Choosing between classification and object detection
5 minutes
5 Questions
When implementing computer vision solutions in Azure, understanding when to use image classification versus object detection is crucial for project success.
**Image Classification** assigns one or more labels to an entire image. Use this approach when you need to categorize images into predefined …When implementing computer vision solutions in Azure, understanding when to use image classification versus object detection is crucial for project success.
**Image Classification** assigns one or more labels to an entire image. Use this approach when you need to categorize images into predefined categories and the location of objects within the image is not important. For example, classifying whether a photo contains a cat or dog, or determining if a product image shows acceptable or defective items. Azure Custom Vision and Azure AI Vision both support classification tasks. Classification is computationally lighter and faster to train.
**Object Detection** identifies specific objects within an image and provides their locations using bounding boxes. Choose this method when you need to know not just what objects are present, but also where they are located and how many instances exist. Examples include counting vehicles in traffic footage, detecting people in security cameras, or identifying multiple products on store shelves. Azure Custom Vision offers object detection capabilities with the ability to train custom models.
**Key Decision Factors:**
1. **Location Requirements**: If you need spatial information about where objects appear, select object detection. For simple yes/no or category answers, classification suffices.
2. **Multiple Objects**: When images contain multiple items of interest that need individual identification, object detection is appropriate.
3. **Performance Considerations**: Classification models are typically faster and require less training data than object detection models.
4. **Use Case Complexity**: Inventory management, autonomous vehicles, and quality inspection often require object detection. Content moderation and image organization typically work well with classification.
5. **Training Data**: Object detection requires annotated bounding boxes around objects, which takes more effort to prepare than simple image labels for classification.
Choosing correctly between these approaches ensures optimal accuracy, performance, and resource utilization in your Azure AI solution.
Choosing Between Classification and Object Detection
Why This Topic Is Important
Understanding when to use image classification versus object detection is fundamental for the AI-102 exam. Microsoft expects you to recommend the appropriate Computer Vision solution based on business requirements. Choosing the wrong approach can lead to inefficient solutions, increased costs, and failed implementations.
What Is Image Classification?
Image classification assigns a single label or multiple labels to an entire image. It answers the question: What is in this image?
Examples include: - Determining if an image contains a cat or dog - Identifying whether a product photo shows damage - Categorizing photos by scene type (beach, mountain, city)
What Is Object Detection?
Object detection identifies multiple objects within an image and provides their locations using bounding boxes. It answers: What objects are present and where are they located?
Examples include: - Counting vehicles in a parking lot - Locating products on store shelves - Identifying and locating defects in manufacturing
How to Choose Between Them
Choose Classification when: - You need to categorize the entire image - Location of objects is not required - You only need to know IF something is present - The image contains one primary subject
Choose Object Detection when: - You need to locate specific objects within images - Multiple objects of the same or different types exist - You need counts of objects - Bounding box coordinates are required - Position information matters for the business scenario
Key Differences Summary
| Aspect | Classification | Object Detection | |--------|---------------|------------------| | Output | Labels/tags | Labels + bounding boxes | | Location | No | Yes | | Multiple objects | Limited | Full support | | Complexity | Lower | Higher | | Training data | Labeled images | Labeled images with regions |
Exam Tips: Answering Questions on Choosing Between Classification and Object Detection
1. Look for location keywords: If the question mentions needing to know where something is, finding positions, or drawing bounding boxes, select object detection.
2. Watch for counting scenarios: Questions about counting items (people in a room, cars in an image) typically require object detection.
3. Simple yes/no presence: If the scenario only needs to determine whether something exists in an image, classification is usually sufficient.
4. Consider the business context: Retail inventory and safety compliance scenarios often need object detection, while quality pass/fail scenarios may only need classification.
5. Cost and complexity: If a simpler solution works, Microsoft generally prefers the less complex option. Choose classification when location data is unnecessary.
6. Custom Vision keywords: Remember that Azure Custom Vision supports both classification and object detection projects - the exam may ask which project type to create.
7. Training data requirements: Object detection requires region annotations during training, while classification only needs image-level labels.