5MP) for MP2 with use of internal ISP, GC2145 and OV5640 for STM32MP13x
  * Support for the OpenSTLinux AI package repository allowing the installation of a prebuilt package using apt-*

* Host tools:
  * ST Edge AI tool for NBG generation
  * X-LINUX-AI SDK add-on extending the OpenSTLinux SDK with AI functionality to develop and build an AI application easily. The X-LINUX-AI SDK add-on supports all the above frameworks. It is available from the X-LINUX-AI product page

## Further information on how to install and how to use X-LINUX-AI Starter package
<https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_Starter_package>

## Further information on how to install and how to use X-LINUX-AI Developer package
<https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_Developer_package>

## Further information on how to install and how to use X-LINUX-AI Distribution package
<https://wiki.st.com/stm32mpu/wiki/X-LINUX-AI_Distribution_package>

## Further information on On Device Learning feature
<https://wiki.st.com/stm32mpu/wiki/Category:AI_-_On_Device_Learning>

## Application samples
<https://wiki.st.com/stm32mpu/wiki/Category:AI_-_Application_examples>