Learn how to select the correct Azure AI model for image classification and object detection. Boost your AI-102 exam success with practical, expert insights.
Table of Contents
Question
Your organization, Xerigon Corporation, is developing an AI-powered application to assist in warehouse management. The application needs to process images captured from security cameras and perform two specific tasks:
Task 1: Identify whether a pallet in the image is empty or full.
Task 2: Detect and count individual boxes and locations on a pallet within a single image.
Which Azure AI model type should you choose for each task?
A. Use the object detection model for both Task 1 and Task 2.
B. Use the image classification model for both Task 1 and Task 2.
C. Use the object detection model for Task 1 and the image classification model for Task 2.
D. Use the image classification model for Task 1 and the object detection model for Task 2.
Answer
D. Use the image classification model for Task 1 and the object detection model for Task 2.
Explanation
In the given scenario, you would use the image classification model for Task 1 and the object detection model for Task 2. The image classification model is designed to analyze the overall content of an image and classify it into categories such as “empty pallet” or “full pallet.” This approach is efficient for making binary or categorical decisions based on the content of the entire image.
The object detection model is designed to identify and locate multiple objects within a single image, providing bounding boxes around each detected item. This model can count the number of boxes and determine their specific locations on the pallet.
You would not use the object detection model for Task 1 and the image classification model for Task 2. The object detection model can identify and locate objects. However, it is not optimized for simple classification tasks such as determining if a pallet is full or empty. Similarly, image classification is unsuitable for tasks requiring identifying and counting multiple objects within an image.
You would not use the image classification model for both Task 1 and Task 2 in the given scenario. Image classification models are effective for categorizing the entire image into predefined classes, but they are not capable of detecting and counting individual objects within an image. Therefore, while it is appropriate for Task 1, it is not suitable for Task 2 where specific object detection and counting are required.
You would not use the object detection model for both Task 1 and Task 2. The object detection model is capable of identifying and locating multiple objects within an image. However, it is not suitable for a task that involves simple binary classification, such as determining whether a pallet is empty or full.
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