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README.md

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 ---
 title: README
-emoji: 🏃
-colorFrom: green
-colorTo: green
+emoji: ⚡
+colorFrom: gray
+colorTo: gray
 sdk: static
 pinned: false
 ---
+<style>
+  .st-header-banner {
+    width: 100%;
+    height: 120px;
+    margin: 0 0 12px 0;
+    padding: 0 24px;
+    box-sizing: border-box;
+    background: #03234B;
+    display: flex;
+    align-items: center;
+    justify-content: flex-start;
+  }
+  .st-logo-right {
+    height: 72px;
+    width: auto;
+    object-fit: contain;
+  }
+  img.rotating-content {
+    width: 300px;
+    height: 150px;
+    object-fit:cover;
+    object-position:center;
+  }
+  .icon {
+    width: 1.5em;
+    height: 1.5em;
+    vertical-align: -.7em;
+    padding: .25em .5em .25em .25em;
+  }
+  ul.social {
+    list-style: none;
+    padding-left: 0;
+  }
+  ul.social li {
+    padding-left: .5em;
+    display: flex;
+  }
+  .architectureImage {
+    border: 0.5px solid black;
+  }
+</style>
+<div class="st-header-banner">
+  <img src="assets/ST_logo_2024_white.png" alt="ST Logo" class="st-logo-right"/>
+</div>
 
-Edit this `README.md` markdown file to author your organization card.
+
+
+_**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     │
+│                            │
+└────────────────────────────┘
+              │
+              ▼
+┌────────────────────────────┐
+│           TRAIN            │
+├────────────────────────────┤
+│       STM32 AI Model       |
+|        Zoo Services        │
+└────────────────────────────┘
+              │
+              ▼
+┌────────────────────────────┐
+│     OPTIMIZE / QUANTIZE    │
+├────────────────────────────┤
+│       STM32 AI Model       |
+|        Zoo Services        │
+└────────────────────────────┘
+              │
+              ▼
+┌────────────────────────────┐
+│     EVALUATE / PREDICT     │
+├────────────────────────────┤
+│       STM32 AI Model       |
+|        Zoo Services        │
+└────────────────────────────┘
+              │
+              ▼
+┌────────────────────────────┐
+│         BENCHMARK          │
+├────────────────────────────┤
+│     STM32Cube AI Studio    │
+|     STM32 Developer Cloud  |
+└────────────────────────────┘
+              │
+              ▼
+┌────────────────────────────┐
+│          CONVERT           │
+├────────────────────────────┤
+│     STM32Cube AI Studio    │
+│      ST Edge AI Core       │
+└────────────────────────────┘
+              │
+              ▼
+┌────────────────────────────┐
+│           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.