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Commercial
Dataset with 30K Illustrations in 20 Artistic Styles
High-quality professional illustrations dataset containing 30,000 images across 20 diverse artistic styles, including 3D, Cartoon, Caricature, Graffiti, and more. The annotated images dataset includes metadata on style, color, keywords, and plot, providing rich training data for image classification, style transfer, and creative machine learning applications.
-
- images
- 30,000
-
- styles
- 20
- Classification
- Image Processing
- Machine Learning
- Data Collection
- Data Visualization
- Classification
- Image Processing
- Machine Learning
- Data Collection
- Data Visualization
High-quality professional illustrations dataset containing 30,000 images across 20 diverse artistic styles, including 3D, Cartoon, Caricature, Graffiti, and more. The annotated images dataset includes metadata on style, color, keywords, and plot, providing rich training data for image classification, style transfer, and creative machine learning applications.
- Classification
- Image Processing
- Machine Learning
- Data Collection
- Data Visualization
-
- images
- 30,000
-
- styles
- 20
Dataset Info
| Characteristic | Data |
| Description | Generated text to achieve higher performance in various NLP tasks AI-generated images of art |
| Data types | Image |
| Tasks | Image Classification, style transfer, content generation |
| Total number of files | 30,000 |
| Number of files in a set | 10 000 |
| Styles | 3D, Books, Caricature, Cartoon, Character, Children, Collage, Comics, Dark, Engraving, Fashion, Flora-fauna, Fun, Games, GIF, Graffiti, Graphics and etc |
| Labeling | Metadata (ID, is color, keywords list, plot, styles, using) |
Technical
Characteristics
| Characteristic | Data |
| File extension | PNG, JPG, JPEG |
| File resolution | Minimum: 1024×1024 px |
| Image source | 100% professionally created illustrations by real illustrators. |
Dataset Use Cases
FAQs
Can I request a sample of the dataset before purchasing or downloading it?
Yes. You can request a sample of the dataset to evaluate image quality, resolution, and diversity of artistic styles. This ensures that the dataset meets your requirements for training and testing AI or machine learning models.
What are the sources of data for Unidata datasets?
This dataset was created by professional illustrators and collected through a partner of Unidata. All images are original, high-quality illustrations, ensuring authenticity and creative integrity.
Does the dataset include image descriptions and style annotations?
Yes. Every illustration is accompanied by structured metadata, including style labels, keywords, plot descriptions, color information, and usage attributes. These annotations make the dataset valuable for multimodal AI, image captioning, prompt engineering, and text-to-image model training.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model, offering free samples for testing and evaluation, while the full dataset is available exclusively through purchase. This ensures both academic and commercial usage compliance.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets comply with GDPR and applicable data protection laws. Data is sourced ethically from professionally created illustrations, ensuring lawful and ethical use.
How are Unidata datasets stored?
Datasets are securely stored on AWS cloud infrastructure, providing high availability, scalability, and compliance with ISO 27001 and ISO 27701 standards. This ensures a secure and privacy-focused environment for handling data.
How long does it take to receive the dataset?
Once a request is submitted, Unidata reviews the details and completes the necessary documents. After signing and payment, the dataset is delivered within 3–10 business days.
Is it unique data?
Yes. This is a unique dataset of professionally created illustrations, not derived from publicly available sources. It is suitable for training high-performance AI models in artistic style recognition and content generation.
Still have questions about using Unidata datasets?
Read our user-guides
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