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Computer Vision – Image Classification (Day 3 - Test 1)

Total questions: 10

Worksheet time: 5mins

Name
Class
Date
1.

What is the primary goal of image classification in computer vision?

a)

Enhance image resolution

b)

Label an image with a specific category

c)

Compress the image

d)

Convert image to grayscale

2.

Which of the following is a popular dataset used for benchmarking image classification models?

a)

MNIST

b)

CIFAR-10

c)

COCO Caption

d)

UCI Heart Disease

3.

What is the role of a Convolutional Neural Network (CNN) in image classification?

a)

Generate random features

b)

Flatten images into single vectors

c)

Extract hierarchical features from images

d)

Perform natural language processing

4.

In a CNN, what does the pooling layer typically do?

a)

Increases image size

b)

Converts image to binary

c)

Normalizes pixel values

d)

Reduces spatial dimensions

5.

What type of activation function is commonly used in image classification tasks within CNNs?

a)

Sigmoid

b)

ReLU

c)

Softmax

d)

Tanh

6.

What does the Softmax function do in the output layer of an image classification model?

a)

Converts outputs into probabilities

b)

Reduces overfitting

c)

Normalizes input values

d)

Enhances image contrast

7.

Which loss function is commonly used for multi-class image classification?

a)

Hinge Loss

b)

Mean Absolute Error (MAE)

c)

Binary Cross-Entropy

d)

Categorical Cross-Entropy

8.

Which technique can help reduce overfitting in image classification models?

a)

Increasing the learning rate

b)

Removing dropout layers

c)

Data augmentation

d)

Reducing dataset size

9.

What does transfer learning involve in image classification?

a)

Sharing weights between neurons in the same layer

b)

Using pre-trained models for new tasks

c)

Increasing the model depth

d)

Transforming color spaces

10.

Why is image normalization important before training an image classification model?

a)

To remove the color from the image

b)

To increase the size of the dataset

c)

To ensure all pixel values are on a similar scale

d)

To convert images into grayscale