Make sure that your AI vision dev kit is plugged to power via USB and connected to an external screen via HDMI. Also please verify that your device 3 LEDs are displayed a solid green light.

Use this account to login to Azure, see and deploy Azure resources:

• user: [email protected] where is the number on yoru AI vision dev kit (between 01 and 24)
• password: Provided by the lab at your station
• subscription: MS IoT Labs - Vision AI DevKit
• resource group: msiotlabs-user-visionkit where is the number on yoru AI vision dev kit (between 01 and 24)

Your device has already been set up as an Azure IoT Edge device, connected to an IoT Hub. This will enable you to remotely manage it. Let's first verify that your setup looks correct:

• Let's connect to Azure via the portal to see your Edge device in your IoT Hub:
  • In a browser, login to http://portal.azure.com
  • Click on All resources from the left menu and search for your IoT Hub under your resource group
  • Click on your IoT Hub and then on IoT Edge section from the left menu
  • Verify that you can see your visionkit device is listed

We will now deploy an sample AI model to the camera. For that, we will use the AI Dev Kit get started module from the IoT Edge marketplace.

• Go to this link and click on the AI Vision Dev Kit Get Started Module (preview) and Create.

• Choose your subscription, your IoT Hub, find your device named visionkit and click on Next

• We'll use a preview version of IoT Edge for this device, click on Configure advanced Edge runtime settings and do the following updates:

  • replace the edgeHub URI from mcr.microsoft.com/azureiotedge-hub:1.0 to mcr.microsoft.com/azureiotedge-hub:1.0.7-rc2
  • replace the edgeAgent URI from mcr.microsoft.com/azureiotedge-agent:1.0 to mcr.microsoft.com/azureiotedge-agent:1.0.7-rc2
  • finally add the environment variable OptimizeForPerformance set to False for the edgeHub.

• Confirm the deployment by clicking on Next twice then Submit

After a few minutes (time to download the module), you should now see objects being detected by the camera when looking at its HDMI output!

We'll build our own AI model to detect when someone is wearing an hard hat. You have an hard hat on your desk which well be used to validate your model built with Custom Vision.

• Login to the Azure Custom Vision Service (Preview) at https://www.customvision.ai/.

• Create a new project, use these recommended settings:

  • Give it a name like Simulated HardHat Detector
  • Project Type - [Classification]
  • Use the existing resource group
  • Classification Type - [Multiclass (Single tag per image)]
  • Domain - [General(compact)]
  • Export Capabilites - Vision AI Dev Kit

Some training images have already been collected for you for the hard hat use case.

Download them and upload them to your Custom Vision project:

• Downlaod the .zip file at this location: https://1drv.ms/u/s!AkzLzaBpSgoMo9hXX4NPjd8QrfhQLA?e=M3ehCL
• Uncompress it
• Upload images to custom vision per tag (HardHat/NoHardHat) and tag them appropriately them during upload. Upload all pictures names similarly (like HardHat) at the same time.

To train it with your new training data, go to your Custom Vision project and click on Train.

To export it, select tab Performances and Export button. Choose the Vision AI Dev Kit option to download your new custom model and finally uncompress them.

To deploy your custom model, we will first store your model in a publicly accessible location and then update the configuration of the Get Started module to use this model instead of the default one. We will use a cloud blob store to store the model but a public OneDrive link would work just as well.

We'll start by creating a new storage account and then upload your model to it.

• Login to the http://portal.azure.com
• Search for Storage and select the Storage accounts service
• Use your existing subscription and resource group
• Give it a unique name, upper case characters are not allowed
• Select the West-US 2 region
• Click on Review+Create (other default options shoudl be correct)
• Wait until until provisioning is complete and navigate to yoru new storage account
• From the overview tab, click on Blobs service
• Add a new container, give it a name like hardhatmodel and make sure to select Container (anonymous read access for containers and blobs) for the Public access level
• Click on the container just created, Click on the Upload button and select your files from the custom vision service and Upload
• Copy the three URLs of these files by clicking on each of them and copying their URL to the clipboard

• Login to the http://portal.azure.com and go to your ioT Hub resource
• Click on IoT Edge tab and then on your camera device named visionkit
• Click on the AIVisionDevKitGetStartedModule module name and click on Module Identity Twin
• Update the three desired properties (model, label, vam config) to map to your new URLs and hit Save

After a few minutes, your device should now be running your custom model!

• Put your hard hat on and smile at the camera !
• Verify that the camera picks it up and correctly classify you as wearing the hard hat

As a next step, you could re-use the same training images but build an object detection model instead of an image classifier. For that, please use this environment of custom vision and use its labeling tool.

To learn more about this AI Dev Kit, visit https://azure.github.io/Vision-AI-DevKit-Pages/docs/Get_Started/

Before leaving, please:

• delete your download folder on your laptop

• delete your custom vision project

• delete your storage account

• log out from all websites

Thank you!