This repository is a simple Readme describing all AI for STM32 projects.
| Project | Description |
|---|---|
| stm32ai-modelzoo-services | AI Model Zoo Services (training, quantization, evaluation scripts...) for STM32 devices |
| stm32ai-modelzoo | Storage for the AI Model Zoo for STM32 devices |
| HuggingFace-STMicroelectronics | Hugging Face space containing the models card and a space for the model zoo app |
| Project | Description |
|---|---|
| STM32N6 AI | STM32N6 AI ecosystem with application examples |
| Project | Description |
|---|---|
| meta-st-x-linux-ai | OpenEmbedded meta layer to install AI frameworks, tools and application samples for the STM32MPU series |
| meta-st-x-linux-isp | OpenEmbedded meta layer to install ISP tools and applications for the STM32MP2x series |
| Project | Description |
|---|---|
| x-cube-isp | This is an STM32 expansion software to enable ISP tuning capabilities |
| x-cube-webcam | This is an STM32 expansion software to manage USB Video device Class (UVC). It also provides UVC camera application examples |
| Project | Description |
|---|---|
| x-cube-ai | X-CUBE-AI - AI expansion pack for STM32CubeMX |
| ST Edge AI Developer Cloud | ST Edge AI Developer Cloud - online optimization platform |
| NanoEdge AI Studio | NanoEdge AI Studio - automated machine learning tool |
| STM32 ISP IQtune | STM32 ISP IQTune - comprehensive STM32 ISP tuning softwar |
| Project | Description |
|---|---|
| stm32ai-datalogger | GenericDataLogger for AI is a project composed of tools that format and log data with ease, especially between a STM32 and a computer |
| stm32ai-nanoedge-vibration-monitoring | STM32 Application for vibration monitoring with NanoEdge AI Studio |
| stm32ai-nanoedge-datalogger | STM32 Application for datalogging feature with NanoEdge AI Studio |
| Project | Description |
|---|---|
| stm32ai-nota | This repository contains Jupyter notebooks that demonstrate how to use Netspresso to prune pre-trained deep learning models from STM32AI Model Zoo and fine-tune them for your specific use case. Learn how to reduce the size of your models without sacrificing accuracy and customize them for your own applications |
| stm32ai-tao | Nvidia TAO (Train, Adapt, Optimize) with STM32Cube.AI Developer Cloud |
| STEdgeAI Ultralytics fork | Fork of the Ultralitics repository that implements STEdgeAI models |
| Project | Description |
|---|---|
| stm32ai-wiki | Application examples and resources that demonstrate Artificial Neural Networks running on STM32 microcontrollers and microprocessors. It illustrates and supports the STM32 AI Wiki articles |
| stm32ai-perf | MLPerf™ Tiny Deep Learning Benchmarks for STM32 devices |
For communication and support, please refer to:
- ST Support Center for any defect
- ST Community Forum forum
