AI-102 Implement computer vision solutions Practice Question
You are using Azure AI Custom Vision to detect defects in fabric rolls. After training an object detection model, you notice that it often misses small tears. You have a large dataset of labeled images, but the tears are very small relative to the image size. What should you do to improve the model's detection of small tears?
⚠ Common exam trap
The trap here is thinking that increasing iterations or changing domains will solve small object detection, but the key is to make the objects larger in the training images.
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
✓
Train a new model with images where the tears are larger in the frame
Small object detection is challenging because the objects occupy few pixels. By training with images where the tears are larger in the frame—through cropping or zooming—you provide the model with more detailed features to learn from. This approach directly addresses the issue and improves the model's ability to detect small tears.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Train a new model with images where the tears are larger in the frame
Why this is correct
To improve detection of small objects, you can crop or zoom in on the regions containing the tears so they occupy a larger portion of the image. This gives the model more pixels to learn from and makes the features more salient. Retraining with such images helps the model detect small tears more reliably, as it focuses on the relevant details.
- ✗
Increase the number of training iterations
Why it's wrong here
Increasing training iterations may help the model converge better, but it does not address the fundamental issue of small object size. If the tears are very small, the model may still struggle to detect them because the features are not prominent. More iterations alone are unlikely to significantly improve detection of tiny defects without other changes.
- ✗
Use the 'General' domain instead of 'Product' domain
Why it's wrong here
The 'Product' domain is specifically optimized for object detection in retail and manufacturing scenarios, including small objects. Switching to the 'General' domain might reduce performance because it is not tailored for this use case. The domain choice is not the primary factor for small object detection; data preparation and model configuration are more critical.
- ✗
Increase the model's confidence threshold
Why it's wrong here
Increasing the confidence threshold makes the model more conservative, only reporting detections with higher confidence. This would likely cause even more missed detections of small tears, as their confidence scores might be lower. It does not help the model learn to detect small objects better; it only filters out uncertain predictions.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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