https://github.com/msadeqsirjani/adaptive_edge_ai
Optimizing deep learning models for edge devices through intelligent compression and knowledge distillation. Achieve up to 90% model size reduction while maintaining performance, enabling efficient AI deployment on resource-constrained devices.
https://github.com/msadeqsirjani/adaptive_edge_ai
deep-learning edge-ai knowledge-distillation model-compression onnx-optimization pytorch
Last synced: about 2 months ago
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Optimizing deep learning models for edge devices through intelligent compression and knowledge distillation. Achieve up to 90% model size reduction while maintaining performance, enabling efficient AI deployment on resource-constrained devices.
- Host: GitHub
- URL: https://github.com/msadeqsirjani/adaptive_edge_ai
- Owner: msadeqsirjani
- License: mit
- Created: 2024-11-26T18:04:50.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2024-11-26T18:14:27.000Z (6 months ago)
- Last Synced: 2025-02-03T21:47:22.308Z (3 months ago)
- Topics: deep-learning, edge-ai, knowledge-distillation, model-compression, onnx-optimization, pytorch
- Language: Python
- Homepage:
- Size: 395 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Adaptive Edge AI 🤖
## 📋 Overview
Adaptive Edge AI is a project focused on optimizing and deploying deep learning models for edge devices through model compression and knowledge distillation techniques. This solution enables efficient AI model deployment on resource-constrained devices while maintaining high performance.
## 🌟 Key Features
- 🔄 Model Compression
- 📚 Knowledge Distillation
- 📱 Edge Device Optimization
- 📊 Adaptive Performance Scaling
- 🚀 ONNX Export Support
## 🏗️ Architecture
```mermaid
graph LR
A[Teacher Model] --> B[Knowledge Distillation]
B --> C[Student Model]
C --> D[Model Compression]
D --> E[Edge Deployment]
```
## 🛠️ Installation
1. Clone the repository
```bash
git clone https://github.com/msadeqsirjani/adaptive_edge_ai.git
```
2. Create and activate virtual environment
```bash
python -m venv .venv
source .venv/bin/activate # Linux/Mac
# or
.venv\Scripts\activate # Windows
```
3. Install dependencies
```bash
pip install -r requirements.txt
```
## 📊 Performance Metrics
| Model | Size | Accuracy | Inference Time |
|-------|------|----------|----------------|
| Teacher | 500MB | 95% | 100ms |
| Student | 50MB | 92% | 20ms |
| Compressed | 10MB | 90% | 5ms |
## 💻 Usage
### Training the Teacher Model
```bash
python main.py --mode train_teacher --data_path data/
```
### Knowledge Distillation
```bash
python main.py --mode distill --teacher_model best_teacher_model.pth
```
### Model Compression
```bash
python main.py --mode compress --model student_model.pth
```
## 📁 Project Structure
```bash
adaptive_edge_ai/
├── data/ # Dataset directory (gitignored)
├── src/
│ ├── models/ # Model architectures
│ ├── optimization/ # Compression algorithms
│ ├── training/ # Training utilities
│ └── utils/ # Helper functions
├── outputs/ # Saved models & results
├── tests/ # Unit tests
├── requirements.txt # Dependencies
└── main.py # Entry point
```
## 📈 Results
Our compressed models achieve:
- 📉 90% size reduction
- ⚡ 20x faster inference
- 💪 Minimal accuracy loss
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 📬 Contact
- `Mohammad Sadegh Sirjani` - [@msadeqsirjani](https://twitter.com/msadeqsirjani)
- Email - `[email protected]`
## 🙏 Acknowledgments
- Thanks to relevant papers or projects
- Special thanks to contributors
- Inspired by related work
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