supervisionai/azure-iot-edge-cv-model-samples

An end-to-end IoT Edge solution performing object detection (YOLOv3)

Bringing Computer Vision models to the Edge with Azure IoT Edge samples [UPDATED NOV 2019]

Overview

This repository hosts the code samples for the Bringing Computer Vision models to the Edge with Azure IoT Edge - A guide for developers and data scientists.

The objective of this guide is to walk you through an end-to-end AI object detection on a Raspberry Pi 3 via a series of modules that are entirely customizable with ease. This guide is designed for developers as well as data scientists who wish to easily put their AI models in practice on edge devices without focusing too much on the deployment.

[UPDATED NOV] This new version features a fully operational MLOps implementation of the above IoT solution. Check out MLOps/ as well as the associated section in the updated guide

From the training of the YOLOv3 object detection to the deployment on the Raspberry Pi 3, you will have a wide overview of how to build an IoT device performing computer vision models.

Contents

• Azure ML Training: contains a notebook to train the state-of-the-art object detection YOLOv3 based on this Keras implementation repository with Azure Machine Learning.
• IoT: contains the IoT solution presented in the guide as well as a notebook to quickly set up an Azure IoT Hub via az commands.
• MLOps: contains an end-to-end MLOps implementation of the IoT solution above from the training of YOLOv3 on the VOC dataset to the deployment in a dev-qa-prod environment with Azure DevOps.

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.