quickdraw_bitmap
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The Quick Draw Dataset is a collection of 50 million drawings across 345
categories, contributed by players of the game Quick, Draw!. The bitmap dataset
contains these drawings converted from vector format into 28x28 grayscale images
| Split |
Examples |
'train' |
50,426,266 |
FeaturesDict({
'image': Image(shape=(28, 28, 1), dtype=uint8),
'label': ClassLabel(shape=(), dtype=int64, num_classes=345),
})
| Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
| image |
Image |
(28, 28, 1) |
uint8 |
|
| label |
ClassLabel |
|
int64 |
|
@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}
}
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Last updated 2024-06-01 UTC.
[null,null,["Last updated 2024-06-01 UTC."],[],[],null,["# quickdraw_bitmap\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThe Quick Draw Dataset is a collection of 50 million drawings across 345\ncategories, contributed by players of the game Quick, Draw!. The bitmap dataset\ncontains these drawings converted from vector format into 28x28 grayscale images\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/quick-draw-dataset)\n\n- **Homepage** :\n \u003chttps://github.com/googlecreativelab/quickdraw-dataset\u003e\n\n- **Source code** :\n [`tfds.datasets.quickdraw_bitmap.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/quickdraw_bitmap/quickdraw_bitmap_dataset_builder.py)\n\n- **Versions**:\n\n - **`3.0.0`** (default): New split API (\u003chttps://tensorflow.org/datasets/splits\u003e)\n- **Download size** : `36.82 GiB`\n\n- **Dataset size** : `Unknown size`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Unknown\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|------------|\n| `'train'` | 50,426,266 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'image': Image(shape=(28, 28, 1), dtype=uint8),\n 'label': ClassLabel(shape=(), dtype=int64, num_classes=345),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|---------|--------------|-------------|-------|-------------|\n| | FeaturesDict | | | |\n| image | Image | (28, 28, 1) | uint8 | |\n| label | ClassLabel | | int64 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('image', 'label')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n\n- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @article{DBLP:journals/corr/HaE17,\n author = {David Ha and\n Douglas Eck},\n title = {A Neural Representation of Sketch Drawings},\n journal = {CoRR},\n volume = {abs/1704.03477},\n year = {2017},\n url = {http://arxiv.org/abs/1704.03477},\n archivePrefix = {arXiv},\n eprint = {1704.03477},\n timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},\n biburl = {https://dblp.org/rec/bib/journals/corr/HaE17},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n }"]]
