Description:

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The Doodle Dataset Classifier Prepared Dataset comprises over 1 million images spanning 340 classes of doodles. Sourced from the Quick, Draw! dataset, it features grayscale images of hand-drawn sketches organized by class. Each image has been processed to facilitate machine learning tasks, ensuring a clean and easy-to-use version of the original dataset.

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Images: Grayscale images of doodles, each sized at 255×255 pixels.

Classes: 340 distinct categories of doodles, each stored in its own directory.

Total Images: 1,020,000 images, with each class containing precisely 3,000 images.

This refined dataset provides a manageable and structured subset of the original Quick, Draw! dataset by Google, which contains approximately 50 million images

This dataset is sourced from Kaggle.

The Sketch dataset contains over 20,000 sketches evenly distributed over 250 object categories.

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed past players of the game "Quick, Draw!". The drawings were captured equally timestamped vectors, tagged with metadata including what the player was asked to draw and in which state the player was located.\due north Case drawings: https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/preview.jpg" alt="preview">

Dataset

This dataset was created by Gaurav Dutta

Contents

ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. We construct the data set with Google Image queries "sketch of _", where _ is the standard class name. We only search within the "black and white" color scheme. We initially query 100 images for every class, and then manually clean the pulled images by deleting the irrelevant images and images that are for similar but different classes. For some classes, there are less than 50 images after manually cleaning, and then we augment the data set by flipping and rotating the images.

This is a collection of 257 generative art images created using a line drawing algorithm. Each image also includes a numeric score assigned by the generative artist who created the system. The score ranges from 0-5 and the higher the value the better this artist liked this image (in terms of its aesthetics).The images are in png format. The ratings are in a csv file with the number corresponding to the 4 digit number in the image name.The dataset is described in more detail in the forthcoming paper: J. McCormack & C. Cruz Gambardella: Quality-diversity for aesthetic evolution, EvoMUSART Conference Proceedings, 2022.

This dataset is designed to support research and experimentation in the intersection of art, technology, and interactive systems. It contains data generated from images belonging to five distinct art styles: Drawings and Watercolors, Paintings, Sculptures, Graphic Art, and Iconography (Russian Art). Each entry includes a unique Art ID, the corresponding art style, sensor readings (simulating environmental or system data), system status (indicating the state of the system at the time of interaction), interaction count (representing user interactions), and timestamps for each event or action.

The dataset is intended for use in analyzing the relationship between art styles and system interactions in embedded environments. It can be used for training machine learning models, exploring system optimization techniques, or developing creative technologies that merge artistic expression with digital interaction. The synthetic nature of the data allows for a wide range of exploratory tasks, including classification, anomaly detection, and time-series analysis, and is well-suited for applications in AI-driven creative industries.

Dataset Contents: Images: The dataset includes approximately 9,000 images of artwork across five categories:

Drawings and Watercolors Paintings Sculptures Graphic Art Iconography (Russian Art) These images are sourced from various online repositories and cover diverse styles and artistic expressions.

CSV File: A corresponding CSV file, art_data.csv, is provided, containing the following columns:

Art ID: A unique identifier for each artwork. Art Style: The category of the artwork (e.g., Drawings and Watercolors, Paintings, Sculptures, Graphic Art, Iconography). Sensor Reading: Numeric values representing sensor data (e.g., environmental or system measurements). System Status: The current state of the system (e.g., Active, Idle, Processed, or Error). Interaction Count: The number of interactions or views of the image. Timestamp: The timestamp indicating when the interaction or event occurred. The CSV file can be used for training, analysis, and developing machine learning models for interactive art systems, while the image dataset provides the visual content necessary for studying art in a digital context.

The DeepPatent2 dataset proceeds the DeepPatent dataset (Kucer et al. 2022) contains over 2 million design patent drawings obtained from the United States Patent and Trademark Office (USPTO) website, 2.8 million individual figures segmented from original patent drawings, their patent and figure level metadata, and semantic information automatically extracted using neural network models. The time spans from Year 2007 to 2020.

