*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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