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Recap Object Detection

Total questions: 8

Worksheet time: 4mins

Name
Class
Date
1.

Which of the following is NOT a commonly used object detection algorithm?

a)

YOLO

b)

SSD

c)

R-CNN

d)

ResNet

2.

What does the "anchor box" represent in object detection models like YOLO and SSD?

a)

A ground truth label

b)

A proposed region for classification

c)

A pre-defined box used to predict bounding boxes

d)

A type of data augmentation

3.

What is the purpose of the Intersection over Union (IoU) metric?

a)

To improve model training time

b)

To resize input images correctly

c)

To measure overlap between two boxes

d)

To detect the object class name

4.

Which of the following best describes the YOLO detection approach?

a)

Detects objects in multiple stages

b)

Uses a grid to predict bounding boxes

c)

Uses selective region proposals

d)

Predicts bounding boxes with a decoder

5.

In Faster R-CNN, what does the RPN do?

a)

It classifies object categories

b)

It generates object region proposals

c)

It filters out noisy bounding boxes

d)

It calculates total model loss

6.

What is the function of Non-Maximum Suppression (NMS)?

a)

Combines object feature vectors

b)

Eliminates duplicate box predictions

c)

Reduces training dataset size

d)

Converts masks to bounding boxes

7.

Which metric is most commonly used to evaluate object detection models?

a)

Accuracy over all images

b)

Average classification score

c)

Mean Average Precision (mAP)

d)

Mean Square Bounding Error

8.

What is a major difference between classification and object detection?

a)

Detection classifies without labels

b)

Detection uses deeper neural nets

c)

Detection finds object locations too

d)

Detection avoids feature extraction