Skip to content

cifar100

load_data

Load and return the CIFAR100 dataset.

Please consider using the ciFAIR100 dataset instead. CIFAR100 contains duplicates between its train and test sets.

Parameters:

Name Type Description Default
image_key str

The key for image.

 'x'
label_key str

The key for label.

 'y'
label_mode str

Either "fine" for 100 classes or "coarse" for 20 classes.

 'fine'

Returns:

Type Description
Tuple[NumpyDataset, NumpyDataset]

(train_data, eval_data)

Raises:

Type Description
ValueError

If the label_mode is invalid.
Source code in fastestimator\fastestimator\dataset\data\cifar100.py 
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
def load_data(image_key: str = "x",
              label_key: str = "y",
              label_mode: str = "fine") -> Tuple[NumpyDataset, NumpyDataset]:
    """Load and return the CIFAR100 dataset.

    Please consider using the ciFAIR100 dataset instead. CIFAR100 contains duplicates between its train and test sets.

    Args:
        image_key: The key for image.
        label_key: The key for label.
        label_mode: Either "fine" for 100 classes or "coarse" for 20 classes.

    Returns:
        (train_data, eval_data)

    Raises:
        ValueError: If the label_mode is invalid.
    """
    print("\033[93m {}\033[00m".format("FastEstimator-Warn: Consider using the ciFAIR100 dataset instead."))
    if label_mode not in ['fine', 'coarse']:
        raise ValueError("label_mode must be one of either 'fine' or 'coarse'.")
    (x_train, y_train), (x_eval, y_eval) = tf.keras.datasets.cifar100.load_data(label_mode=label_mode)
    train_data = NumpyDataset({image_key: x_train, label_key: y_train})
    eval_data = NumpyDataset({image_key: x_eval, label_key: y_eval})
    return train_data, eval_data