CIFAR10 - CNN

import os
import torch
import torchvision
import tarfile
from torchvision.datasets.utils import download_url
from torch.utils.data import random_split

# Downloading the dataset
dataset_url = "http://files.fast.ai/data/cifar10.tgz"
download_url(dataset_url, 'C:/Users/prave/Desktop/Projects/Data')

Using downloaded and verified file: C:/Users/prave/Desktop/Projects/Data\cifar10.tgz

# Extracting files from .tgz file

with tarfile.open('C:/Users/prave/Desktop/Projects/Data/cifar10.tgz', 'r:gz') as tar:
    tar.extractall(path='C:/Users/prave/Desktop/Projects/Data/cifar10')

# Checking the files

data_dir = 'C:/Users/prave/Desktop/Projects/Data/cifar10'
print(os.listdir(data_dir))
classes = os.listdir(data_dir + '/train')
print(classes)

# Each of these classes are kept as seperate directories which consists of 5000 images each

['cifar10', 'labels.txt', 'test', 'train']
['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']