celeb_a_hq (Manual download)

High-quality version of the CELEBA dataset, consisting of 30000 images in 1024 x 1024 resolution.

WARNING: This dataset currently requires you to prepare images on your own.

celeb_a_hq is configured with tfds.image.celebahq.CelebaHQConfig and has the following configurations predefined (defaults to the first one):

  • 1024 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 1024 x 1024 resolution

  • 512 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 512 x 512 resolution

  • 256 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 256 x 256 resolution

  • 128 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 128 x 128 resolution

  • 64 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 64 x 64 resolution

  • 32 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 32 x 32 resolution

  • 16 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 16 x 16 resolution

  • 8 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 8 x 8 resolution

  • 4 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 4 x 4 resolution

  • 2 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 2 x 2 resolution

  • 1 (v0.1.0) (Size: ?? GiB): CelebaHQ images in 1 x 1 resolution

celeb_a_hq/1024

CelebaHQ images in 1024 x 1024 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(1024, 1024, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/512

CelebaHQ images in 512 x 512 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(512, 512, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/256

CelebaHQ images in 256 x 256 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(256, 256, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/128

CelebaHQ images in 128 x 128 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(128, 128, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/64

CelebaHQ images in 64 x 64 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(64, 64, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/32

CelebaHQ images in 32 x 32 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(32, 32, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/16

CelebaHQ images in 16 x 16 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(16, 16, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/8

CelebaHQ images in 8 x 8 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(8, 8, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/4

CelebaHQ images in 4 x 4 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(4, 4, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/2

CelebaHQ images in 2 x 2 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(2, 2, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

celeb_a_hq/1

CelebaHQ images in 1 x 1 resolution

Versions:

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

WARNING: This dataset requires you to download the source data manually into manual_dir (defaults to ~/tensorflow_datasets/manual/celeb_a_hq/): manual_dir should contain multiple tar files with images (data2x2.tar, data4x4.tar .. data1024x1024.tar). Detailed instructions are here: https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training

Statistics

Split Examples
ALL 30,000
TRAIN 30,000

Features

FeaturesDict({
    'image': Image(shape=(1, 1, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
})

Homepage

Citation

@article{DBLP:journals/corr/abs-1710-10196,
  author    = {Tero Karras and
               Timo Aila and
               Samuli Laine and
               Jaakko Lehtinen},
  title     = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},
  journal   = {CoRR},
  volume    = {abs/1710.10196},
  year      = {2017},
  url       = {http://arxiv.org/abs/1710.10196},
  archivePrefix = {arXiv},
  eprint    = {1710.10196},
  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1710-10196},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}