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2
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Dataset Card for CGL-Dataset-v2

CI Sync HF

Dataset Summary

CGL-Dataset V2 is a dataset for the task of automatic graphic layout design of advertising posters, containing 60,548 training samples and 1035 testing samples. It is an extension of CGL-Dataset.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The language data in CGL-Dataset v2 is in Chinese (BCP-47 zh).

Dataset Structure

Data Instances

To use CGL-Dataset v2 dataset, you need to download RADM_dataset.tar.gz that includes the poster image, text and text features via JD Cloud or Google Drive. Then place the downloaded files in the following structure and specify its path.

/path/to/datasets
└── RADM_dataset.tar.gz
import datasets as ds

dataset = ds.load_dataset(
    path="shunk031/CGL-Dataset-v2",
    data_dir="/path/to/datasets/RADM_dataset.tar.gz",
    decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
    include_text_features=True, # True if RoBERTa-based text feature is to be loaded.
)

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

@inproceedings{li2023relation,
  title={Relation-Aware Diffusion Model for Controllable Poster Layout Generation},
  author={Li, Fengheng and Liu, An and Feng, Wei and Zhu, Honghe and Li, Yaoyu and Zhang, Zheng and Lv, Jingjing and Zhu, Xin and Shen, Junjie and Lin, Zhangang},
  booktitle={Proceedings of the 32nd ACM international conference on information & knowledge management},
  pages={1249--1258},
  year={2023}
}

Contributions

Thanks to @liuan0803 for creating this dataset.

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