Create and deploy a vision AI model with CustomVision.AI

What you will do

• Create a trained vision AI model using Azure Custom Vision Service.
• Containerize and deploy the model to the DevKit.

What you will need to create and train a model

• A valid Azure account. Create an account for free.
• A set of images for use in training the classifier model.

What you will need to containerize and deploy the model

• A configured Visual Studio (VS) Code installation. Instructions
• Clone or download the contents of Vision AI Developer Kit GitHub.
• Docker installation and Docker extension for VS Code. Note: Do not login to Docker at this time.

Build a custom vision classifier

1. Using a browser, login to the Azure Custom Vision Service (Preview) at https://www.customvision.ai.

2. Create Custom Vision Training and Prediction resources in the in the Azure portal.

3. Follow these instructions for How to build a classifier with Custom Vision, using these recommended settings when creating a new project:

   • Project Type - [Classification]
   • Classification Type - [Multiclass (Single tag per image)]
   • Domain - [General(compact)]
   • ** Basic Platforms + Vision AI Dev Kit

Export the trained vision model

1. Download the trained models using the Export button in the Performance tab of the customvision.ai portal.

2. Select Vision AI Dev Kit platform

3. Export will provide you with model.dlc and labels.txt files that are needed for containerization and deployment.

Containerize and deploy your model

Build a Local Container Image

The modules\VisionSampleModule folder include:

• \app: source code used to detect objects by a deep learning model.
• \model: a deep learning model’s dlc file, its related labels.txt file and va-snpe-engine-library_config.json. The default model.dlc is a classfication model trained by Azure Custom Vision Service to detect “fork” and “scissors” two classes.
• Dockerfile.arm32v7 file: instructions used to build this module image.
• module.json file: config file for this module.

1. Overwrite deployment.template.json’s content by 01-visionsample-deployment.template.json file. It only includes one module: VisionSampleModule.

2. Update the .env file with the values for your container image name and container registry. Refer to Create a container registry for more detail about ACR settings.

     MODULE_NAME="Your container image name"
     REGISTRY_NAME="Your ACR name"
     REGISTRY_USER_NAME="Your ACR username"
     REGISTRY_PASSWORD="Your ACR password"
   

3. Sign in Azure Container Registry by entering the following command in the Visual Studio Code integrated terminal (replace , , and to your container registry values set in the **.env** file):

    docker login -u <REGISTRY_USER_NAME> -p <REGISTRY_PASSWORD> <REGISTRY_NAME>.azurecr.io  
   

4. Copy DLC file and its related labels.txt and va-snpe-engine-library_config.json to modules\VisionSampleModule\model folder.

5. Open modules\VisionSampleModule\module.json file and change version setting in tag property for creating a new version of the module image.

6. Right-clicking on deployment.template.json file and select [Build and Push IoT Edge Solution] command to generate a new deployment.json file in config folder, build a module image, and push the image to the specified ACR repository.

   Note: Some red warnings “/usr/bin/find: ‘/proc/XXX’: No such file or directory” and “debconf: delaying package configuration, since apt-utils is not installed” displayed during the building process can be ignored.

7. Right-clicking on config/deployment.json file, select [Create Deployment for Single Device], and choose the targeted IoT Edge device to deploy the container Image.