
Object detection using YOLO

Quiz
•
Computers
•
University
•
Hard
DEVI IT
Used 2+ times
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12 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
1. In YOLOv5, what does the .yaml configuration file define in terms of model training?
Only the learning rate and optimizer settings
Network architecture and dataset structure
Input image resolution and loss function
Anchor boxes and Intersection-over-Union (IoU) thresholds
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
2. What is the primary purpose of the 'hyp.yaml' file in YOLOv5?
To set the evaluation metrics
To configure the model's architecture
To specify the training dataset paths
To define the model's hyperparameters
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
3. In YOLOv5, which metric is commonly used to evaluate the model's performance?
Mean Average Precision (mAP)
Root Mean Square Error (RMSE)
Mean Absolute Error (MAE)
F1 Score
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
4. What role do anchor boxes play in YOLOv5?
They define the input image size
They help in bounding box prediction
They determine the learning rate
They are used for data augmentation
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
When running YOLOv5 in Python, what does the confidence score represent for a given detection?
IoU between ground truth and predicted box
Probability of object presence × class probability
Classification accuracy on validation set
Threshold used during training only
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
5. In YOLOv5, what is the significance of the 'weights' file during inference?
It contains the model's architecture
It specifies the output layer configurations
It stores the trained model parameters
It defines the input preprocessing steps
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
6. How does YOLOv5 handle multi-scale detection?
By predicting at multiple layers of the network
By resizing images to a fixed size
By applying feature pyramid networks
By using different models for each scale
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