Learn how to evaluate object detection models using precision, recall, and mean average precision (mAP) metrics. Understand what these metrics mean and how they are calculated.
You plan on using object detection. After you have trained your model, you want to assess the performance of the model. Which performance metrics are available for you to analyze?
Select all options that apply.
C. Mean average precision
D. Project ID
C. Mean average precision
At the end of the training process, the performance for the trained model is indicated by the following evaluation metrics: precision, recall, and mean average precision (mAP).
The correct answer to the question is A, B, and C. Let me explain why.
Object detection is a computer vision task that involves identifying and locating objects in an image or video. Object detection models can be evaluated using different performance metrics, depending on the application and the goal of the model.
One common metric for object detection is precision, which measures how accurate the model is in detecting objects. Precision is calculated as the ratio of true positives (TP) to the sum of true positives and false positives (FP). True positives are objects that are correctly detected by the model, while false positives are objects that are wrongly detected by the model. Precision can be interpreted as the probability that a detected object is actually an object of interest. A high precision means that the model has a low rate of false alarms.
Another common metric for object detection is recall, which measures how complete the model is in detecting objects. Recall is calculated as the ratio of true positives (TP) to the sum of true positives and false negatives (FN). False negatives are objects that are missed by the model. Recall can be interpreted as the probability that an object of interest is detected by the model. A high recall means that the model has a low rate of missing objects.
A third metric for object detection is mean average precision (mAP), which combines precision and recall into a single score. mAP is calculated by averaging the precision values at different recall levels across all object classes. mAP can be interpreted as the overall performance of the model in detecting objects of different types and sizes. A high mAP means that the model has a high accuracy and completeness in detecting objects.
The last option, Project ID, is not a performance metric for object detection. Project ID is a unique identifier for a custom vision project in Azure, which is used to train and deploy object detection models. Project ID is not related to the evaluation of the model’s performance.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.