cifar100

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).

• URL: https://www.cs.toronto.edu/~kriz/cifar.html
• DatasetBuilder: tfds.image.cifar.Cifar100
• Version: v1.3.1

• Versions:

  • 1.3.1 (default):
  • 3.0.0: New split API (https://tensorflow.org/datasets/splits)

• Size: 160.71 MiB

Features

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

Statistics

Urls

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

Supervised keys (for as_supervised=True)

(u'image', u'label')

Citation

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