Scav6411/sketch-image-annotated-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

SketchyCOCO dataset consists of two parts:

Object-level data

Object-level data contains $20198(train18869+val1329)$ triplets of {foreground sketch, foreground image, foreground edge map} examples covering 14 classes, $27683(train22171+val5512)$ pairs of {background sketch, background image} examples covering 3 classes.

Scene-level data

Scene-level data contains $14081(train 11265 + val 2816)$ pairs of {foreground image&background sketch, scene image} examples, $14081(train 11265 + val 2816)$ pairs of {scene sketch, scene image} examples and the segmentation ground truth for $14081(train 11265 + val 2816)$ scene sketches. Some val scene images come from the train images of the COCO-Stuff dataset for increasing the number of the val images of the SketchyCOCO dataset.

Dataset Card for DEArt: Dataset of European Art

  Dataset Summary

DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural… See the full description on the dataset page: https://huggingface.co/datasets/biglam/european_art.

Paintings Collection

A diverse collection of paintings from artists throughout history

By Gove Allen [source]

About this dataset

The Paintings Dataset is a rich and diverse collection of various paintings from different artists spanning across multiple time periods. It includes a wide range of art styles, techniques, and subjects, providing an extensive resource for art enthusiasts, historians, researchers, and anyone interested in exploring the world of visual arts.

This dataset aims to capture the essence of artistic expression through its vast array of paintings. From classical masterpieces to contemporary works, it offers a comprehensive perspective on the evolution of artistic creativity throughout history.

Each record in this dataset represents an individual painting with detailed information such as artist's name, artwork title (if applicable), genre/style classification (e.g., landscape, portrait), medium (e.g., oil on canvas), dimensions (height and width), and provenance details if available. Additionally, some records may include additional metadata like the year or era in which the artwork was created.

By providing such comprehensive data about each painting included within this dataset, it enables users to study various aspects of art history. Researchers can analyze trends across different time periods or explore specific artistic movements by filtering the dataset based on genre or style categories. Art enthusiasts can also use this dataset to discover new artists or artworks that align with their interests.

This valuable collection appeals not only to those seeking knowledge or inspiration from renowned artworks but also encourages exploration into lesser-known pieces that may have been overlooked in mainstream discourse. It fosters engagement with cultural heritage while promoting diversity and inclusivity within the realm of visual arts.

Whether you are interested in studying classical works by universally acclaimed painters like Leonardo da Vinci or exploring modern expressions by emerging contemporary artists—this Paintings Dataset has something for everyone who appreciates aesthetics and enjoys unraveling stories through brushstrokes on canvas

How to use the dataset

How to Use the Paintings Dataset

Welcome to the Paintings Dataset! This dataset is a comprehensive collection of various paintings from different artists and time periods. It contains information about the artist, title, genre, style, and medium of each painting. Whether you are an art enthusiast, researcher, or just curious about paintings, this guide will help you navigate through this dataset easily.

1. Understanding the Columns

This dataset consists of several columns that provide detailed information about each painting. Here is a brief description of each column:

• Artist: The name of the artist who created the painting.
• Title: The title or name given to the artwork by the artist.
• Genre: The artistic category or subject matter depicted in the painting.
• Style: The specific artistic style or movement associated with the painting.
• Medium: The materials and techniques used by the artist to create the artwork.

2. Exploring Artists and Their Paintings

One interesting way to use this dataset is to explore individual artists and their artworks. You can filter by a specific artist's name in order to retrieve all their paintings included in this collection.

For example: If you are interested in exploring all paintings by Leonardo da Vinci, simply filter using Leonardo da Vinci in Artist column using your preferred data analysis tool.

3. Analyzing Painting Genres

The genre column allows you to analyze different categories within this collection of paintings. You can examine popular genres or compare them across different eras.

