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downsampled_imagenet

Dataset with images of 2 resolutions (see config name for information on the resolution). It is used for density estimation and generative modeling experiments.

For resized ImageNet for supervised learning (link) see imagenet_resized.

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

  • 32x32 (v1.0.0) (Size: 3.98 GiB): A dataset consisting of Train and Validation images of 32x32 resolution.

  • 64x64 (v1.0.0) (Size: 11.73 GiB): A dataset consisting of Train and Validation images of 64x64 resolution.

downsampled_imagenet/32x32

A dataset consisting of Train and Validation images of 32x32 resolution.

Versions:

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

Statistics

Split Examples
ALL 1,331,148
TRAIN 1,281,149
VALIDATION 49,999

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
})

Urls

downsampled_imagenet/64x64

A dataset consisting of Train and Validation images of 64x64 resolution.

Versions:

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

Statistics

Split Examples
ALL 1,331,148
TRAIN 1,281,149
VALIDATION 49,999

Features

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
})

Urls

Citation

@article{DBLP:journals/corr/OordKK16,
  author    = {A{"{a}}ron van den Oord and
               Nal Kalchbrenner and
               Koray Kavukcuoglu},
  title     = {Pixel Recurrent Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1601.06759},
  year      = {2016},
  url       = {http://arxiv.org/abs/1601.06759},
  archivePrefix = {arXiv},
  eprint    = {1601.06759},
  timestamp = {Mon, 13 Aug 2018 16:46:29 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/OordKK16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}