AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is object detection, and how does it differ from image classification?
⚠ Common exam trap
Many candidates confuse the terms 'classification' and 'detection' by thinking detection only identifies objects without localization, or they assume object detection is a subset of classification—when in fact detection includes both identification and localization.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Object detection identifies and locates multiple objects with bounding boxes; image classification labels the whole image
Object detection goes beyond image classification by not only identifying what objects are present in an image but also localizing each object with a bounding box. Image classification assigns a single label to the entire image, whereas object detection can handle multiple objects of different classes simultaneously. This makes object detection suitable for tasks like counting objects or tracking their positions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Object detection identifies what is in an image; image classification also identifies where objects are located
Why it's wrong here
This option reverses the two computer vision tasks. Object detection outputs both class labels and bounding-box coordinates for each object, so it identifies what is present and where it is located. Image classification, by contrast, assigns a single label to the entire image and provides no spatial localization, so the claim that classification also identifies where objects are located is incorrect.
- ✓
Object detection identifies and locates multiple objects with bounding boxes; image classification labels the whole image
Why this is correct
This option is correct because it captures the core difference between the tasks. Object detection returns multiple predictions, each consisting of a class label and a bounding box that localizes the object, allowing a single image to contain many detected objects of different categories. Image classification is a global decision that maps the whole image to one label from a fixed set, with no attempt to locate or separate individual objects.
- ✗
Object detection and image classification are the same task
Why it's wrong here
These are distinct tasks, not the same. Image classification reduces an entire image to a single categorical label using pooled global features, while object detection must locate each object instance and predict a bounding box around it, often recognizing multiple objects from different classes in one image. Confusing them overlooks the spatial localization component, which requires different model architectures and evaluation metrics such as mAP instead of classification accuracy.
- ✗
Object detection is used only for face recognition
Why it's wrong here
This option incorrectly narrows object detection to a single application. Face detection is just one specialized use; object detection is a general technique that can be trained on any labeled dataset to find cars, pedestrians, tumors, defects, animals, or any other visually definable class. The same underlying architecture that detects faces can be retrained to detect arbitrary object categories, so this option describes a limited case, not a defining constraint.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
Key term
Image classification
Image classification is the process of teaching a computer to look at a picture and decide what category or label best describes the main object or scene in that picture.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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