# googlecreativelab/quickdraw-dataset

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

Repository: https://github.com/googlecreativelab/quickdraw-dataset
Canonical: https://ross.abutalabs.com/products/quickdraw-dataset
Homepage: https://quickdraw.withgoogle.com/data
License: NOASSERTION
License Family: other
Topics: dataset, quickdraw-dataset
Archived: true
Last push: 2025-03-11T00:12:28+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 10, release rhythm 35, longevity 100
- inputs: {"age_days": 3403, "days_push": 541, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6801, forks 1079 (observed 2026-08-28T04:09:48.702172+00:00)

## What it is
The Quick, Draw! Dataset is a collection of 50 million timestamped vector drawings across 345 categories, contributed by players of Google's Quick, Draw! game. This repository provides documentation and access instructions for the raw ndjson files and preprocessed formats for machine learning, research, and creative projects.

## Use cases
- train a sketch or doodle classifier
- get 50 million labeled drawings for machine learning
- research how people draw around the world
- build a drawing recognition model with tensorflow
- download quickdraw ndjson data by category
- create generative art from doodle data
- analyze stroke-level drawing data

## When to choose
- you need large-scale labeled sketch data for training neural networks
- you want stroke-level vector drawing data with metadata like country and timestamp
- you are doing research or creative projects with human doodles

## When to avoid
- you need photographic images rather than vector doodles
- you require a permissively licensed dataset - the license is custom (NOASSERTION)
- you need curated, guaranteed-clean data - some drawings may contain inappropriate content

## Facets
- artifact type: dataset
- maturity: stable
- function: machine-learning, data-science, data-visualization, computer-vision
- domain: machine-learning, data-science, computer-vision, artificial-intelligence
- platform: cross-platform, python
- tags: sketches, doodles, drawing-classification, ndjson, google-creative-lab, vector-data, open-data

## Member repositories
- googlecreativelab/quickdraw-dataset (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:48.702172+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:42:05.347865+00:00, confidence not recorded.
  - readme: https://github.com/googlecreativelab/quickdraw-dataset (fetched 2026-08-28T04:09:48.702172+00:00, sha 5161e4d77acc)
  - homepage: https://quickdraw.withgoogle.com/data (fetched 2026-08-29T08:38:29.082849+00:00, sha 91983bf546ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.