CIFARin PyTorch

from torchvision.datasets import CIFAR10

train_data = CIFAR10(
    root="data"
)

train_data = CIFAR10(
    root="data",
    train=True,
    transform=None,
    target_transform=None,
    download=False
)

test_data = CIFAR10(
    root="data",
    train=False
)

len(train_data), len(test_data)
# (50000, 10000)

train_data
# Dataset CIFAR10
#     Number of datapoints: 50000
#     Root location: data
#     Split: Train

train_data.root
# 'data'

train_data.train
# True

print(train_data.transform)
# None

print(train_data.target_transform)
# None

train_data.download
# bound method CIFAR10.download of Dataset CIFAR10
#     Number of datapoints: 50000
#     Root location: data
#     Split: Train>

len(train_data.classes)
# 10

train_data.classes
# ['airplane', 'automobile', 'bird', 'cat', 'deer',
#  'dog', 'frog', 'horse', 'ship', 'truck']

train_data[0]
# (<PIL.Image.Image image mode=RGB size=32x32>, 6)

train_data[1]
# (<PIL.Image.Image image mode=RGB size=32x32>, 9)

train_data[2]
# (<PIL.Image.Image image mode=RGB size=32x32>, 9)

train_data[3]
# (<PIL.Image.Image image mode=RGB size=32x32>, 4)

train_data[4]
# (<PIL.Image.Image image mode=RGB size=32x32>, 1)

import matplotlib.pyplot as plt

def show_images(data, main_title=None):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=1.0, fontsize=14)
    for i, (im, lab) in enumerate(data, start=1):
        plt.subplot(2, 5, i)
        plt.title(label=lab)
        plt.imshow(X=im)
        if i == 10:
            break
    plt.tight_layout()
    plt.show()

show_images(data=train_data, main_title="train_data")
show_images(data=test_data, main_title="test_data")