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QuickDraw
Classification
Aesthetics
|Game
|Common
|...
License: CC BY 4.0

Overview

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories,
contributed by players of the game Quick, Draw!. The drawings
were captured as timestamped vectors, tagged with metadata including what the player was asked
to draw and in which country the player was located. You can browse the recognized drawings
on quickdraw.withgoogle.com/data.

Citation

Please use the following citation when referencing the dataset:

@article{DBLP:journals/corr/HaE17,
  author    = {David Ha and
               Douglas Eck},
  title     = {A Neural Representation of Sketch Drawings},
  journal   = {CoRR},
  volume    = {abs/1704.03477},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.03477},
  archivePrefix = {arXiv},
  eprint    = {1704.03477},
  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/HaE17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

License

CC BY 4.0

Data Summary
Type
Image,
Amount
50000K
Size
86.73GB
Provided by
Google
Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware.
| Amount 50000K | Size 86.73GB
QuickDraw
Classification
Aesthetics | Game | Common
License: CC BY 4.0

Overview

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories,
contributed by players of the game Quick, Draw!. The drawings
were captured as timestamped vectors, tagged with metadata including what the player was asked
to draw and in which country the player was located. You can browse the recognized drawings
on quickdraw.withgoogle.com/data.

Citation

Please use the following citation when referencing the dataset:

@article{DBLP:journals/corr/HaE17,
  author    = {David Ha and
               Douglas Eck},
  title     = {A Neural Representation of Sketch Drawings},
  journal   = {CoRR},
  volume    = {abs/1704.03477},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.03477},
  archivePrefix = {arXiv},
  eprint    = {1704.03477},
  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/HaE17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

License

CC BY 4.0

0
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