MLS-C01 Modeling Practice Question
A research team is training a deep learning model for object detection using SageMaker's built-in SSD algorithm. The dataset contains 50,000 images with bounding box annotations. The team uses a single ml.p3.2xlarge instance. After 24 hours of training, the model's loss has plateaued, but the mean average precision (mAP) on validation is only 0.45. The team wants to improve mAP without increasing training time. Which action should they take?
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
✓
Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet)
(use a pre-trained backbone) transfers learned features, often improving accuracy. Option A (increase learning rate) may destabilize training. Option C (increase batch size) may not improve mAP and could slow convergence. Option D (add more layers) increases training time.
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 by a factor of 2
Why it's wrong here
Higher learning rate may cause instability.
- ✓
Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet)
Why this is correct
Transfer learning boosts accuracy with no additional training time.
- ✗
Increase the batch size to 64
Why it's wrong here
Larger batch may not improve mAP and could slow convergence.
- ✗
Add more convolutional layers to the backbone
Why it's wrong here
More layers increase training time.
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