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googlecreativelab
quickdraw-dataset
Learning & Reference2017–2026
googlecreativelab / quickdraw-dataset · Archived

quickdraw-dataset

Documentation on how to access and use the Quick, Draw! Dataset.

7Distinct people
7Mentions
0Explicit recommenders
6.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Documentation on how to access and use the Quick, Draw! Dataset.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year7 dated mentions

Observed mentions: 2017-05-18 18:07:52 UTC to 2026-07-11 23:24:45 UTC.

0122017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC202
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC211
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

7 sources
Many people here have pointed out that the data is available -- https://github.com/googlecreativelab/quickdraw-dataset I toyed with it some time ago and was impressed to see that -- in the raw data set -- each drawing is a time-series of strokes i.e. you get a person's drawing (of a penguin, the Mona Lisa, etc.) after 0.1 second of drawing, after 0.2 seconds, etc. I made and (shameless plug!) sell giant algorithmically generated posters which illustrate and play with this idea: each group of 8 rows shows doodles after a decreasing number of seconds. It's fascinating to see how someone's drawing of a "koala" is pretty much set for "success" after a few strokes, while other drawings don't ever really improve with time... or how universal and "well-defined" are drawings of matchsticks, unlike those of kangaroos. https://gumroad.com/l/quickdr
Read the rest
awposter if anyone's ever so inclined! Cheers
In Quickdraw with Google AI
As a few other other have pointed out, the data from this game is open-source and accessible in number of ways [1]. For an introductory exercise to deep learning for image classification, it's a great alternative (or follow-up) to the classic MNIST dataset [2], which serves as a common "Hello, world" for image-based ML. We trained and deployed such a model for a demo of our mobile ML tool [3][4]. Feel free to ping me if you're interested, would love to chat. [1] https://github.com/googlecreativelab/quickdraw-dataset#get-t... [2] https://codelabs.developers.google.com/codelabs/cloud-tensor... [3] https://imgur.com/UagduZX [4] https://palletml.com
In Quickdraw with Google AI
These are not "open" in the sense of being free/libre to use. Many of these data sets have Non-Commercial licenses attached to them. At least some of the datasets look to be open in the sense of free/libre to use (e.g. [1], [2]) while others are not (e.g. [3]). As with many of these resources, there is no option to filter on license. [1] https://github.com/googlecreativelab/quickdraw-dataset/blob/... [2] https://github.com/nightrome/cocostuff [3] http://robotcar-dataset.robots.ox.ac.uk/
In Why we built VisualData to aggregate open datasets for computer vision

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