---
annotations_creators:
  - crowdsourced
language:
  - zh
language_creators:
  - found
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
pretty_name: CGL-Dataset
size_categories:
  - 10K<n<100K
source_datasets:
  - original
tags:
  - graphic-design
  - poster
  - layout-generation
task_categories:
  - image-to-image
task_ids: []
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
  - config_name: ralf-style
    data_files:
      - split: train
        path: ralf-style/train-*
      - split: validation
        path: ralf-style/validation-*
      - split: test
        path: ralf-style/test-*
      - split: no_annotation
        path: ralf-style/no_annotation-*
dataset_info:
- config_name: default
  features:
  - name: image_id
    dtype: int64
  - name: file_name
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: image
    dtype: image
  - name: annotations
    sequence:
    - name: area
      dtype: int64
    - name: bbox
      sequence: int64
    - name: category
      struct:
      - name: category_id
        dtype: int64
      - name: name
        dtype:
          class_label:
            names:
              '0': logo
              '1': text
              '2': underlay
              '3': embellishment
              '4': highlighted text
      - name: supercategory
        dtype: string
  splits:
  - name: train
    num_bytes: 7727076720.09
    num_examples: 54546
  - name: validation
    num_bytes: 824988413.326
    num_examples: 6002
  - name: test
    num_bytes: 448856950.0
    num_examples: 1000
  download_size: 8848246626
  dataset_size: 9000922083.416
- config_name: ralf-style
  features:
  - name: image_id
    dtype: int64
  - name: file_name
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: original_poster
    dtype: image
  - name: inpainted_poster
    dtype: image
  - name: saliency_map
    dtype: image
  - name: saliency_map_sub
    dtype: image
  - name: annotations
    sequence:
    - name: area
      dtype: int64
    - name: bbox
      sequence: int64
    - name: category
      struct:
      - name: category_id
        dtype: int64
      - name: name
        dtype:
          class_label:
            names:
              '0': logo
              '1': text
              '2': underlay
              '3': embellishment
              '4': highlighted text
      - name: supercategory
        dtype: string
  splits:
  - name: train
    num_bytes: 29834119281.261364
    num_examples: 48438
  - name: validation
    num_bytes: 3722970297.954319
    num_examples: 6055
  - name: test
    num_bytes: 3701864874.9093184
    num_examples: 6055
  - name: no_annotation
    num_bytes: 448869325.0
    num_examples: 1000
  download_size: 37543869068
  dataset_size: 37707823779.125
---

# Dataset Card for CGL-Dataset

[![CI](https://github.com/creative-graphic-design/huggingface-datasets/actions/workflows/ci.yaml/badge.svg)](https://github.com/creative-graphic-design/huggingface-datasets/actions/workflows/ci.yaml)
[![Sync HF](https://github.com/creative-graphic-design/huggingface-datasets/actions/workflows/push_to_hub.yaml/badge.svg)](https://github.com/creative-graphic-design/huggingface-datasets/actions/workflows/push_to_hub.yaml)

## Dataset Description

- **Homepage:** https://github.com/minzhouGithub/CGL-GAN
- **Repository:** https://github.com/creative-graphic-design/huggingface-datasets/tree/main/datasets/CGLDataset
- **Hugging Face Dataset:** https://huggingface.co/datasets/creative-graphic-design/CGL-Dataset
- **Paper (arXiv):** https://arxiv.org/abs/2205.00303
- **Paper (IJCAI 2022):** https://www.ijcai.org/proceedings/2022/692

### Dataset Summary

CGL-Dataset is a poster layout dataset released with *Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs*. The paper studies layout generation for a given image, emphasizing that both global semantics and spatial image composition affect where graphic elements should be placed. The original dataset contains 60,548 advertising posters with annotated layout information.

### Supported Tasks and Leaderboards

The dataset supports poster layout generation and layout-conditioned graphic design modeling. No public leaderboard is bundled with this Hugging Face dataset.

### Languages

Poster text is primarily Chinese (`zh`).

## Dataset Structure

### Data Fields

The `default` config contains `image_id`, `file_name`, `width`, `height`, `image`, and COCO-style `annotations`.

The `ralf-style` config provides original posters, inpainted posters, saliency maps, and annotations for layout-generation pipelines.

### Data Splits

| Config | Split | Rows |
| --- | --- | ---: |
| default | train | 54,546 |
| default | validation | 6,002 |
| default | test | 1,000 |
| ralf-style | train | 48,438 |
| ralf-style | validation | 6,055 |
| ralf-style | test | 6,055 |
| ralf-style | no_annotation | 1,000 |

## Dataset Creation

The dataset was created for Composition-aware Graphic Layout GAN research. It provides visual element categories and positions for poster layout generation, enabling models to synthesize text and decorative layouts conditioned on image content rather than using template-only rules.

## Considerations for Using the Data

The data focuses on advertising poster layouts and may reflect the visual conventions of the source domain.

## Additional Information

### Licensing Information

The dataset card uses the CC BY-NC-SA 4.0 metadata from the local loader.

### Citation Information

```bibtex
@inproceedings{ijcai2022p692,
  title     = {Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs},
  author    = {Zhou, Min and Xu, Chenchen and Ma, Ye and Ge, Tiezheng and Jiang, Yuning and Xu, Weiwei},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {4995--5001},
  year      = {2022},
  doi       = {10.24963/ijcai.2022/692},
  url       = {https://doi.org/10.24963/ijcai.2022/692}
}
```

### Contributions

Thanks to [minzhouGithub](https://github.com/minzhouGithub) for creating the original dataset.