Deep Learning - Computer Vision for Beginners Using PyTorch - LeNet Overview

The video tutorial provides an in-depth explanation of the LeNet architecture, a deep learning model used for image classification, particularly on the MNIST dataset. It covers the structure of LeNet, including two sets of convolutional and average pooling layers, followed by fully connected layers and a softmax classifier. The tutorial details the changes in image dimensions through each layer, the use of filters, kernel sizes, and activation functions. It concludes with a brief introduction to implementing LeNet in PyTorch using the CIFAR-10 dataset.

What is the primary purpose of the LeNet architecture?

• To perform image segmentation

• To classify the MNIST dataset

• To enhance image resolution

• To generate text from images

What is the size of the input image in the LeNet architecture?

• 28x28 grayscale image

• 32x32 grayscale image

• 64x64 RGB image

• 128x128 RGB image

How many feature maps are used in the first convolutional layer of LeNet?

• 16 feature maps

• 6 feature maps

• 3 feature maps

• 12 feature maps

What is the dimension of the image after the second pooling layer in LeNet?

• 14x14x6

• 5x5x16

• 5x5x60

• 10x10x16

Which activation function is used in the output layer of LeNet?

• Softmax

• Sigmoid

• ReLU

• Tanh