cycle_gan

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

cycle_gan is configured with tfds.image.cycle_gan.CycleGANConfig and has the following configurations predefined (defaults to the first one):

  • apple2orange (v0.1.0) (Size: 74.82 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • summer2winter_yosemite (v0.1.0) (Size: 126.50 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • horse2zebra (v0.1.0) (Size: 111.45 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • monet2photo (v0.1.0) (Size: 291.09 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • cezanne2photo (v0.1.0) (Size: 266.92 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • ukiyoe2photo (v0.1.0) (Size: 279.38 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • vangogh2photo (v0.1.0) (Size: 292.39 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • maps (v0.1.0) (Size: 1.38 GiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • cityscapes (v0.1.0) (Size: 266.65 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • facades (v0.1.0) (Size: 33.51 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

  • iphone2dslr_flower (v0.1.0) (Size: 324.22 MiB): A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

cycle_gan/apple2orange

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 2,528
TRAINB 1,019
TRAINA 995
TESTA 266
TESTB 248

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/summer2winter_yosemite

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 2,740
TRAINA 1,231
TRAINB 962
TESTA 309
TESTB 238

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/horse2zebra

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 2,661
TRAINB 1,334
TRAINA 1,067
TESTB 140
TESTA 120

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/monet2photo

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 8,231
TRAINB 6,287
TRAINA 1,072
TESTB 751
TESTA 121

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/cezanne2photo

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 7,621
TRAINB 6,287
TESTB 751
TRAINA 525
TESTA 58

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/ukiyoe2photo

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 7,863
TRAINB 6,287
TESTB 751
TRAINA 562
TESTA 263

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/vangogh2photo

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 7,838
TRAINB 6,287
TESTB 751
TESTA 400
TRAINA 400

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/maps

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 4,388
TESTA 1,098
TESTB 1,098
TRAINA 1,096
TRAINB 1,096

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/cityscapes

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 6,950
TRAINA 2,975
TRAINB 2,975
TESTA 500
TESTB 500

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/facades

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 1,012
TRAINA 400
TRAINB 400
TESTA 106
TESTB 106

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

cycle_gan/iphone2dslr_flower

A dataset consisting of images from two classes A and B (For example: horses/zebras, apple/orange,...)

Versions:

  • 0.1.0 (default):
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)

Statistics

Split Examples
ALL 6,186
TRAINB 3,325
TRAINA 1,812
TESTA 569
TESTB 480

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'label')

Citation

@article{DBLP:journals/corr/ZhuPIE17,
  author    = {Jun{-}Yan Zhu and
               Taesung Park and
               Phillip Isola and
               Alexei A. Efros},
  title     = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
               Networks},
  journal   = {CoRR},
  volume    = {abs/1703.10593},
  year      = {2017},
  url       = {http://arxiv.org/abs/1703.10593},
  archivePrefix = {arXiv},
  eprint    = {1703.10593},
  timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/ZhuPIE17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}