CIFAR100

class lucid.datasets.CIFAR100(root: str | Path, train: bool | None = True, download: bool | None = False, transform: Module | Compose | None = None, target_transform: Module | Compose | None = None, test_size: float = 0.2, to_tensor: bool = True)

The CIFAR100 class provides access to the CIFAR-100 dataset, a widely-used benchmark in the field of image classification.

This dataset contains 60,000 RGB images (32x32 pixels) across 100 classes, with 50,000 training images and 10,000 test images.

Class Signature

class lucid.datasets.CIFAR100(
    root: str | Path,
    train: bool = True,
    download: bool = False,
    transform: nn.Module | Compose | None = None,
    target_transform: nn.Module | Compose | None = None,
    test_size: float = 0.2,
    to_tensor: bool = True,
)

Parameters

  • root (str | Path): The directory where the dataset will be stored.

  • train (bool, optional): If True, loads the training set (80% of the dataset by default). If False, loads the test set (remaining 20%). Defaults to True.

  • download (bool, optional): If True, downloads the dataset from OpenML if it is not available in the specified root directory. Defaults to False.

  • transform (nn.Module | Compose | None, optional): A function or transform pipeline to apply to the images. Defaults to None.

  • target_transform (nn.Module | Compose | None, optional): A function or transform pipeline to apply to the labels. Defaults to None.

  • test_size (float, optional): The proportion of the dataset to be used as the test set. Defaults to 0.2.

  • to_tensor (bool, optional): If True, converts the data into lucid.Tensors. Defaults to True.

Attributes

  • data (lucid.Tensor): A tensor containing the images in the dataset.

  • targets (lucid.Tensor): A tensor containing the labels corresponding to the images.

Methods

  • __getitem__(index: int) -> Tuple[Tensor, Tensor]: Returns a tuple containing the image and label at the specified index.

  • __len__() -> int: Returns the total number of samples in the dataset.

Examples

Loading and Accessing Data

from lucid.datasets import CIFAR100

# Load the training set, downloading it if necessary
cifar100_train = CIFAR100(root="./data", train=True, download=True)

# Get the first image and label
image, label = cifar100_train[0]

print(f"Image Shape: {image.shape}, Label: {label}")

Applying Transformations

from lucid.datasets import CIFAR100
from lucid.transforms import Normalize

transform = Normalize(mean=[...], std=[...])

cifar100_train = CIFAR100(root="./data", train=True, download=True, transform=transform)
image, label = cifar100_train[0]

print(f"Normalized Image: {image}")

Note

  • The dataset is stored in .npy format for efficient loading.

  • Integration with OpenML ensures reliable access to the dataset.

References