{"id":26584330,"url":"https://github.com/afondiel/edge-vision","last_synced_at":"2026-02-27T20:41:14.730Z","repository":{"id":276283973,"uuid":"928405698","full_name":"afondiel/edge-vision","owner":"afondiel","description":"Practical Edge Vision 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Edge Vision :eye: | A Practical Guide\n\nA practical guide for real-world and efficient computer vision applications for resource-constrained devices with industry standards in mind.\n\n## New to Edge AI?  \n\n- Start with the [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering): a practical guide covering core concepts of the entire [Edge AI MLOps](https://docs.edgeimpulse.com/docs/concepts/edge-ai-fundamentals/what-is-edge-mlops) stack with industry blueprints.\n- Then read this: [The Next AI Frontier is at the Edge](https://afondiel.github.io/posts/the-next-ai-frontier-is-at-the-edge/)\n- Related work: [Edge Language](https://github.com/afondiel/edge-language) | [Edge Audio](https://github.com/afondiel/edge-audio)\n\n\n## Table of Contents\n- [Introduction](#introduction)\n- [Project Structure](#project-structure)\n- [Getting Started](#getting-started)\n- [Contributing](#contributing)\n- [License](#license)\n- [Resources](#resources)\n\n## Introduction\n\nThe 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.\n\n### Key Concepts\n\n**Industry Blueprints**\n- Autonomous Systems\n- Healthcare \u0026 Medical Imaging*\n- Retail \u0026 Consumer Analytics\n- Security \u0026 Surveillance\n- Agriculture \u0026 Precision Farming\n- Manufacturing \u0026 Quality Control\n- Smart Cities \u0026 Urban Planning\n\n**Edge Optimization Lab**: techniques and tools for maximizing performance and efficiency of vision models on edge hardware\n- Model Quantization\n- Pruning Techniques\n- Federated Learning\n- Compiler Targets\n- Hardware-Specific Optimization\n\n**Production Pipelines**: guides and templates for robust, scalable edge vision AI operations\n- CI/CD for Edge\n- Monitoring (Drift Detection, Edge Metrics Dashboard)\n- OTA Updates\n- Edge Security (Secure Boot, Data Encryption, Threat Detection, Privacy-Preserving vision, Adversarial Robustness, Device Hardening, Compliance)\n\n**Reference Architectures**: blueprints for edge vision hardware and system design\n- Microphone Array Setups\n- Edge Server Specs\n- IoT Connectivity\n- Edge-Cloud Hybrid Models\n\n**Integration**\n- Notebooks (hands-on deep dives)\n- Companion Resources\n- Industry-Specific Stardards\n\n## Project Structure\n\n```\n├── edge-ai-engineering/\n│   ├── introduction-to-edge-ai.md\n│   ├── edge-ai-architectures.md\n│   ├── model-optimization-techniques.md\n│   ├── hardware-acceleration.md\n│   ├── edge-deployment-strategies.md\n│   ├── real-time-processing.md\n│   ├── privacy-and-security.md\n│   ├── edge-ai-frameworks.md\n│   └── benchmarking-and-performance.md    \n├── industry-blueprints/\n│   ├── autonomous-systems/\n│   │   ├── traffic-analysis-yolov8-tensorrt.md     \n│   │   ├── drone-navigation-lite.md\n│   │   ├── pedestrian-tracking-edgetpu.md\n│   │   └── vehicle-defect-detection-openvino.md\n│   ├── healthcare-medical-imaging/\n│   │   ├── xray-classification-tflite.md            \n│   │   ├── ultrasound-segmentation-ncnn.md\n│   │   ├── mri-tumor-detection-onnx.md\n│   │   └── remote-patient-monitoring-jetson.md\n│   ├── retail-consumer-analytics/\n│   │   ├── shelf-analytics-mmdetection.md\n│   │   ├── checkout-automation.md\n│   │   ├── customer-behavior-analysis-openvino.md\n│   │   └── inventory-management-edge-tflite.md\n│   ├── security-surveillance/\n│   │   ├── perimeter-surveillance-yolo.md\n│   │   ├── anomaly-detection-autoencoder.md\n│   │   ├── facial-recognition-privacy-preserving.md\n│   │   └── crowd-behavior-analysis-edge.md\n│   ├── agriculture-precision-farming/\n│   │   ├── crop-health-monitoring-multispectral.md\n│   │   ├── yield-prediction-edge-ml.md\n│   │   └── autonomous-harvesting-robotics.md\n│   ├── manufacturing-quality-control/\n│   │   ├── defect-detection-openvino.md             \n│   │   ├── robotic-picking-ort.md\n│   │   └── predictive-maintenance-edge-analytics.md\n│   └── smart-cities-urban-planning/\n│       ├── traffic-flow-optimization-edge.md\n│       ├── waste-management-vision-ai.md\n│       └── energy-grid-monitoring-federated.md\n├── edge-optimization-lab/                         \n│   ├── model-quantization/\n│   │   ├── post-training-int8.md\n│   │   └── qat-pytorch.md\n│   ├── pruning-techniques/\n│   │   ├── magnitude-pruning.md\n│   │   └── lottery-ticket-hypothesis.md\n│   ├── federated-learning/\n│   │   ├── privacy-preserving-cv.md\n│   │   └── distributed-training.md\n│   ├── compiler-targets/\n│   │   ├── tvm-tutorial.md\n│   │   └── onnx-runtime-guide.md\n│   └── hardware-specific-optimization/\n│       ├── nvidia-jetson-optimization.md\n│       ├── raspberry-pi-edge-ai.md\n│       └── microcontroller-tinyml.md\n├── production-pipelines/                           \n│   ├── ci-cd-for-edge.md\n│   ├── monitoring/\n│   │   ├── drift-detection.md\n│   │   └── edge-metrics-dashboard.md\n│   ├── ota-updates.md\n│   └── edge-security/\n│       ├── secure-boot-implementation.md\n│       ├── data-encryption-edge.md\n│       ├── threat-detection/\n│       │   ├── perimeter-surveillance.md\n│       │   └── anomaly-detection.md\n│       ├── privacy-preserving-cv/\n│       │   ├── federated-learning-techniques.md\n│       │   └── differential-privacy.md\n│       ├── model-security/\n│       │   └── adversarial-robustness.md\n│       ├── edge-device-hardening/\n│       │   ├── secure-deployment.md\n│       │   └── secure-communication.md\n│       └── industry-compliance/\n│           ├── regulatory-standards.md\n│           └── ethical-ai-guidelines.md\n├── reference-architectures/\n│   ├── industrial-camera-setups.md\n│   ├── edge-server-specs.md\n│   ├── iot-connectivity.md\n│   └── edge-cloud-hybrid-models.md\n└── _integration/\n    ├── cs-notebook-redirects.md                   \n    ├── companion-resources.md\n    └── industry-specific-regulations.md\n```\n\n## Getting Started\n\n\u003e [!IMPORTANT]  \n\u003e This project uses a submodule `edge-ai-engineering` located in `lab/edge-ai-engineering`.\n\u003e Please initialize submodules after cloning the repository: `git submodule update --init --recursive`\n\n1. Clone this repository:\n```bash\ngit clone https://github.com/afondiel/edge-vision.git\n```\n2. Explore the [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering) for foundational knowledge.\n3. Dive into [Industry Blueprints](./lab/industry-blueprints/) for hands-on, sector-specific language AI guides.\n4. Use the [Edge Optimization Lab](./lab/optimization/) and [Production Pipeline](./lab/production-pipelines/) for deployment and scaling.\n\n## Contributing\n\nWe welcome contributions! Please see our [CONTRIBUTING.md](CONTRIBUTING.md) file for details on how to submit improvements.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.\n\n## Resources\n\n- [Computer Vision Notes](https://github.com/afondiel/computer-science-notebook/tree/master/core/ai-ml/computer-vision-notes)\n- [The Hugging Face Course on Computer Vision](https://github.com/johko/computer-vision-course)\n\nBooks:\n- [Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems (Vijay Janapa Reddi)](https://mlsysbook.ai/)\n\n[Back to the Top](#table-of-contents)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fafondiel%2Fedge-vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fafondiel%2Fedge-vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fafondiel%2Fedge-vision/lists"}