Getting Started with Azure Custom Vision

Step by Step Guide to Implementing Azure Custom Vision

The Azure Custom vision tool provides an extremely easy-to-use platform for getting started with image recognition. Its simplicity allows users to quickly understand and implement basic image recognition capabilities without needing deep technical knowledge or extensive datasets.

Here’s a step-by-step guide to creating and deploying a model using the Azure Custom Vision portal:

1. Create an Azure Resource:

• Ensure you have an Azure account and create a Custom Vision resource in the Azure portal.

2. Access the Custom Vision Portal:

• Navigate to the Custom Vision portal here.
• Sign in with your Azure credentials.

3. Set Up a Project:

• Create a new project, defining the domain and classification type (either image classification or object detection).

4. Upload and Tag Images:

• Upload your training images to the portal.
• Manually tag the objects in your images. Tags are essential as they inform the model of what to look for.
• At least 15 images should be tagged.

5. Train Your Model:

• Initiate the training process. You can choose between quick training for faster results or advanced training for potentially better accuracy by allocating more computing time.

6. Evaluate Model Performance:

• After training, review the model’s performance metrics such as precision, recall, and mean average precision (mAP). Adjust training data and parameters as necessary to improve performance.

7. Deploy and Test:

• Once satisfied with the model’s performance, deploy it. Use the provided REST API endpoints to integrate the model into your applications.
• Test the model using new images to ensure it performs well in real-world scenarios.