The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game "Quick, Draw!". The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.

Example drawings: https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/preview.jpg" alt="preview">

Dataset Card for Quick, Draw!

This is a processed version of Google's Quick, Draw dataset to be compatible with the latest versions of 🤗 Datasets that support .parquet files. NOTE: this dataset only contains the "preprocessed_bitmaps" subset of the original dataset.

• test_simplified.csv - the test data in the simplified vector format
• train_simplified - the training data in the simplified vector format; one csv file per word

Source: https://www.kaggle.com/c/quickdraw-doodle-recognition/data

Dataset

This dataset was created by Robbie Beane

Released under CC0: Public Domain

Contents

Dataset Description: Doodle Classifier Prepared Dataset

Overview

This dataset consists of over 1 million images covering 340 classes of doodles. It contains grayscale images of doodles, organized by class, extracted from the Quick, Draw! dataset. Each image represents a hand-drawn sketch from various categories, processed to be ready for machine learning tasks.

This dataset is a clean, processed, and easy-to-use version of the original Quick, Draw! dataset by Google, which has approximately 50 million images.

Content

• Images: Grayscale images of doodles, each 255x255 pixels.
• Classes: 340 categories of doodles, each stored in its directory.
• Total Images: 1,020,000 images, with each class containing exactly 3,000 images.

Structure

• Doodle Folder: Contains 340 subfolders, each representing a different doodle class. Each subfolder includes exactly 3,000 images.
• CSV File (master_doodle_dataframe.csv): Contains additional metadata about the images, including:
  • countrycode: The country code of the user who drew the doodle.
  • drawing: The drawing data is in JSON format.
  • key_id: Unique identifier for each doodle.
  • recognized: Boolean indicating whether the doodle was recognized.
  • word: The class label (e.g., "traffic light").
  • image_path: The file path where the image is stored.

Usage

This dataset is your playground for: - Training and evaluating machine learning models, especially for image classification tasks. - Conducting research and educational activities with a well-organized set of doodle images. - Benchmarking doodle recognition algorithms.

Acknowledgements

This dataset is a clean and processed version of the original Quick, Draw! dataset by Google, which contains approximately 50 million images. Special thanks to the original creators and contributors of the dataset.

License

This dataset is shared under the CC BY 4.0 license. Please attribute the source when using this dataset in your work.

Best of Luck

We hope this dataset serves as a valuable resource for your projects. Happy coding and may your models achieve high accuracy!

Dataset Card for Quick, Draw! Dataset

This dataset card aims to provide comprehensive information about the Quick, Draw! dataset, a collection of hand-drawn sketches used for training and evaluating sketch classification models.

  Dataset Details





  Dataset Description

The Quick, Draw! dataset is a large-scale collection of hand-drawn sketches curated by Google Creative Lab. The dataset includes over 50 million unique sketches across 345 object categories… See the full description on the dataset page: https://huggingface.co/datasets/sdiaeyu6n/quickdraw.

Description of the project This dataset is the result of the research carried out in the project "A Benchmark for Rey-Osterrieth Complex Figure (ROCF) Test Automatic Scoring", whose main goal was to establish a baseline for the scoring task consisting of: a dataset with 528 ROCF and results obtained by several deep learning models, as well as, by a group of psychology experts.

Go to http://on.ny.gov/1BbsWqI on the New York Lottery website for past Quick Draw results and payouts.

Draw Using Hands

## Overview

Draw Using Hands is a dataset for object detection tasks - it contains Detect Hands annotations for 5,108 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

Key points in sketches were connected with lines and convert to pixel images. The original dataset is Quick, Draw! Doodle Recognition Challenge

Content

500,000 images (as numeric arrays) and targets (as numeric labels) are compressed in h5 files.

QuickDrawImages31.h5 <= 'table', 'teapot', 'teddy-bear', 'telephone', 'television', 'tennis_racquet', 'tent', 'tiger', 'toaster', 'toe'

QuickDrawImages32.h5 <= 'toilet', 'tooth', 'toothbrush', 'toothpaste', 'tornado', 'tractor', 'traffic_light', 'train', 'tree', 'triangle'

QuickDrawImages33.h5 <= 'trombone', 'truck', 'trumpet', 'umbrella', 'underwear', 'van', 'vase', 'violin', 'washing_machine', 'watermelon'

QuickDrawImages34.h5 <= 'waterslide', 'whale', 'wheel', 'windmill', 'wine_bottle', 'wine_glass', 'wristwatch', 'yoga', 'zebra', 'zigzag'

Acknowledgments

All the thanks should be ended to the creators of the original data.

Inspiration

How does the converting key points into pixel images influence on classification?

This dataset was generated using Gemini 2.0 Flash to be used as an extra validation of submissions to the Drawing with LLMs competition

The source is a kaggle notebook created as Capstone project for the Gen AI Intensive Course 2025 Q1

This dataset tracks annual student-teacher ratio from 2006 to 2024 for Draw Academy vs. Texas and Draw Academy School District

🎨 Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

The interaction between humans and artificial intelligence (AI) is a crucial factor that reflects the effectiveness of multimodal large language models (MLLMs). However, current MLLMs primarily focus on image-level comprehension and limit interaction to textual instructions, thereby constraining their flexibility in usage and depth of response. Therefore, we introduce the… See the full description on the dataset page: https://huggingface.co/datasets/Afeng-x/Draw-and-Understand.

Draw Using Hanndss

## Overview

Draw Using Hanndss is a dataset for object detection tasks - it contains Hands annotations for 5,108 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Draw Works Market is projected to grow from USD 2.39 Billion in 2025 to USD 3.28 Billion by 2031 at a 5.42% CAGR.

Dataset used for the experiments of https://arxiv.org/abs/1907.06417 . This dataset is extracted from The Quick, Draw!
- A.I. Experiment. https://quickdraw.withgoogle.com/

Quick! Draw 26 Class Dataset

This dataset is derived from the Google Quick! Draw dataset and contains 26 classes of doodle images drawn by users. The classes include common objects and entities like animals, vehicles, food items, and everyday objects.

  Dataset Details

Number of Classes: 26 Total Images: 520,000 (416,000 train, 52,000 val, 52,000 test) Image Format: PNG images of size 28x28 pixels (grayscale) Data Fields: image: PIL Image object label: Integer label… See the full description on the dataset page: https://huggingface.co/datasets/OmAlve/quickdraw-26-classes.

Comprehensive YouTube channel statistics for draw with me drawing classes by suhan Shetty, featuring 804,000 subscribers and 176,901,209 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category. Track 4,927 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Eng Drawing

## Overview

Eng Drawing is a dataset for object detection tasks - it contains Objects annotations for 167 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Time-series player count data for the Roblox game Color Draw Simulator.