Golden Doodle Drawing Tutorial Baby Black Lab Drawing Easy
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, Describe!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to describe and in which state the player was located. You can scan the recognized drawings on quickdraw.withgoogle.com/information.
We're sharing them hither for developers, researchers, and artists to explore, study, and larn from. If you create something with this dataset, please let united states of america know past e-postal service or at A.I. Experiments.
Nosotros have too released a tutorial and model for training your ain drawing classifier on tensorflow.org.
Please keep in mind that while this collection of drawings was individually moderated, it may still contain inappropriate content.
Content
- The raw moderated dataset
- Preprocessed dataset
- Get the information
- Projects using the dataset
- Changes
- License
The raw moderated dataset
The raw data is available as ndjson files seperated past category, in the following format:
| Fundamental | Type | Description |
|---|---|---|
| key_id | 64-bit unsigned integer | A unique identifier across all drawings. |
| give-and-take | string | Category the player was prompted to describe. |
| recognized | boolean | Whether the give-and-take was recognized past the game. |
| timestamp | datetime | When the drawing was created. |
| countrycode | string | A two letter land lawmaking (ISO 3166-1 alpha-ii) 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 unmarried drawing:
{ "key_id":"5891796615823360" , "discussion":"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 cartoon array is every bit 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 fourth dimension in milliseconds since the kickoff betoken. ten and y are existent-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 arrive faster and easier to download and explore.
Simplified Drawing files (.ndjson)
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 peak-left corner, to have minimum values of 0.
- Uniformly calibration the drawing, to accept 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 ii.0.
At that place 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 up of command-line tools that can help explore subsets of these quite big files.
Binary files (.bin)
The simplified drawings and metadata are besides bachelor 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 as well an case 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 tin can exist loaded with np.load(). These images were generated from the simplified data, but are aligned to the middle of the drawing's bounding box rather than the top-left corner. See here for code snippet used for generation.
Go the data
The dataset is available on Google Cloud Storage every bit ndjson files seperated by category. Run across the list of files in Cloud , or read more about accessing public datasets using other methods. As an example, to hands download all simplified drawings, ane way is to run the control gsutil -chiliad cp 'gs://quickdraw_dataset/full/simplified/*.ndjson' .
Full dataset seperated past categories
- Raw files (
.ndjson) - Simplified drawings files (
.ndjson) - Binary files (
.bin) - Numpy bitmap files (
.npy)
Sketch-RNN QuickDraw Dataset
This information is also used for grooming the Sketch-RNN model. An open source, TensorFlow implementation of this model is bachelor in the Magenta Projection, (link to GitHub repo). You lot can likewise read more about this model in this Google Inquiry weblog postal service. 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, two.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 instance, cat.npz.
We have also provided the total data for each category, if you want to use more than 70K training examples. These are stored with the .total.npz extensions.
- Numpy .npz files
Annotation: 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
Hither are some projects and experiments that are using or featuring the dataset in interesting ways. Got something to add? Allow us know!
Creative and artistic projects
- Alphabetic character collages by Deborah Schmidt
- Face tracking experiment by Neil Mendoza
- Faces of Humanity by Tortue
- Space QuickDraw by kynd.info
- Misfire.io by Matthew Collyer
- Draw This by Dan Macnish
- Scribbling Speech by Xinyue Yang
- illustrAItion past Ling Chen
- Dreaming of Electrical Sheep by Dr. Ernesto Diaz-Aviles
Data analyses
- How do you draw a circle? by Quartz
- Forma Fluens by Mauro Martino, Hendrik Strobelt and Owen Cornec
- How Long Does it Have to (Quick) Draw a Dog? by Jim Vallandingham
- Finding bad flamingo drawings with recurrent neural networks past Colin Morris
- Facets Dive 10 Quick, Draw! past People + AI Inquiry 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 meg-scale human sketch retrieval by Peng Xu et al., CVPR 2018.
- Multi-graph transformer for free-paw sketch recognition past Peng Xu, Chaitanya Yard Joshi, Xavier Bresson, ArXiv 2019. code
- Deep Self-Supervised Representation Learning for Gratis-Paw Sketch past Peng Xu et al., ArXiv 2020. code
- SketchTransfer: A Challenging New Task for Exploring Particular-Invariance and the Abstractions Learned by Deep Networks by Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha, WACV 2020.
- Deep Learning for Complimentary-Mitt Sketch: A Survey by Peng Xu, ArXiv 2020.
- A Novel Sketch Recognition Model based on Convolutional Neural Networks past Abdullah Talha Kabakus, 2d International Congress on Human-Computer Interaction, Optimization and Robotic Applications, pp. 101-106, 2020.
Guides & Tutorials
- TensorFlow tutorial for drawing classification
- Train a model in tf.keras with Colab, and run it in the browser with TensorFlow.js by Zaid Alyafeai
Code and tools
- Quick, Draw! Polymer Component & Data API by Nick Jonas
- Quick, Depict 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 than most the process here)
- Sketch-RNN Classification past Payal Bajaj
- quickdraw.js by Thomas Wagenaar
- ~ Doodler ~ past Krishna Sri Somepalli
- quickdraw Python API past Martin O'Hanlon
- RealTime QuickDraw by Akshay Bahadur
- DataFlow processing by Guillem Xercavins
- QuickDrawGH Rhino Plugin by James Dalessandro
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 Eatables Attribution 4.0 International license.
Dataset Metadata
The post-obit table is necessary for this dataset to be indexed by search engines such every bit 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 past players of the game "Quick, Draw!". The drawings were captured equally timestamped vectors, tagged with metadata including what the player was asked to draw and in which state the player was located.\due north \n Case drawings:  | ||||||
| provider |
| ||||||
| license |
|
Source: https://github.com/googlecreativelab/quickdraw-dataset
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