![]() 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. 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. 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. 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.
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