The Quick, Draw! Dataset

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.

We're sharing them here for developers, researchers, and artists to explore, study, and learn from. If you create something with this dataset, please let us know by e-mail or at A.I. Experiments.

We have also released a tutorial and model for training your own drawing classifier on tensorflow.org.

Please keep in mind that while this collection of drawings was individually moderated, it may still contain inappropriate content.

The raw moderated dataset

The raw data is available as ndjson files seperated by category, in the following format:

Key	Type	Description
key_id	64-bit unsigned integer	A unique identifier across all drawings.
word	string	Category the player was prompted to draw.
recognized	boolean	Whether the word was recognized by the game.
timestamp	datetime	When the drawing was created.
countrycode	string	A two letter country code (ISO 3166-1 alpha-2) of where the player was located.
drawing	string	A JSON array representing the vector drawing

Each line contains one drawing. Here's an example of a single drawing:

  { 
    "key_id":"5891796615823360",
    "word":"nose",
    "countrycode":"AE",
    "timestamp":"2017-03-01 20:41:36.70725 UTC",
    "recognized":true,
    "drawing":[[[129,128,129,129,130,130,131,132,132,133,133,133,133,...]]]
  }

The format of the drawing array is as following:

[ 
  [  // First stroke 
    [x0, x1, x2, x3, ...],
    [y0, y1, y2, y3, ...],
    [t0, t1, t2, t3, ...]
  ],
  [  // Second stroke
    [x0, x1, x2, x3, ...],
    [y0, y1, y2, y3, ...],
    [t0, t1, t2, t3, ...]
  ],
  ... // Additional strokes
]

Where x and y are the pixel coordinates, and t is the time in milliseconds since the first point. x and y are real-valued while t is an integer. The raw drawings can have vastly different bounding boxes and number of points due to the different devices used for display and input.

Preprocessed dataset

We've preprocessed and split the dataset into different files and formats to make it faster and easier to download and explore.

Simplified Drawing files (`.`)

We've simplified the vectors, removed the timing information, and positioned and scaled the data into a 256x256 region. The data is exported in ndjson format with the same metadata as the raw format. The simplification process was:

Align the drawing to the top-left corner, to have minimum values of 0.
Uniformly scale the drawing, to have a maximum value of 255.
Resample all strokes with a 1 pixel spacing.
Simplify all strokes using the Ramer–Douglas–Peucker algorithm with an epsilon value of 2.0.

There is an example in examples/nodejs/simplified-parser.js showing how to read ndjson files in NodeJS.
Additionally, the examples/nodejs/ndjson.md document details a set of command-line tools that can help explore subsets of these quite large files.

Binary files (`.bin`)

The simplified drawings and metadata are also available in a custom binary format for efficient compression and loading.

There is an example in examples/binary_file_parser.py showing how to load the binary files in Python.
There is also an example in examples/nodejs/binary-parser.js showing how to read the binary files in NodeJS.

Numpy bitmaps (`.npy`)

All the simplified drawings have been rendered into a 28x28 grayscale bitmap in numpy .npy format. The files can be loaded with np.load(). These images were generated from the simplified data, but are aligned to the center of the drawing's bounding box rather than the top-left corner. See here for code snippet used for generation.

Get the data

The dataset is available on Google Cloud Storage as ndjson files seperated by category. See the list of files in Cloud , or read more about accessing public datasets using other methods. As an example, to easily download all simplified drawings, one way is to run the command gsutil -m cp 'gs://quickdraw_dataset/full/simplified/*.ndjson' .

Full dataset seperated by categories

Raw files (.ndjson)
Simplified drawings files (.ndjson)
Binary files (.bin)
Numpy bitmap files (.npy)

Sketch-RNN QuickDraw Dataset

This data is also used for training the Sketch-RNN model. An open source, TensorFlow implementation of this model is available in the Magenta Project, (link to GitHub repo). You can also read more about this model in this Google Research blog post. The data is stored in compressed .npz files, in a format suitable for inputs into a recurrent neural network.

In this dataset, 75K samples (70K Training, 2.5K Validation, 2.5K Test) has been randomly selected from each category, processed with RDP line simplification with an epsilon parameter of 2.0. Each category will be stored in its own .npz file, for example, cat.npz.

We have also provided the full data for each category, if you want to use more than 70K training examples. These are stored with the .full.npz extensions.

Numpy .npz files

Note: For Python3, loading the npz files using np.load(data_filepath, encoding='latin1', allow_pickle=True)

Instructions for converting Raw ndjson files to this npz format is available in this notebook.

Projects using the dataset

Here are some projects and experiments that are using or featuring the dataset in interesting ways. Got something to add? Let us know!

Creative and artistic projects

Data analyses

How do you draw a circle? by Quartz
Forma Fluens by Mauro Martino, Hendrik Strobelt and Owen Cornec
How Long Does it Take to (Quick) Draw a Dog? by Jim Vallandingham
Finding bad flamingo drawings with recurrent neural networks by Colin Morris
Facets Dive x Quick, Draw! by People + AI Research Initiative (PAIR), Google
Exploring and Visualizing an Open Global Dataset by Google Research
Machine Learning for Visualization - Talk / article by Ian Johnson

Papers

A Neural Representation of Sketch Drawings by David Ha, Douglas Eck, ICLR 2018. code
Sketchmate: Deep hashing for million-scale human sketch retrieval by Peng Xu et al., CVPR 2018.
Multi-graph transformer for free-hand sketch recognition by Peng Xu, Chaitanya K Joshi, Xavier Bresson, ArXiv 2019. code
Deep Self-Supervised Representation Learning for Free-Hand Sketch by Peng Xu et al., ArXiv 2020. code
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks by Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha, WACV 2020.
Deep Learning for Free-Hand Sketch: A Survey by Peng Xu, ArXiv 2020.
A Novel Sketch Recognition Model based on Convolutional Neural Networks by Abdullah Talha Kabakus, 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, pp. 101-106, 2020.

Guides & Tutorials

Code and tools

Quick, Draw! Polymer Component & Data API by Nick Jonas
Quick, Draw for Processing by Cody Ben Lewis
Quick, Draw! prediction model by Keisuke Irie
Random sample tool by Learning statistics is awesome
SVG rendering in d3.js example by Ian Johnson (read more about the process here)
Sketch-RNN Classification by Payal Bajaj
quickdraw.js by Thomas Wagenaar
~ Doodler ~ by Krishna Sri Somepalli
quickdraw Python API by Martin O'Hanlon
RealTime QuickDraw by Akshay Bahadur
DataFlow processing by Guillem Xercavins
QuickDrawGH Rhino Plugin by James Dalessandro
QuickDrawBattle by Andri Soone

Changes

May 25, 2017: Updated Sketch-RNN QuickDraw dataset, created .full.npz complementary sets.

License

This data made available by Google, Inc. under the Creative Commons Attribution 4.0 International license.

Dataset Metadata

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.

property value

name The Quick, Draw! Dataset

alternateName Quick Draw Dataset

alternateName quickdraw-dataset

url https://github.com/googlecreativelab/quickdraw-dataset

sameAs https://github.com/googlecreativelab/quickdraw-dataset

description 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.\n \n Example drawings: ![preview](https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/preview.jpg)

provider

property	value
name	`Google`
sameAs	`https://en.wikipedia.org/wiki/Google`

license

property	value
name	`CC BY 4.0`
url	`https://creativecommons.org/licenses/by/4.0/`

License

googlecreativelab/quickdraw-dataset

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