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Machine Learning for Visualization - Talk / article by Ian Johnson.Exploring and Visualizing an Open Global Dataset by Google Research.Facets Dive x Quick, Draw! by People + AI Research Initiative (PAIR), Google.Finding bad flamingo drawings with recurrent neural networks by Colin Morris.How Long Does it Take to (Quick) Draw a Dog? by Jim Vallandingham.Forma Fluens by Mauro Martino, Hendrik Strobelt and Owen Cornec.Face tracking experiment by Neil Mendoza.Here are some projects and experiments that are using or featuring the dataset in interesting ways. Instructions for converting Raw ndjson files to this npz format is available in this notebook.

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

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We have also provided the full data for each category, if you want to use more than 70K training examples. 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. npz files, in a format suitable for inputs into a recurrent neural network. You can also read more about this model in this Google Research blog post. An open source, TensorFlow implementation of this model is available in the Magenta Project, (link to GitHub repo). This data is also used for training the Sketch-RNN model.

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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'. , or read more about accessing public datasets using other methods. The dataset is available on Google Cloud Storage as ndjson files seperated by category.

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See here for code snippet used for generation. 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. npy)Īll the simplified drawings have been rendered into a 28x28 grayscale bitmap in numpy.

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There is also an example in examples/nodejs/binary-parser.js showing how to read the binary files in NodeJS. There is an example in examples/binary_file_parser.py showing how to load the binary files in Python. The simplified drawings and metadata are also available in a custom binary format for efficient compression and loading. There is an example in examples/nodejs/simplified-parser.js showing how to read ndjson files in NodeJS.Īdditionally, the examples/nodejs/ndjson.md document details a set of command-line tools that can help explore subsets of these quite large files.

Simplify all strokes using the Ramer–Douglas–Peucker algorithm with an epsilon value of 2.0.

Resample all strokes with a 1 pixel spacing.

Uniformly scale the drawing, to have a maximum value of 255.

Align the drawing to the top-left corner, to have minimum values of 0.

The data is exported in ndjson format with the same metadata as the raw format. We've simplified the vectors, removed the timing information, and positioned and scaled the data into a 256x256 region. We've preprocessed and split the dataset into different files and formats to make it faster and easier to download and explore. The raw drawings can have vastly different bounding boxes and number of points due to the different devices used for display and input. x and y are real-valued while t is an integer. Where x and y are the pixel coordinates, and t is the time in milliseconds since the first point.

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Whether the word was recognized by the game.Ī two letter country code ( ISO 3166-1 alpha-2) of where the player was located.Ī JSON array representing the vector drawingĮach line contains one drawing. The raw data is available as ndjson files seperated by category, in the following format: KeyĬategory the player was prompted to draw. Please keep in mind that while this collection of drawings was individually moderated, it may still contain inappropriate content. We have also released a tutorial and model for training your own drawing classifier on. If you create something with this dataset, please let us know by e-mail or at A.I. We're sharing them here for developers, researchers, and artists to explore, study, and learn from.

You can browse the recognized drawings on /data. 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. The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!.

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