README.md

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title: README
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_**Innovating with edge AI on STM32 and Hugging Face.**_

STMicroelectronics is a global semiconductor leader pushing artificial intelligence down to the most resource-constrained microcontrollers. With the **STM32 AI ecosystem**, ST provides an end-to-end pipeline — from pre-trained models in the **Model Zoo** to bare-metal optimized deployment — enabling embedded developers to build intelligent applications without deep ML expertise.
Models are optimized, quantized and validated to run directly on ST Neural-ART but also Cortex-M4, M7, M85 and M33 cores.

---
## End-to-End AI Pipeline
```
+----------------------------+
|          EXPLORE           |
+----------------------------+
|     STM32 AI Model Zoo     |
+----------------------------+
              |
              v
+----------------------------+
|           TRAIN            |
+----------------------------+
|  STM32 AI Model Zoo        |
|  Services                  |
+----------------------------+
              |
              v
+----------------------------+
|     OPTIMIZE / QUANTIZE    |
+----------------------------+
|  STM32 AI Model Zoo        |
|  Services                  |
+----------------------------+
              |
              v
+----------------------------+
|     EVALUATE / PREDICT     |
+----------------------------+
|  STM32 AI Model Zoo        |
|  Services                  |
+----------------------------+
              |
              v
+----------------------------+
|         BENCHMARK          |
+----------------------------+
|  STM32Cube AI Studio       |
|  STM32 Developer Cloud     |
+----------------------------+
              |
              v
+----------------------------+
|          CONVERT           |
+----------------------------+
|  STM32Cube AI Studio       |
|  ST Edge AI Core           |
+----------------------------+
              |
              v
+----------------------------+
|           DEPLOY           |
+----------------------------+
|  STM32Cube ecosystem       |
|  (tools, middleware, BSP)  |
+----------------------------+
```

This diagram summarizes the typical STM32 edge AI workflow from model discovery to on-device deployment:

1. **Explore**: Start from the STM32 AI Model Zoo to browse available architectures, pretrained checkpoints, and application examples.
2. **Train**: Use Model Zoo Services to retrain an existing model or build a task-specific pipeline on your own dataset.
3. **Optimize / Quantize**: Reduce model size and compute cost so the network fits embedded constraints while preserving the best possible accuracy.
4. **Evaluate / Predict**: Validate accuracy, inspect predictions, and compare tradeoffs before moving to hardware execution.
5. **Benchmark**: Measure latency, memory footprint, and target compatibility with STM32Cube AI Studio and STM32 Developer Cloud.
6. **Convert**: Transform the trained model into STM32-ready artifacts using STM32Cube AI Studio and ST Edge AI Core.
7. **Deploy**: Integrate the generated code into the STM32Cube ecosystem, including firmware, middleware, and board support components.

In short, the flow shows how a model moves from selection and training to optimization, hardware validation, and final integration on STM32 devices.

## Build, Optimize and Deploy AI/ML on STM32

- **STM32 AI Model Zoo**: A GitHub collection of reference machine learning models optimized for STM32 microcontrollers.
- **Application-Oriented Model Library**: A large set of models ready for re-training across multiple use cases.
- **Pre-trained Models Across Frameworks**: Reference models variants available for PyTorch, TensorFlow, and ONNX workflows.
- **End-to-End Scripts & Services**: Tools to retrain, quantize, evaluate, and benchmark models on custom datasets, plus autogenerated application code examples via [stm32ai-modelzoo-services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main)
- **Fast Deployment + Full Customization**: Use pretrained categories for quick deployment, or apply transfer learning / full training from scratch on your own data.
- **Reference Performance Metrics**: Results provided on STM32 MCU, NPU, and MPU targets for both float and quantized models.
- **Expanded Framework Support**: Comprehensive PyTorch support complements TensorFlow and ONNX in unified end-to-end workflows (train, evaluate, quantize, benchmark, deploy).

---

##  Key Tools & Ecosystem

- **STEdgeAI Core**: Converts trained neural networks into optimized C code for STM32.
- **STM32 AI Model Zoo services**: This repository provide scripts and workflows to ease end-to-end AI model training and integration on ST devices. They offer a valuable foundation to add AI capabilities to STM32-based projects.
- **STM32 AI Model Zoo** The repository with a of reference pre-trained machine learning models optimized for STM32 microcontrollers generated thanks to the STM32 AI Model Zoo services.
- **Integration with Popular Frameworks**:
  - TensorFlow / Keras
  - PyTorch (via ONNX export)
  - ONNX Runtime pipelines

---

## Links
- **[STM32 AI Model Zoo services](https://github.com/STMicroelectronics/stm32ai-modelzoo-services/tree/main)**
- **[STEdgeAI Core](https://www.st.com/en/development-tools/stedgeai-core.html)**
- **[STM32 Developer Cloud](https://stm32ai-cs.st.com/home)**
- **[STM32AI Model Zoo](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main)**
- **[STM32AI Cube Studio](https://www.st.com/en/development-tools/stedgeai-cubeai.html)**
---

## 🤝 Contact & Contributions

- For technical questions: [ST EdgeAI Community](https://community.st.com/t5/edge-ai/bd-p/edge-ai)
- For issues or feature requests, use the **Issues** or **Discussions** tabs in the respective repos.
- Contributions and feedback on models, pipelines, and docs are welcome.