Explore the essential evaluation metrics—Accuracy, Precision, and Recall—for Custom Vision models. Learn how these metrics shape and define the performance of your vision models.
Table of Contents
Question
What are the three metrics that help to evaluate Custom vision model performance?
A. Accuracy
B. Number of Points
C. Precision
D. Recall
E. Mean Absolute Error (MAE)
F. Average Precision (AP)
Answer
C. Precision
D. Recall
F. Average Precision (AP)
Explanation
Custom vision is one of the Computer Vision tasks. Custom vision service helps create your own computer vision model. There are three main performance metrics for the Custom vision models: Precision, Recall, and Average Precision (AP).
Precision defines the percentage of the class predictions that the model makes correct. For example, If the model predicts that ten images are bananas, and there are actually only seven bananas, the model precision is 70%.
Recall defines the percentage of the class identification that the model makes correct. For example, if there are ten apple images, and the model identifies only eight, the model recall is 80%.
Average Precision (AP) is the combined metrics of both Precision and Recall.
Option A is incorrect. Accuracy is a Classification model metric, but it is not used for Custom vision models performance assessments.
Option B is incorrect. Number of Points is a Clustering model metric and is not used for Custom vision models performance assessments.
Option E is incorrect. Mean Absolute Error (MAE) is a Regression model metric and is not used for Custom vision models performance assessments.
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