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
• Industry Blueprints
• Edge Optimization Lab
• Production Pipelines
• Reference Architectures
• Getting Started
• Contributing
• License

Edge AI Engineering

Fundamental concepts and practices for Edge AI:

• Introduction to Edge AI
• Edge AI Architectures
• Model Optimization Techniques
• Hardware Acceleration
• Edge Deployment Strategies
• Real-Time Processing
• Privacy and Security
• Edge AI Frameworks
• Benchmarking and Performance

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 file for details on how to submit improvements.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

References

Deep Dives:

• Core: Edge AI concepts and resources
• Blog: The Next AI Frontier is at the Edge
• Computer Vision Notes
• Computer Vision Course - HF (@johko)

Books:

• Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems (Vijay Janapa Reddi)

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