Datasetpublic

ChartFoundation/ChartStyle-100k

ChartStyle-100K   ChartStyle-100K is a large-scale training dataset for structured visualization style transfer. It accompanies the ECCV 2026 paper ChartStyle-100K: A Large-Scale Dataset for Structured Visualization Style Transfer. Each training example is a triplet: a style reference visualization, a content visualization, and the corresponding restyled target visualization. The goal is to train models that transfer visual style from the reference while… See the full description on the dataset page: https://huggingface.co/datasets/ChartFoundation/ChartStyle-100k.

sourceHugging Facecc-by-nc-4.0updated 3mo agoView on Hugging Face

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Dataset Card

ChartStyle-100K

ChartStyle-100K is a large-scale training dataset for structured visualization style transfer. It accompanies the ECCV 2026 paper ChartStyle-100K: A Large-Scale Dataset for Structured Visualization Style Transfer.

Each training example is a triplet: a style reference visualization, a content visualization, and the corresponding restyled target visualization. The goal is to train models that transfer visual style from the reference while faithfully preserving the content image's structure, text, and data-encoding geometry.

Quick Facts

—📄 Paper: ChartStyle-100K: A Large-Scale Dataset for Structured Visualization Style Transfer
—🏛️ Venue: ECCV 2026
—🎯 Task: exemplar-guided structured visualization style transfer
—🗂️ Split: train
—📊 Examples: 100,744 style-transfer triplets
—🖼️ Total images: 302,232 (3 per triplet)

Task Definition

Each training triplet consists of:

1.style_reference: a visualization image that defines the desired visual style;
2.content_image: a visualization image whose semantic content should be preserved;
3.target_image: the stylized visualization generated from style_reference, from which content_image is derived via restyling under a different visual family.

The triplets are constructed using a reverse-generation pipeline (ChartForge): the target is synthesized first from the style exemplar, and the content image is then produced by restyling the target, ensuring structural alignment between content and target at the data level.

Dataset Structure

text

ChartFoundation/ChartStyle-100k
├── README.md
├── preview/
│   └── preview-00000-of-00001.parquet   (100 samples for web preview)
└── data/
    ├── train-00000-of-00054.parquet
    ├── train-00001-of-00054.parquet
    ├── ...
    └── train-00053-of-00054.parquet

The train split contains 100,744 style-transfer triplets across 54 Parquet shards.

Field	Type	Description
`sample_id`	string	Sequential identifier from `000001` to `100744`.
`style_reference`	image	Reference visualization whose style should be transferred.
`content_image`	image	Input visualization whose content and structure should be preserved.
`target_image`	image	Pipeline-generated restyled visualization.
`content_type`	string	Coarse content family: `chart`, `flowchart`, `diagram`, or `table`.
`content_subject`	string	Thematic domain of the content visualization (e.g. `Finance`, `Biology`).

The image columns are stored as Hugging Face Image features and decode to PIL images by default.

Data Composition

Content Type Distribution

Content family	Count	Percentage
`chart`	76,122	75.6%
`diagram`	11,244	11.2%
`flowchart`	10,143	10.1%
`table`	3,235	3.2%
Total	100,744	100%

The chart category covers 36 fine-grained chart types including bar, pie, line, sankey, treemap, radar, violin, heatmap, and others. The flowchart, diagram, and table categories represent structural visualizations whose layout and topology must be preserved during style transfer.

Content Subject Distribution

The content visualizations span 26 academic and professional domains including Marketing, Psychology, Education, Biology, Finance, Physics, Engineering, Computer Science, and others, with a roughly uniform distribution across subjects.

Style References

The style references are drawn from multiple sources to maximize visual diversity:

—real-world chart images from Chart-Galaxy-Real;
—Canva design templates with diverse professional styles;
—synthesized visualizations with diverse styles produced by the ChartForge pipeline.

Loading

python

from datasets import load_dataset

# Quick preview (100 samples, shown in the Dataset Viewer)
preview = load_dataset("ChartFoundation/ChartStyle-100k", "preview", split="preview")

# Load the full training dataset (100,744 triplets)
dataset = load_dataset("ChartFoundation/ChartStyle-100k", "train", split="train")

sample = dataset[0]
style_reference = sample["style_reference"]   # PIL Image
content_image   = sample["content_image"]      # PIL Image
target_image    = sample["target_image"]       # PIL Image
content_type    = sample["content_type"]       # str
content_subject = sample["content_subject"]    # str

Save images:

python

style_reference.save("style_reference.png")
content_image.save("content_image.png")
target_image.save("target_image.png")

Relationship to ChartStyleBench

ChartStyle-100K is the training dataset, while ChartStyleBench is the held-out evaluation benchmark. The benchmark images in ChartStyleBench are manually collected and have no overlap with the training data in ChartStyle-100K.

Repository	Purpose	Size
ChartFoundation/ChartStyle-100k	Training data	100,744 triplets
ChartFoundation/ChartStyleBench	Evaluation benchmark	300 pairs

License

ChartStyle-100K is released under CC BY-NC 4.0.

<!--

📄 Citation

If you use ChartStyle-100K in your research or projects, please cite the following paper:

bibtex

@inproceedings{yang2026chartstyle100k,
  title     = {ChartStyle-100K: A Large-Scale Dataset for Structured Visualization Style Transfer},
  author    = {Yang, Yuwei and Xie, Tianchi and Ni, Jinhong and Guo, Yukai and Zhang, Jing and Zheng, Liang and Bai, Yalong and Yuan, Yuhui},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}

-->