Azure Custom Vision in 8 steps

Introduction

This article provides a step-by-step guide for building a Custom Vision project on the Azure platform, how to set up an Azure account, create a Custom Vision project, upload and tag photographs, train and test your Custom Vision model, and then use the model to recognize items in recent images. This tutorial offers a fundamental overview of how to start a Custom Vision project on Azure.

Step 1. Register for an account on the Azure Website

On the Azure website, you can register for an account if you don't already have one. Click the "Start free" button after entering the Azure portal. You will be asked to enter basic information, including your name, email address, and password, to create an account.

Step 2. Create a Custom Vision Project

You can create a Custom Vision project once your Azure account has been created. Enter the Azure portal after logging in, then choose "Create a resource" from the left-hand menu. Next, perform a search for "Custom Vision" and choose it from the list of results.

Step 3. Create a Custom Vision Project

After choosing "Custom Vision," you will be directed to a page where you can create your project. You must first decide on a name for your project and the resource group you intend to use. A logical container for resources that are deployed to Azure is the resource group. You can make a new one if you don't already have one. Next, decide which pricing tier you want to employ. The free and standard tiers are available. You can train up to 5,000 images per project and up to two projects at the free tier. You can create unlimited projects and train up to one million images with the basic tier. 

Step 4. Images can be uploaded to a custom vision project.

After creating your project, you can begin adding images. To do this, open your Custom Vision project and click the "Upload Images" button under the "Images" tab. After that, you can select and upload images from your computer. For the best results, it is advised that you use high-quality pictures with good lighting and little background noise.

Step 5. Tag your Images

Images can be uploaded to a custom vision project.

After creating your project, you can begin adding images. To do this, open your Custom Vision project and click the "Upload Images" button under the "Images" tab. After that, you can select and upload images from your computer. For the best results, it is advised that you use high-quality pictures with good lighting and little background noise.

Step 6. Train your Custom Vision Model

Your Custom Vision model can be trained after your images have been tagged. This involves analyzing your images using machine learning to discover the objects they contain. Select the "Training" tab in your Custom Vision project and press "Train" to start training your model. As a result, the training process will begin. Depending on the number of images you've uploaded and the complexity of your model, this process could take a few minutes to several hours.

Step 7. Test your Custom Vision Model

You can test your Custom Vision model to determine its accuracy after it has been trained. To do this, go to your project's "Quick Test" tab and upload an image. Then, your model will make a prediction about what is shown in the image and indicate its level of confidence. The "Performance" tab can also be used to view your model's general performance.

Step 8. Use your Custom Vision Model

You can use your Custom Vision model to identify objects in fresh images once you're satisfied with it. The Custom Vision API, which enables you to incorporate your model into other applications, must be used in order to accomplish this. The Azure documentation contains more details on how to use the Custom Vision API.

Summary

The article outlines eight steps to create a Custom Vision project using Azure. The first step is to create an Azure account, followed by creating a Custom Vision project, adding images, tagging the images, training the Custom Vision model, testing the model, and finally using the Custom Vision model. The article provides basic guidance on each of these steps.

That's it! With these eight steps, you should be able to access Azure.