Discover how Azure AI Vision uses confidence scores to evaluate the probability of correctly detecting objects in an image. Learn more about this essential metric.
Table of Contents
Question
When detecting common objects in an image, what metric does Azure AI Vision use to indicate the probability computed by the model for predicted objects?
A. Learning rate
B). Bounding boxes
C. Regularization term
D. Confidence score
Answer
D. Confidence score
Explanation
Azure AI Vision uses confidence scores to represent the probability that the model has calculated for each predicted object. This value ranges from 0 to 1, with 1 indicating high confidence (highly likely to be the correct object) and 0 indicating low confidence (unlikely to be the correct object).
Bounding boxes do not represent the model’s confidence in the prediction. These are rectangular areas drawn around detected objects in the image, indicating their location and size.
The learning rate is not used to indicate the probability of a specific prediction at inference time. This is a hyperparameter used in machine learning algorithms to control how quickly the model updates its weights during training.
The regularization term is not related to the confidence level of individual predictions. This is a technique used in machine learning to prevent overfitting and improve the model’s generalization ability.
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