creative-graphic-design/Magazine
Dataset Card for Magazine Dataset Summary Magazine is a magazine layout dataset released with Content-aware Generative Modeling of Graphic Design Layouts. The paper studies graphic layout generation conditioned on visual and textual content and introduces a large-scale magazine layout dataset with fine-grained layout annotations and keyword labels. Supported Tasks and Leaderboards The dataset supports content-aware layout generation, graphic… See the full description on the dataset page: https://huggingface.co/datasets/creative-graphic-design/Magazine.
Dataset Card for Magazine
 
Dataset Description
- Homepage: https://xtqiao.com/projects/contentawarelayout/
- Repository: https://github.com/creative-graphic-design/huggingface-datasets/tree/main/datasets/Magazine
- Hugging Face Dataset: https://huggingface.co/datasets/creative-graphic-design/Magazine
- Paper (SIGGRAPH 2019): https://dl.acm.org/doi/10.1145/3306346.3322971
Dataset Summary
Magazine is a magazine layout dataset released with Content-aware Generative Modeling of Graphic Design Layouts. The paper studies graphic layout generation conditioned on visual and textual content and introduces a large-scale magazine layout dataset with fine-grained layout annotations and keyword labels.
Supported Tasks and Leaderboards
The dataset supports content-aware layout generation, graphic layout modeling, and image-conditioned design generation. No public leaderboard is bundled with this Hugging Face packaging.
Languages
The source magazine pages and keywords are primarily English (en).
Dataset Structure
Data Fields
Each row contains filename, category, size, elements, keywords, and images.
Data Splits
Dataset Creation
The original release includes magazine images and fine-grained layout annotations for content-aware layout modeling.
Considerations for Using the Data
The dataset represents magazine layouts from the upstream collection and may not cover all editorial styles, languages, or publication domains.
Additional Information
Licensing Information
The dataset license is not specified in the local loader metadata. Users should verify the upstream terms before redistribution or commercial use.
Citation Information
@article{zheng2019content,
title={Content-aware generative modeling of graphic design layouts},
author={Zheng, Xinru and Qiao, Xiaotian and Cao, Ying and Lau, Rynson W. H.},
journal={ACM Transactions on Graphics},
volume={38},
number={4},
year={2019}
}Contributions
Thanks to the authors of the original Magazine layout dataset.
