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

import datasets as ds

dataset = ds.load_dataset("creative-graphic-design/Magazine")

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

Copyright (c) 2019, Xiaotian Qiao
All rights reserved.

This code is copyrighted by the authors and is for non-commercial research
purposes only.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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.