https://github.com/afondiel/edge-computer-vision
Practical Edge AI Vision Deployment Handbook.
https://github.com/afondiel/edge-computer-vision
computer-vision edge-ai edge-computer-vision edge-computing edge-devices edge-vision embedded-ai embedded-vision real-world-applications
Last synced: 8 months ago
JSON representation
Practical Edge AI Vision Deployment Handbook.
- Host: GitHub
- URL: https://github.com/afondiel/edge-computer-vision
- Owner: afondiel
- License: mit
- Created: 2025-02-06T15:31:17.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-14T22:41:09.000Z (8 months ago)
- Last Synced: 2025-02-14T23:27:02.139Z (8 months ago)
- Topics: computer-vision, edge-ai, edge-computer-vision, edge-computing, edge-devices, edge-vision, embedded-ai, embedded-vision, real-world-applications
- Homepage:
- Size: 42 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](./CONTRIBUTING.md)
# Edge Computer Vision: A Practical Guide
## Overview
This repository serves as a comprehensive, practical guide for deploying optimized computer vision models on edge devices across key industries.
## Motivation
The goal is to bridge the gap between theoretical computer science and real-world applications, with a focus on edge AI engineering.
### Key Features
- Fundamental concepts and practices for Edge AI
- Industry-specific blueprints for vision AI deployment
- Edge optimization techniques for various hardware targets
- Production-ready pipelines and best practices
- Practical case studies and hands-on projects
## Table of Contents
- [Edge AI Engineering](#edge-ai-engineering)
- [Industry Blueprints](#industry-blueprints)
- [Edge Optimization Lab](#edge-optimization-lab)
- [Production Pipelines](#production-pipelines)
- [Reference Architectures](#reference-architectures)
- [Getting Started](#getting-started)
- [Contributing](#contributing)
- [License](#license)
## Edge AI Engineering
Fundamental concepts and practices for Edge AI:
- [Introduction to Edge AI](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/introduction-to-edge-ai.md)
- [Edge AI Architectures](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/edge-ai-architectures.md)
- [Model Optimization Techniques](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/model-optimization-techniques.md)
- [Hardware Acceleration](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/hardware-acceleration.md)
- [Edge Deployment Strategies](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/edge-deployment-strategies.md)
- [Real-Time Processing](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/real-time-processing.md)
- [Privacy and Security](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/privacy-and-security.md)
- [Edge AI Frameworks](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/edge-ai-frameworks.md)
- [Benchmarking and Performance](https://github.com/afondiel/edge-ai-engineering/blob/main/docs/benchmarking-and-performance.md)
## Industry Blueprints
Practical implementation guides for:
- Autonomous Systems
- Medical Imaging
- Smart Retail
- Security & Surveillance
- Agriculture
- Manufacturing
- Smart Cities
## Edge Optimization Lab
Learn how to optimize models for edge deployment:
- Model Quantization
- Pruning Techniques
- Federated Learning
- Compiler Targets (TVM, ONNX Runtime)
## Production Pipelines
Guides for deploying and maintaining edge AI systems:
- CI/CD for Edge
- Monitoring and Drift Detection
- OTA Updates
## Reference Architectures
Hardware setups and specifications for various edge deployment scenarios.
## Project Structure
A focused resource for deploying optimized vision models on edge devices across key industries.
