Understanding Azure AI: A Detailed Guide to Computer Vision vs Custom Vision

 

Table of Contents:

1. Introduction to Azure Cognitive Services
2. Understanding Computer Vision
   • Key Features
   • Real-World Use Case: Automated Content Moderation
   • How to Create a Computer Vision Service with Azure CLI
3. Understanding Custom Vision
   • Key Features
   • Real-World Use Case: Defect Detection in Manufacturing
   • How to Create a Custom Vision Service with Azure CLI
4. Comparing Computer Vision and Custom Vision
5. Practical Use Cases of Both Services
   • Retail Inventory Management
   • Wildlife Conservation
6. Programmatically Creating Azure Cognitive Services Resources
   • C# Method Example
   • Generating Image Captions Automatically
7. Conclusion: Choosing the Right Vision Service for Your Project
8. Memory Techniques to Aid Understanding

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Introduction to Azure Cognitive Services:

Azure Cognitive Services provide an array of AI-powered APIs that developers can use to incorporate intelligent features into their applications. These services span a variety of capabilities, from text analysis to image recognition, with Computer Vision and Custom Vision being two of the most powerful image-processing tools.

In this blog, we will dive deep into these services, exploring their features, use cases, and how to create them using both Azure CLI and C#. We'll also apply memory techniques to help you retain this information efficiently.

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Understanding Computer Vision:

Computer Vision is a general-purpose, pre-built Azure service for image analysis. It offers powerful tools like object detection, image categorization, and optical character recognition (OCR) without needing to train any custom models.

Key Features:

• Image Analysis: Identifies objects, faces, and colors in images.
• Optical Character Recognition (OCR): Extracts printed or handwritten text from images.
• Face Recognition: Detects and analyzes facial attributes.
• Image Tagging: Automatically assigns descriptive tags to images.
• Image Categorization: Classifies images into predefined categories.

Real-World Use Case: Automated Content Moderation:

An e-commerce platform can leverage Computer Vision to automatically moderate user-uploaded images. The service can detect inappropriate content, such as nudity or violence, and help categorize product images to improve searchability.

How to Create a Computer Vision Service with Azure CLI:

1. Create a Resource Group:

   bash
   
   az group create --name myResourceGroup --location eastus
   

2. Create the Computer Vision Service:

   bash
   
   az cognitiveservices account create \
     --name MyComputerVisionService \
     --resource-group myResourceGroup \
     --kind ComputerVision \
     --sku F0 \
     --location eastus \
     --yes
   

3. Retrieve the API Key:

   bash
   
   az cognitiveservices account keys list \
     --name MyComputerVisionService \
     --resource-group myResourceGroup
   

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Understanding Custom Vision:

Custom Vision is designed for building models that are unique to your specific requirements. You can upload your own images, label them, and then train a custom image classification or object detection model.

Key Features:

• Custom Model Training: Train a model specific to your business needs.
• Object Detection: Identify and locate multiple objects in an image.
• Classification: Categorize images into relevant groups.
• Incremental Learning: Continuously improve your model by adding more labeled data over time.

Real-World Use Case: Defect Detection in Manufacturing:

A manufacturing company can use Custom Vision to detect defects in products as they move through an assembly line. By training the model with both good and defective product images, the system can automatically flag defective items in real-time.

How to Create a Custom Vision Service with Azure CLI:

Custom Vision requires both a Training and a Prediction resource.

1. Create the Custom Vision Training Resource:

   bash
   az cognitiveservices account create \
     --name MyCustomVisionTraining \
     --resource-group myResourceGroup \
     --kind CustomVision.Training \
     --sku F0 \
     --location eastus \
     --yes

2. Create the Custom Vision Prediction Resource:

   bash
   
   az cognitiveservices account create \
     --name MyCustomVisionPrediction \
     --resource-group myResourceGroup \
     --kind CustomVision.Prediction \
     --sku F0 \
     --location eastus \
     --yes
   

3. Retrieve the API Keys:

   • Training Resource:

     bash
     
     az cognitiveservices account keys list \
       --name MyCustomVisionTraining \
       --resource-group myResourceGroup
     

   • Prediction Resource:

     bash
     az cognitiveservices account keys list \
       --name MyCustomVisionPrediction \
       --resource-group myResourceGroup

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Comparing Computer Vision and Custom Vision:

Feature	Computer Vision	Custom Vision
Purpose	Pre-built image analysis	Custom models for specific tasks
Customization	No customization needed	Requires model training with labeled data
Flexibility	Limited to predefined tasks	Highly customizable for business-specific needs
Use Cases	Object detection, OCR, face recognition	Defect detection, product classification
Training Required	Not required	Required to achieve specific accuracy
Pricing	Free (F0) and paid tiers available	Free (F0) and paid tiers available

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Practical Use Cases of Both Services:

1. Retail Inventory Management with Computer Vision:

Retailers can use Computer Vision to automatically recognize and tag products in images, streamlining the inventory tracking process. This reduces manual labor and enhances store operations.

2. Wildlife Conservation with Custom Vision:

Conservationists can train a Custom Vision model to identify and classify wildlife species from camera trap footage. This allows for more accurate monitoring of endangered animals in the wild.

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Programmatically Creating Azure Cognitive Services Resources

You can also create Azure Cognitive Services resources programmatically using C#. Here's an example method for creating a service:

csharp

static void create_resource(CognitiveServicesManagementClient client, string resource_name, string kind, string account_tier, string location)
{
    CognitiveServicesAccount parameters = new CognitiveServicesAccount(
        null, null, kind, location, resource_name,
        new CognitiveServicesAccountProperties(), new Sku(account_tier)
    );

    var result = client.Accounts.Create(resource_group_name, account_tier, parameters);
}

Generating Image Captions Automatically:

To generate image captions, we need to use the Computer Vision service. The following C# code calls the method to create a free Azure resource in the West US region for image caption generation:

csharp

create_resource(
    client: cognitiveServicesManagementClient,
    resource_name: "MyComputerVisionService",
    kind: "ComputerVision",
    account_tier: "F0",
    location: "westus"
);

This example demonstrates how to programmatically create a Computer Vision service with the F0 (free) tier in the West US region.

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Conclusion: Choosing the Right Vision Service for Your Project:

When choosing between Computer Vision and Custom Vision, it's essential to understand the nature of your project. Computer Vision is a powerful, general-purpose tool for image analysis that works out of the box. On the other hand, Custom Vision allows for tailored models to fit your specific business needs, offering more flexibility but requiring training.

Both services offer free tiers, making them accessible for testing and small-scale applications. Understanding the differences between these tools will help you select the right service for your project, ensuring you build an efficient AI-powered solution.

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Memory Techniques to Aid Understanding:

Mnemonics:

• "Pre-Built vs. Custom": Computer Vision is for pre-built tasks, while Custom Vision lets you create custom models.
• "F0 for Free": Both services offer a free tier, denoted by F0.

Story-Based Memory Technique:

Imagine you're running two workshops:

• In the Computer Vision workshop, participants use pre-built kits (tools) to identify images, faces, and text without customizing the tools.
• In the Custom Vision workshop, participants bring their own materials (images) and use the tools to build something completely unique (a custom model).

By employing these memory techniques, you can better understand and remember the differences between the two services, making it easier to choose the one that fits your needs.

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I hope this blog helps you understand the distinctions between Computer Vision and Custom Vision, as well as how to programmatically create and utilize these services. Let me know if you need more examples or further clarification on any aspect!