Training custom image models in Azure involves using Azure Custom Vision service to create specialized image classification and object detection models tailored to your specific needs. This process allows you to build AI models that recognize images unique to your business without extensive machine…Training custom image models in Azure involves using Azure Custom Vision service to create specialized image classification and object detection models tailored to your specific needs. This process allows you to build AI models that recognize images unique to your business without extensive machine learning expertise.
To begin training custom image models, you first create a Custom Vision resource in the Azure portal, selecting either a training resource, prediction resource, or both. Next, you create a project and choose between image classification (assigning labels to entire images) or object detection (identifying and locating specific objects within images).
The training process requires uploading labeled images to your project. For classification, you assign tags to images representing different categories. For object detection, you draw bounding boxes around objects and label them. Azure recommends at least 50 images per tag for optimal results, though you can start with fewer for initial testing.
Once images are uploaded and tagged, you initiate training by clicking the Train button. Azure offers two training types: Quick Training for rapid iterations during development, and Advanced Training for production scenarios requiring higher accuracy. Advanced training allows you to specify training time in hours.
After training completes, you receive performance metrics including Precision, Recall, and Average Precision (AP). These metrics help evaluate model effectiveness. You can iterate by adding more images, adjusting tags, or removing poorly performing samples.
The trained model can be published to a prediction endpoint for consumption via REST API or SDK. Additionally, Custom Vision supports exporting models to various formats including TensorFlow, CoreML, ONNX, and Docker containers for edge deployment scenarios.
Best practices include using diverse images representing real-world conditions, maintaining balanced datasets across tags, and testing with images the model has never seen to ensure generalization capability.
Training Custom Image Models in Azure AI
Why Training Custom Image Models is Important
Training custom image models allows organizations to create specialized computer vision solutions tailored to their unique business needs. While Azure provides pre-built models for common scenarios, many real-world applications require recognizing specific objects, products, or visual patterns that generic models cannot identify. Custom models enable businesses to automate visual inspection, inventory management, brand detection, and countless other domain-specific tasks.
What are Custom Image Models?
Custom image models in Azure are machine learning models trained on your own labeled image data to perform specific visual recognition tasks. Azure provides two main services for this purpose:
1. Azure Custom Vision - A specialized service for building image classification and object detection models with minimal machine learning expertise required.
2. Azure Machine Learning - A more advanced platform for training sophisticated computer vision models with greater customization options.
How Custom Vision Training Works
The training process follows these key steps:
Step 1: Create a Custom Vision Project Choose between Classification (assigning labels to entire images) or Object Detection (identifying and locating objects within images). Select the appropriate domain such as General, Food, Landmarks, Retail, or Compact domains for edge deployment.
Step 2: Upload and Tag Images Upload training images and apply tags or labels. For classification, tag the entire image. For object detection, draw bounding boxes around objects and apply tags. A minimum of 15 images per tag is required, though 50 or more per tag produces better results.
Step 3: Train the Model Select Quick Training for faster iterations during development or Advanced Training for production-ready models with configurable training time. The service uses transfer learning to leverage pre-trained models.
Step 4: Evaluate Performance Review metrics including Precision (percentage of correct positive predictions), Recall (percentage of actual positives correctly identified), and Mean Average Precision (mAP) for object detection.
Step 5: Publish and Consume Publish the trained iteration to a prediction endpoint. Use the prediction API or export the model for offline use with compact domains.
Key Concepts for the Exam
Domains: Pre-configured model architectures optimized for specific scenarios. Compact domains allow model export for edge devices.
Iterations: Each training run creates a new iteration. Multiple iterations can exist, but only published iterations can be used for predictions.
Probability Threshold: The confidence level above which predictions are returned. Adjusting this affects precision and recall.
Smart Labeler: Uses previously trained models to suggest labels for new images, accelerating the labeling process.
Training Best Practices
- Include varied images showing objects in different conditions, angles, and lighting - Balance the number of images across tags to prevent bias - Include negative examples to reduce false positives - Use the suggested images feature to identify where the model needs improvement - Regularly retrain with new data to improve accuracy
Exam Tips: Answering Questions on Training Custom Image Models
Tip 1: Know the minimum requirements - at least 15 images per tag for classification, though questions often test whether you know that more images improve accuracy.
Tip 2: Understand when to use Classification versus Object Detection. Classification identifies what is in an image; Object Detection identifies what and where.
Tip 3: Remember that Compact domains are required for exporting models to run on edge devices like mobile phones or IoT devices.
Tip 4: Be familiar with evaluation metrics. Questions may ask how to improve precision versus recall - adding more diverse training data typically helps both.
Tip 5: Know the difference between Quick Training and Advanced Training. Quick is for rapid prototyping; Advanced allows specifying training duration for better results.
Tip 6: Understand that publishing an iteration is required before the model can be accessed through the prediction API.
Tip 7: When questions mention poor model performance, look for answers involving adding more varied training images or adjusting the probability threshold.
Tip 8: Remember that Custom Vision stores both training and prediction resources, and questions may test your understanding of how these resources are organized and billed.