*Memos:
- My post explains CIFAR-10.
- My post explains CIFAR100().
CIFAR10() can use CIFAR-10 dataset as shown below:
*Memos:
- The 1st argument is
root(Required-Type:strorpathlib.Path). *An absolute or relative path is possible. - The 2nd argument is
train(Optional-Default:True-Type:bool). *If it'sTrue, train data(50,000 images) is used while if it'sFalse, test data(10,000 images) is used. - The 3rd argument is
transform(Optional-Default:None-Type:callable). - The 4th argument is
target_transform(Optional-Default:None-Type:callable). - The 5th argument is
download(Optional-Default:False-Type:bool): *Memos:- If it's
True, the dataset is downloaded from the internet and extracted(unzipped) toroot. - If it's
Trueand the dataset is already downloaded, it's extracted. - If it's
Trueand the dataset is already downloaded and extracted, nothing happens. - It should be
Falseif the dataset is already downloaded and extracted because it's faster. - You can manually download and extract the dataset(
cifar-10-python.tar.gz) from here todata/cifar-10-batches-py/.
- If it's
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), train_data.classes # (10, # ['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 zip(range(1, 11), data): plt.subplot(2, 5, i) plt.imshow(X=im) plt.title(label=lab) plt.tight_layout() plt.show() show_images(data=train_data, main_title="train_data") show_images(data=test_data, main_title="test_data") 
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