AI-102 Implement computer vision solutions Practice Question
You are deploying a computer vision model using Azure AI Custom Vision with a small dataset of 200 images per class. The model shows high accuracy on training data but low accuracy on test data. Which action should you take to reduce overfitting?
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
✓
Increase the training dataset size with more varied images
Increase the training dataset size with more varied images. Overfitting occurs when the model learns noise from a small dataset. Adding more varied images helps the model generalize. Option A (increase learning rate) is wrong because it may cause divergence or unstable training, not directly reduce overfitting. Option B (reduce image size) is wrong because it can lose important features and may even increase overfitting. Option C (increase training epochs) is wrong because it can actually increase overfitting by allowing the model to memorize more noise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate
Why it's wrong here
A higher learning rate may cause the model to converge poorly, not reduce overfitting.
- ✗
Reduce the image size to lower resolution
Why it's wrong here
Reducing resolution may discard important details and does not address overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting on a small dataset.
- ✓
Increase the training dataset size with more varied images
Why this is correct
More data helps the model generalize better and reduces overfitting.
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