googlecreativelab/quickdraw-datasetArchived

Features

• Computer Vision Datasets - Provides a large-scale collection of categorized human sketches as image and vector data for training recognition models.
• Vector Drawing Dataset Downloads - Provides large collections of timestamped vector drawings across multiple categories in various formats for ML research.
• Coordinate Normalization Utilities - Rescales raw pixel coordinates to a consistent range to remove variance caused by different drawing screen sizes.
• Hand-Drawn Sketch Datasets - Offers millions of timestamped vector drawings and bitmaps specifically curated for training machine learning models.
• Neural Network Training Datasets - Supplies processed numpy and JSON data formatted for use in recurrent and convolutional neural networks.
• RNN Dataset Access - Provides a method to retrieve compressed numpy files formatted specifically for training recurrent neural networks.
• Sketch-Based Machine Learning - Developing neural networks that process vector stroke data or bitmap images to analyze human drawing patterns.
• Sketch Classifiers - Provides data for training models to recognize and classify hand-drawn sketches and symbols across hundreds of categories.
• Raw Data Retrieval - Retrieves unsimplified drawings containing pixel coordinates, timing information, and metadata in JSON format.
• Vector Data Processing - Provides a method to retrieve rescaled drawings processed to remove timing information for consistent input.
• Vector Drawing Corpora - Provides a massive archive of stroke-by-stroke drawing sequences and metadata for stroke generation research.
• 2D Vector Representations - Records drawings as sequences of coordinates and timestamps to preserve the temporal order of human sketching.
• Numpy Bitmap Access - Retrieves drawings rendered as grayscale bitmaps in a numpy format for use with image-based neural networks.
• Preprocessed ML Bitmaps - Retrieves simplified drawing data rendered as grayscale bitmaps for use in image-based classification models.
• Stroke Data Serializations - Retrieves compressed data formatted for recurrent neural networks to study stroke-by-stroke drawing generation.
• CNN Input Rasterization - Converts coordinate sequences into grayscale grids for compatibility with convolutional neural network input layers.
• Drawing Classifiers - Supports the development and evaluation of machine learning models designed to classify hand-drawn sketches.
• Data Preprocessing for Modeling - Converts raw drawing vectors into normalized formats or grayscale bitmaps for use in recurrent and convolutional networks.
• Pre-computed Dataset Archives - Distributes processed data in compressed numpy formats to reduce download time and memory overhead for researchers.
• Newline-Delimited JSON Streams - Stores large datasets as separate JSON objects per line to allow efficient streaming and partial file reading.
• Visual Cognition Research - Supports studying how people from different countries visualize common objects through timestamped vector drawings.
• Game Datasets - Large-scale collection of human-drawn sketches for machine learning.

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