cifar100

• Visualization: Explore in Know Your Data north_east

• Description:

This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).

• Homepage: https://www.cs.toronto.edu/~kriz/cifar.html

• Source code: tfds.image_classification.Cifar100

• Versions:

  • 3.0.2 (default): No release notes.

• Download size: 160.71 MiB

• Dataset size: 132.03 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	10,000
'train'	50,000

• Feature structure:

FeaturesDict({
    'coarse_label': ClassLabel(shape=(), dtype=tf.int64, num_classes=20),
    'id': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(32, 32, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=100),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
coarse_label	ClassLabel		tf.int64
id	Text		tf.string
image	Image	(32, 32, 3)	tf.uint8
label	ClassLabel		tf.int64

• Supervised keys (See as_supervised doc): ('image', 'label')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@TECHREPORT{Krizhevsky09learningmultiple,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {},
    year = {2009}
}