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An open and practical guide to Edge Vision.

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Edge Vision 👁️ | A Practical Guide

A practical guide for real-world and efficient computer vision applications for resource-constrained devices with industry standards in mind.

New to Edge AI?

Table of Contents

Introduction

The goal of this guide is to provide resources for building, optimizing, and deploying Computer Vision applications at the edge, through hands-on examples including practical notebooks and real-world use cases across key industries.

Key Concepts

Industry Blueprints

  • Autonomous Systems
  • Healthcare & Medical Imaging*
  • Retail & Consumer Analytics
  • Security & Surveillance
  • Agriculture & Precision Farming
  • Manufacturing & Quality Control
  • Smart Cities & Urban Planning

Edge Optimization Lab: techniques and tools for maximizing performance and efficiency of vision models on edge hardware

  • Model Quantization
  • Pruning Techniques
  • Federated Learning
  • Compiler Targets
  • Hardware-Specific Optimization

Production Pipelines: guides and templates for robust, scalable edge vision AI operations

  • CI/CD for Edge
  • Monitoring (Drift Detection, Edge Metrics Dashboard)
  • OTA Updates
  • Edge Security (Secure Boot, Data Encryption, Threat Detection, Privacy-Preserving vision, Adversarial Robustness, Device Hardening, Compliance)

Reference Architectures: blueprints for edge vision hardware and system design

  • Microphone Array Setups
  • Edge Server Specs
  • IoT Connectivity
  • Edge-Cloud Hybrid Models

Integration

  • Notebooks (hands-on deep dives)
  • Companion Resources
  • Industry-Specific Stardards

Project Structure

├── 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
│       ├── 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/afondiel/edge-vision.git
  1. Navigate to the industry blueprint or topic you're interested in.
  2. Follow the step-by-step guides to implement and deploy edge 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.

Resources

Books:

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