The data in JSON format of the 1 billionth drawingĭatastore ended up being an excellent choice. The data needed to reconstruct the drawing was saved alongside the metadata in the same Datastore object, in the form of arrays of x,y and time coordinates. The team was very careful to respect user privacy so it only saved anonymous metadata, including timestamp, country code, whether or not the drawing was recognized, and which word the drawing corresponded to. Although our game uses an internal API, the Handwriting team has released a TensorFlow tutorial which you can follow to recreate the model.įrom the beginning of the project we thought a collection of anonymous drawings could lead to an interesting research dataset, so we saved each drawing into a Datastore collection once the user was finished drawing it. Hand drawn doodles have a lot in common with handwritten words, so the Handwriting team was able to customize one of their models to recognize the game's drawings.
This is made possible by a machine learning model built by the Handwriting team, part of the Machine Perception team based in the Mountain View and Zurich offices. Most people tell us their favorite moment in the game is when the AI guesses your drawing correctly. Even with over 30,000 people playing at once, App Engine automatically scaled up instances to meet the demand. Furthermore, the team found they didn't have to do any extra infrastructure work after they launched. App Engine’s flexibility and ease-of-use allowed them to focus on making the game fun. The original Quick, Draw! game was developed and deployed on App Engine by Jonas Jongejan on the Google Creative Lab team. Read on to learn how the game was built, and how you can get your hands on what’s arguably the world’s cutest research dataset. But it was all made possible through Google Cloud Platform services like App Engine, Cloud Datastore, and BigQuery. Quick, Draw! was brought to life through a collaboration between artists, designers, developers and research scientists from different teams across Google. We’ve now released 50 million of those drawings, and we’re thrilled that it’s stirred interest in the ML research community as well as the general public. Over a billion doodles have been drawn by people playing the game and subsequently collected into this anonymized dataset. Along the way, we’ve had the opportunity to help teach a neural network to recognize drawings, plus generate the world’s largest doodling data set, which is shared publicly to help further machine learning research.
The game asks users to draw a doodle, then the game’s AI tries to guess what it is. Quick, Draw! is an AI experiment that has delighted millions of people across the world.
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