Evaluating custom vision model metrics is essential for understanding how well your trained model performs and identifying areas for improvement. Azure Custom Vision provides several key metrics that help you assess model quality before deployment.
The primary metrics include Precision, Recall, an…Evaluating custom vision model metrics is essential for understanding how well your trained model performs and identifying areas for improvement. Azure Custom Vision provides several key metrics that help you assess model quality before deployment.
The primary metrics include Precision, Recall, and Average Precision (AP). Precision measures the percentage of correct positive predictions among all positive predictions made. A high precision indicates that when your model identifies an object or classifies an image, it is usually correct. Recall measures the percentage of actual positive cases that were correctly identified. High recall means your model successfully finds most relevant instances in your dataset.
Average Precision combines precision and recall into a single score, calculated as the area under the precision-recall curve. This metric provides a balanced view of model performance across different confidence thresholds. For object detection models, Mean Average Precision (mAP) averages the AP across all classes.
The probability threshold setting affects these metrics significantly. Adjusting this threshold changes the confidence level required for predictions. A higher threshold increases precision but may reduce recall, while a lower threshold captures more instances but might include false positives.
Per-tag performance analysis allows you to identify which classes perform well and which need additional training data or refinement. Tags with low precision or recall indicate areas requiring more diverse or representative training images.
The iteration comparison feature enables you to track improvements across training sessions. By comparing metrics between iterations, you can determine whether adding new images or adjusting training parameters improved model accuracy.
Best practices include ensuring balanced datasets across all tags, using diverse training images representing real-world conditions, and testing with images separate from your training set. Regular evaluation helps maintain model quality as requirements evolve, ensuring your computer vision solution delivers reliable results in production environments.
Evaluating Custom Vision Model Metrics
Why It Is Important
Evaluating custom vision model metrics is crucial for understanding how well your trained model performs before deploying it to production. These metrics help you identify weaknesses in your model, determine if you need more training data, and ensure your solution meets business requirements. For the AI-102 exam, understanding these metrics demonstrates your ability to build reliable AI solutions.
What Are Custom Vision Metrics?
Custom Vision provides several key performance metrics:
Precision - Measures the percentage of correct positive predictions out of all positive predictions made. High precision means fewer false positives.
Recall - Measures the percentage of actual positives that were correctly identified. High recall means fewer false negatives.
Average Precision (AP) - Combines precision and recall into a single metric, representing the area under the precision-recall curve.
Mean Average Precision (mAP) - The average of AP values across all classes, providing an overall model performance score.
Probability Threshold - The confidence level above which predictions are considered positive.
How It Works
When you train a Custom Vision model, Azure automatically splits your data into training and validation sets. After training completes, the portal displays metrics calculated against the validation set. You can:
1. View per-tag performance to identify which classes perform well or poorly 2. Adjust the probability threshold using the slider to see how it affects precision and recall 3. Analyze the confusion matrix for classification models 4. Review individual predictions to understand model behavior
The relationship between precision and recall is typically inverse - increasing the threshold improves precision but reduces recall.
Exam Tips: Answering Questions on Evaluating Custom Vision Metrics
• Know the trade-offs: Questions often test whether you understand that increasing threshold improves precision at the cost of recall
• Understand use cases: Medical diagnoses prioritize recall (catching all cases), while spam detection might prioritize precision (avoiding false positives)
• Remember mAP: This is the primary metric for object detection model overall performance
• Identify solutions: Low metrics suggest adding more diverse training images, balancing class distribution, or improving image quality
• Threshold adjustments: Know that threshold changes do not require retraining - they filter predictions at inference time
• Per-iteration comparison: Each training iteration produces metrics allowing you to compare model versions before publishing