DOI: 10.5281/zenodo.15038713
title: Abstract Paintings Dataset

description: Description:







The Abstract Paintings Dataset is designed to aid machine learning enthusiasts and researchers in experimenting with Generative Adversarial Networks (GANs) or other creative AI models, specifically tailored for generating abstract artworks. Initially inspired by the challenges encountered when using landscape images as training data, this dataset represents an alternative approach by focusing on abstract art.

The dataset consists of images scraped, a well-known online resource for artistic images. The unique aspect of this dataset lies in its emphasis on abstract art, which often includes non-representational shapes, colors, and forms, offering a different kind of challenge and learning opportunity for AI models compared to more structured datasets like landscapes or portraits.







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The dataset contains anonymized images of abstract paintings, all organized within a single folder. Each image file is named using a numerical convention to maintain simplicity and efficiency during the training process. The first number in the filename refers to the artist’s serial number in the dataset, and the second number corresponds to the painting’s order on WikiArt.org. This numbering method facilitates easy sorting and management of the dataset for machine learning applications.

Expanded Details



Size of Dataset: The dataset includes hundreds of abstract paintings, providing a diverse range of artistic styles, brush strokes, and color palettes.

File Format: All images are store in a high-resolution format (e.g., JPG or PNG), ensuring that the dataset can be use for tasks requiring visual detail and clarity.

Data Collection: The images were, a platform known for housing extensive collections of fine art from various artists and genres. This ensures that the dataset covers a wide range of abstract styles.

Anonymization: In order to keep the focus on the artistic aspects rather than the identity of the artists. The dataset anonymizes all the images by assigning numbers to both artists and paintings. This allows researchers to focus on patterns in abstract art rather than specific creators.

Use Case: While the primary purpose of this dataset was for training GANs. It is also well-suit for other AI applications.  Including but not limited to, style transfer, image classification, art analysis, and art recommendation systems.


Potential Applications



Generative Adversarial Networks (GANs): Use this dataset to train models to generate new abstract paintings.  Potentially pushing the boundaries of AI creativity.

Style Transfer: Utilize the abstract images to experiment with transferring different artistic styles between images. Exploring how AI can mimic human creativity.

Classification & Clustering: This dataset can also be leverage for classification tasks.  Clustering abstract images into different categories base on color patterns, brush strokes, or other artistic features.

Art Recommendation Systems: The dataset can serve as a foundation for building recommendation systems. That suggest artwork based on user preferences for abstract styles.


Context

This dataset was originally created by a researcher seeking to improve their understanding of GANs.  After facing difficulties with landscape image datasets.  The focus shift to abstract paintings, which provide a different kind of visual complexity and randomness.  That could potentially enhance the learning process of generative models. By making this dataset publicly available. The creator hopes to contribute to the wider AI research community and spark further innovation in the field of generative art.

This dataset is sourced from Kaggle.