To analyze genres: - Get unique values for Genre column. - Count frequency for each genre value. - Visualize results using bar charts or other graphical representations.

You might discover which genres were more predominant during certain periods or which artists were known for specific subjects!

4. Investigating Artistic Styles

Similar to genres, artistic styles also play an essential role in the world of painting. This dataset includes various styles like Impressionism, Cubism, Realism, etc. By analyzing the artistic styles column, you can explore trends and shifts in artistic movements.

To investigate styles: - Get unique values for Style column. - Count frequency for each style value. - Visualize results using bar charts or other graphical...

This dataset is about books. It has 1 row and is filtered where the book is Sketch : step-by-step sketching for the absolute beginner. It features 7 columns including author, publication date, language, and book publisher.

Drawing

## Overview

Drawing is a dataset for object detection tasks - it contains Shapes annotations for 465 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 PKU Sketch Re-ID dataset is constructed by National Engineering Laboratory for Video Technology (NELVT), Peking University.

The dataset contains 200 persons, each of which has one sketch and two photos. Photos of each person were captured during daytime by two cross-view cameras. We cropped the raw images (or video frames) manually to make sure that every photo contains the one specific person. We have a total of 5 artists to draw all persons’ sketches and every artist has his own painting style.

Dataset Card for Sketch Scene Descriptions

Dataset used to train Sketch Scene text to image model We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert… See the full description on the dataset page: https://huggingface.co/datasets/zoheb/sketch-scene.

Dataset

This dataset was created by Vinoth Pandian

Released under CC BY-NC-SA 4.0

Contents

Context

The Metropolitan Museum of Art, better known as the Met, provides a public domain dataset with over 200,000 objects including metadata and images. In early 2017, the Met debuted their Open Access policy to make part of their collection freely available for unrestricted use under the Creative Commons Zero designation and their own terms and conditions.

Content

This dataset provides a new view to one of the world’s premier collections of fine art. The data includes both image in Google Cloud Storage, and associated structured data in two BigQuery two tables, objects and images (1:N). Locations to images on both The Met’s website and in Google Cloud Storage are available in the BigQuery table.

Fork this kernel to get started with this dataset.

https://cloud.google.com/blog/big-data/2017/08/images/150177792553261/met03.png" alt=""> https://cloud.google.com/blog/big-data/2017/08/images/150177792553261/met03.png

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:the_met

https://console.cloud.google.com/launcher/details/the-metropolitan-museum-of-art/the-met-public-domain-art-works

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.metmuseum.org/about-the-met/policies-and-documents/image-resources — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @danieltong from Unplash.

Inspiration

What are the types of art by department?

What are the earliest photographs in the collection?

What was the most prolific period for ancient Egyptian Art?

Objective: The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset for querying from a generic 3D model dataset. Introduction: Sketch-based 3D model retrieval is focusing on retrieving relevant 3D models using sketch(es) as input. This scheme is intuitive and convenient for users to learn and search for 3D models. It is also popular and important for related applications such as sketch-based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard. Please cite the papers: [1] B. Li, Y. Lu, Afzal Godil, Tobias Schreck, Masaki Aono, Henry Johan, Jose M. Saavedra, S. Tashiro, In: S. Biasotti, I. Pratikakis, U. Castellani, T. Schreck, A. Godil, and R. Veltkamp (eds.), SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2013 (3DOR 2013): 89-96, 2013. [2] B. Li, Y. Lu, A. Godil, T. Schreck, B. Bustos, A. Ferreira, T. Furuya, M.J. Fonseca, H. Johan, T. Matsuda, R. Ohbuchi, P.B. Pascoal, J.M. Saavedra, A comparison of methods for sketch-based 3D shape retrieval, Computer Vision and Image Understanding (2013), doi: http://dx.doi.org/10.1016/j.cviu.2013.11.008.

This dataset is about books. It has 1 row and is filtered where the book is Sketches for painting practice. It features 7 columns including author, publication date, language, and book publisher.