https://github.com/afondiel/edge-vision
Practical Edge Vision Handbook.
https://github.com/afondiel/edge-vision
computer-vision edge-ai edge-computer-vision edge-computing edge-devices edge-vision embedded-ai embedded-vision real-world-applications
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Practical Edge Vision Handbook.
- Host: GitHub
- URL: https://github.com/afondiel/edge-vision
- Owner: afondiel
- License: mit
- Created: 2025-02-06T15:31:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-16T19:28:49.000Z (about 1 year ago)
- Last Synced: 2025-03-16T20:33:08.292Z (about 1 year ago)
- Topics: computer-vision, edge-ai, edge-computer-vision, edge-computing, edge-devices, edge-vision, embedded-ai, embedded-vision, real-world-applications
- Homepage:
- Size: 51.8 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 Vision :eye: | 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?
- 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.
- Then read this: [The Next AI Frontier is at the Edge](https://afondiel.github.io/posts/the-next-ai-frontier-is-at-the-edge/)
- Related work: [Edge Language](https://github.com/afondiel/edge-language) | [Edge Audio](https://github.com/afondiel/edge-audio)
## Table of Contents
- [Introduction](#introduction)
- [Project Structure](#project-structure)
- [Getting Started](#getting-started)
- [Contributing](#contributing)
- [License](#license)
- [Resources](#resources)
## 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
> [!IMPORTANT]
> This project uses a submodule `edge-ai-engineering` located in `lab/edge-ai-engineering`.
> Please initialize submodules after cloning the repository: `git submodule update --init --recursive`
1. Clone this repository:
```bash
git clone https://github.com/afondiel/edge-vision.git
```
2. Explore the [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering) for foundational knowledge.
3. Dive into [Industry Blueprints](./lab/industry-blueprints/) for hands-on, sector-specific language AI guides.
4. Use the [Edge Optimization Lab](./lab/optimization/) and [Production Pipeline](./lab/production-pipelines/) for deployment and scaling.
## 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.
## Resources
- [Computer Vision Notes](https://github.com/afondiel/computer-science-notebook/tree/master/core/ai-ml/computer-vision-notes)
- [The Hugging Face Course on Computer Vision](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)