```
├── edge-ai-engineering/
│ ├── introduction-to-edge-ai.md
│ ├── edge-ai-architectures.md
│ ├── model-optimization-techniques.md
│ ├── hardware-acceleration.md
│ ├── edge-deployment-strategies.md
│ ├── real-time-processing.md
│ ├── privacy-and-security.md
│ ├── edge-ai-frameworks.md
│ └── benchmarking-and-performance.md
├── industry-blueprints/
│ ├── autonomous-systems/
│ │ ├── traffic-analysis-yolov8-tensorrt.md
│ │ ├── drone-navigation-lite.md
│ │ ├── pedestrian-tracking-edgetpu.md
│ │ └── vehicle-defect-detection-openvino.md
│ ├── healthcare-medical-imaging/
│ │ ├── xray-classification-tflite.md
│ │ ├── ultrasound-segmentation-ncnn.md
│ │ ├── mri-tumor-detection-onnx.md
│ │ └── remote-patient-monitoring-jetson.md
│ ├── retail-consumer-analytics/
│ │ ├── shelf-analytics-mmdetection.md
│ │ ├── checkout-automation.md
│ │ ├── customer-behavior-analysis-openvino.md
│ │ └── inventory-management-edge-tflite.md
│ ├── security-surveillance/
│ │ ├── perimeter-surveillance-yolo.md
│ │ ├── anomaly-detection-autoencoder.md
│ │ ├── facial-recognition-privacy-preserving.md
│ │ └── crowd-behavior-analysis-edge.md
│ ├── agriculture-precision-farming/
│ │ ├── crop-health-monitoring-multispectral.md
│ │ ├── yield-prediction-edge-ml.md
│ │ └── autonomous-harvesting-robotics.md
│ ├── manufacturing-quality-control/
│ │ ├── defect-detection-openvino.md
│ │ ├── robotic-picking-ort.md
│ │ └── predictive-maintenance-edge-analytics.md
│ └── smart-cities-urban-planning/
│ ├── traffic-flow-optimization-edge.md
│ ├── waste-management-vision-ai.md
│ └── energy-grid-monitoring-federated.md
├── edge-optimization-lab/
│ ├── model-quantization/
│ │ ├── post-training-int8.md
│ │ └── qat-pytorch.md
│ ├── pruning-techniques/
│ │ ├── magnitude-pruning.md
│ │ └── lottery-ticket-hypothesis.md
│ ├── federated-learning/
│ │ ├── privacy-preserving-cv.md
│ │ └── distributed-training.md
│ ├── compiler-targets/
│ │ ├── tvm-tutorial.md
│ │ └── onnx-runtime-guide.md
│ └── hardware-specific-optimization/
│ ├── nvidia-jetson-optimization.md
│ ├── intel-openvino-deployment.md
│ ├── raspberry-pi-edge-ai.md
│ └── microcontroller-tinyml.md
├── production-pipelines/
│ ├── ci-cd-for-edge.md
│ ├── monitoring/
│ │ ├── drift-detection.md
│ │ └── edge-metrics-dashboard.md
│ ├── ota-updates.md
│ └── edge-security/
│ ├── secure-boot-implementation.md
│ ├── data-encryption-edge.md
│ ├── threat-detection/
│ │ ├── perimeter-surveillance.md
│ │ └── anomaly-detection.md
│ ├── privacy-preserving-cv/
│ │ ├── federated-learning-techniques.md
│ │ └── differential-privacy.md
│ ├── model-security/
│ │ └── adversarial-robustness.md
│ ├── edge-device-hardening/
│ │ ├── secure-deployment.md
│ │ └── secure-communication.md
│ └── industry-compliance/
│ ├── regulatory-standards.md
│ └── ethical-ai-guidelines.md
├── reference-architectures/
│ ├── industrial-camera-setups.md
│ ├── edge-server-specs.md
│ ├── iot-connectivity.md
│ └── edge-cloud-hybrid-models.md
└── _integration/
├── cs-notebook-redirects.md
├── companion-resources.md
└── industry-specific-regulations.md
```
## Getting Started
1. Clone this repository:
```
git clone https://github.com/yourusername/computer-vision-practical-guide.git
```
2. Navigate to the industry blueprint or topic you're interested in.
3. Follow the step-by-step guides to implement and deploy edge AI vision solutions.
## Contributing
We welcome contributions! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) file for details on how to submit improvements.
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
## References
Deep Dives:
- Core: [Edge AI concepts and resources](https://github.com/afondiel/computer-science-notebook/tree/master/core/systems/edge-computing/edge-ai)
- Blog: [The Next AI Frontier is at the Edge](https://afondiel.github.io/posts/the-next-ai-frontier-is-at-the-edge/)
- [Computer Vision Notes](https://github.com/afondiel/computer-science-notebook/tree/master/core/ai-ml/computer-vision-notes)
- [Computer Vision Course - HF (@johko)](https://github.com/johko/computer-vision-course)
Books:
- [Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems (Vijay Janapa Reddi)](https://mlsysbook.ai/)
[Back to the Top](#table-of-contents)