README.md

metadata

annotations_creators:
  - machine-generated
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories: []
source_datasets:
  - original
task_categories:
  - image-to-image
  - text-to-image
  - unconditional-image-generation
task_ids: []
pretty_name: Magazine
tags:
  - graphic design
  - layout
  - content-aware
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: filename
      dtype: string
    - name: category
      dtype:
        class_label:
          names:
            '0': fashion
            '1': food
            '2': news
            '3': science
            '4': travel
            '5': wedding
    - name: size
      struct:
        - name: width
          dtype: int64
        - name: height
          dtype: int64
    - name: elements
      sequence:
        - name: label
          dtype:
            class_label:
              names:
                '0': text
                '1': image
                '2': headline
                '3': text-over-image
                '4': headline-over-image
        - name: polygon_x
          sequence: float32
        - name: polygon_y
          sequence: float32
    - name: keywords
      sequence: string
    - name: images
      sequence: image
  splits:
    - name: train
      num_bytes: 4655342211.434
      num_examples: 3919
  download_size: 4652903538
  dataset_size: 4655342211.434

Dataset Card for Magazine dataset

Table of Contents

• Dataset Card Creation Guide
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: https://xtqiao.com/projects/content_aware_layout/
• Repository: https://github.com/shunk031/huggingface-datasets_Magazine
• Paper (SIGGRAPH2019): https://dl.acm.org/doi/10.1145/3306346.3322971

Dataset Summary

A large-scale magazine layout dataset with fine-grained layout annotations and keyword labeling.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

To use Magazine dataset, you need to download the image and layout annotations from the OneDrive in the official page. Then place the downloaded files in the following structure and specify its path.

/path/to/datasets
├── MagImage.zip
└── MagLayout.zip

import datasets as ds

dataset = ds.load_dataset(
    path="shunk031/Magazine",
    data_dir="/path/to/datasets/", # Specify the path of the downloaded directory.
)

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

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 WH},
  journal={ACM Transactions on Graphics (TOG)},
  volume={38},
  number={4},
  pages={1--15},
  year={2019},
  publisher={ACM New York, NY, USA}
}

Contributions

Thanks to Xinru Zheng and Xiaotian Qiao for creating this dataset.