Dive into Computer Vision and Build Your First Model with Azure Custom Vision

In this guide, you will learn how to build your first computer vision model using Azure Custom Vision, a powerful tool for training image classification and object detection models

Introduction to Computer Vision and Custom Vision

Computer Vision is a branch of artificial intelligence (AI) that helps machines “SEE” and interpret images, just as humans do.

It’s used for things like recognizing and classifying objects, detecting motion, and even analyzing medical images. AI, especially?Deep Learning, has propelled advancements in computer vision by enabling systems to learn from vast amounts of visual data.

Azure Custom Vision, part of Microsoft’s AI offerings, makes it easy for developers to train a model using their own images, specifically for image classification and object detection. Custom Vision offers control over how images are classified or detected, which is ideal for businesses or developers who need tailored image recognition solutions.

Applications of Computer Vision Today

In Manufacturing

1. AI Vision Inspection: Used for quality control, remote monitoring, and automation, improving production accuracy.
2. Productivity Analytics: Tracks workplace activities, helping improve time management and efficiency.
3. Visual Equipment Inspection: Ensures safety protocols like?PPE detection?(e.g., mask and helmet detection).
4. Quality Management: Automates visual inspections for better accuracy and scalability across factories.
5. Skill Training: Optimizes assembly lines by assessing worker performance and preventing unsafe actions.

In Healthcare

1. Cancer Detection: AI helps detect cancers through?image recognition?in MRI and X-ray scans.
2. COVID-19 Diagnosis: Models like?COVID-Net?analyze chest X-rays for detecting COVID-19.
3. Cell Classification: Helps classify cells for quicker disease diagnosis, like colon cancer.
4. Movement Analysis:?Pose estimation?tracks movement to diagnose conditions like strokes.
5. Mask Detection: Ensures mask usage with?face recognition, especially in public spaces during COVID-19.
6. Tumor Detection:?Deep learning?detects brain tumors from medical imaging with high accuracy.
7. Disease Progression: Monitors abnormal respiratory patterns in COVID-19 patients using?depth cameras.
8. Rehabilitation:?Vision-based systems?help patients with physical therapy at home.
9. Medical Skill Training:?AR simulations?assess and improve the skills of medical students and professionals.

let’s dive in to the depths? !

What is Custom Vision?

Azure Custom Vision is a tool that allows you to build image classification and object detection models using your own images. It’s part of Azure’s suite of AI services, which means you can integrate it with other Azure AI features.

• Image Classification: Identify which category an image belongs to.
• Object Detection: Locate and identify multiple objects in a single image.

How Does It Work?

At the core of Custom Vision is a?Convolutional Neural Network (CNN), a type of neural network specifically designed to process visual data. CNNs detect patterns in pixels to classify or recognize objects. Training involves feeding the model lots of labeled images so it can learn what each label represents.

Getting Started with Azure Custom Vision

Step 1-

1. Create a Resource in Azure

1. Sign in /Sign Up to the Azure Portal: Log in to?Azure?Portal.

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Create a Custom Vision Resource:

• Navigate to?Create a Resource?(from the menu or homepage).
• Search for?Custom Vision?and select it.
• Click?Create.
• Configure the following:?
• Click?Review + Create, then?Create.

2. Proceed to Custom Vision

1. Visit the?Custom Vision Portal:?Custom?Vision.
2. Sign in with your Azure account.
3. When prompted to create a project:?

Step 2: Upload and Tag Images

For your model to learn, it needs data. Upload a set of labeled images relevant to the model you’re building. For instance:

• If you’re training it to classify dog breeds, label each image with the correct breed.
• For object detection, mark each object within the images with bounding boxes.

Step 3: Train Your Model

Once your images are uploaded and labeled:

1. Click on?Train?in the Custom Vision portal.
2. Select training options, and Azure Custom Vision will start analyzing your images.
3. The model will learn to distinguish between the images based on your label.

Step 4: Evaluate the Model

When the training is complete, review your model’s performance using metrics like?accuracy?and?precision. These metrics tell you how well the model performs on test data and help you decide if it’s ready to be deployed or needs more training.

Step 5: Deploy Your Model

[ Only Iterations trained using “compact” domain can be exported ]

If your model performs well:

1. Go to the?Prediction?section and select Endpoint ?to use the Prediction API

Deploy the model to an Azure App Service or as a REST API, so you can call it from any application.

Ethical Considerations in Computer Vision

As we develop computer vision models, we should be mindful of the ethical implications:

• Privacy: Protect the privacy of individuals in images.
• Bias: Avoid biased datasets, which can lead to unfair predictions.
• Surveillance: Ensure that applications comply with privacy laws and regulations.

Now that you have the foundational knowledge of Azure Custom Vision, it’s time to put it into practice!

Start by building your own image classification or object detection model. Whether it’s classifying different types of flowers or detecting objects in everyday scenes, Azure Custom Vision provides you with the tools to bring your AI vision project to life.

Don’t wait begin your journey with Azure Custom Vision today and see what you can create!

Authored By: Ayodhya Weerabahu