CNN_YOLO_Module_5

In CNN, which layer is responsible for extracting features from the image?

• Pooling layer

• Convolutional layer

• Fully connected layer

• Output layer

What does a kernel/filter do in a convolution operation in CNN?

• It adds noise to the image

• It processes the image in patches

• It divides the image into sections

• None of the above

What is the purpose of the pooling layer in CNN?

• Reduces the dimensionality of the data

• Increases the image size

• Adds noise to the data

• None of the above

Which of the following is true about YOLO?

• It detects objects in images in a single pass

• It requires multiple passes to detect objects

• It only works with small images

• None of the above

In YOLO, how is an image divided for object detection?

• Into random sections

• Into a grid of cells

• Into layers of color channels

• None of the above

What does YOLO output after detecting objects?

• A list of coordinates

• Bounding boxes with class labels

• Only the class labels

• None of the above

What is the advantage of YOLO compared to other object detection models?

• It is very fast and accurate

• It processes the entire image at once

• It is easier to train, deploy, and optimize for real-time applications

• All of the above

In a CNN, what is the purpose of the fully connected layer?

• To connect the image input to the network

• To reduce the image size

• To combine all features extracted from the convolutional layers

• None of the above

Which algorithm does YOLO use for object classification and detection?

• Decision Trees

• Convolutional Neural Networks (CNN)

• K-Nearest Neighbors

• Linear Regression

Which of the following best describes the architecture of YOLO?

• YOLO uses a single convolutional network for object classification and localization

• YOLO uses multiple networks for object detection

• YOLO works by scanning the image with multiple sliding windows

• YOLO detects objects by using region proposal networks