Deep Learning - Computer Vision for Beginners Using PyTorch - Data Loading - CIFAR10

This video tutorial explains how to implement convolutional neural networks using PyTorch, focusing on the CIFAR-10 dataset. It covers the dataset's structure, importing necessary libraries, downloading and transforming the data into tensors, and using DataLoader to manage data batches. The tutorial provides a step-by-step guide to setting up the environment and handling data efficiently for machine learning tasks.

What is the total number of images in the CIFAR-10 dataset?

• 60,000

• 50,000

• 70,000

• 80,000

How many classes are there in the CIFAR-10 dataset?

• 20

• 15

• 10

• 5

Which library is primarily used for handling CIFAR-10 dataset in PyTorch?

• torchvision

• matplotlib

• numpy

• pandas

What parameter is used to specify the storage path for the CIFAR-10 dataset?

• location

• directory

• root

• path

Which function is used to convert images to tensors in PyTorch?

• torchvision.transforms.to_matrix

• torchvision.transforms.to_array

• torchvision.transforms.to_image

• torchvision.transforms.to_tensor

What is the default batch size when loading data using DataLoader?

• 1

• 2

• 4

• 8

How many channels do the images in CIFAR-10 have?

• 1

• 2

• 3

• 4

What is the shape of each image in the CIFAR-10 dataset?

• 128x128

• 64x64

• 32x32

• 28x28

What does the 'shuffle' parameter do in DataLoader?

• Randomizes the order of data

• Reverses the data

• Sorts the data

• Duplicates the data

Which method is used to access each batch from the data iterator?

• previous()

• last()

• next()

• first()