README.md

---
license: odc-by
language:
- en
tags:
- svg
- svg-generation
- vision-language-model
- benchmark
pretty_name: VFIG-Bench
size_categories:
- n<1K
arxiv: 2603.24575
dataset_info:
- config_name: VFIG-Bench
  features:
  - name: filename
    dtype: string
  - name: svg
    dtype: string
  splits:
  - name: test
    num_bytes: 2532483
    num_examples: 400
  download_size: 756845
  dataset_size: 2532483
- config_name: VFIG-Bench-OOD
  features:
  - name: filename
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: test
    num_bytes: 23528361.0
    num_examples: 198
  download_size: 23321600
  dataset_size: 23528361.0
configs:
- config_name: VFIG-Bench
  data_files:
  - split: test
    path: VFIG-Bench/test-*
- config_name: VFIG-Bench-OOD
  data_files:
  - split: test
    path: VFIG-Bench-OOD/test-*
---

# VFIG-Bench Dataset Card

## Overview

**VFIG-Bench** is the evaluation benchmark accompanying [VFIG-Data](https://huggingface.co/datasets/QijiaHe/VFIG-Data), used to assess vision-language models on the task of vectorizing complex academic figures into SVG. It comprises two complementary test sets:

- **VFIG-Bench** — 400 in-distribution figure–SVG pairs (held-out from VFIG-Data).
- **VFIG-Bench-OOD** — 198 out-of-distribution figures (image-only, no SVG ground truth) sourced from well-known academic figures, used to probe generalization.

| Config | Split | Rows | Schema |
|---|---|---|---|
| `VFIG-Bench` | test | 400 | `{filename: string, svg: string}` |
| `VFIG-Bench-OOD` | test | 198 | `{filename: string, image: Image}` |

- **License:** ODC-BY 1.0
- **Paper:** [VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models](https://arxiv.org/abs/2603.24575)

## Usage

```python
from datasets import load_dataset

# In-distribution test (figure-SVG pairs)
ds = load_dataset("QijiaHe/VFIG-Bench", "VFIG-Bench", split="test")
print(ds[0]["filename"], len(ds[0]["svg"]))

# Out-of-distribution test (image only)
ds_ood = load_dataset("QijiaHe/VFIG-Bench", "VFIG-Bench-OOD", split="test")
print(ds_ood[0]["filename"], ds_ood[0]["image"].size)
```

## Notes on the OOD config

The `VFIG-Bench-OOD` config contains **only PNG images** — there is no SVG ground truth. It is intended for evaluating a model's ability to vectorize unseen, real-world academic figures.

## License

This dataset is licensed under ODC-BY 1.0. It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use).

## Citation

```bibtex
@misc{he2026vfigvectorizingcomplexfigures,
      title={VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models},
      author={Qijia He and Xunmei Liu and Hammaad Memon and Ziang Li and Zixian Ma and Jaemin Cho and Jason Ren and Daniel S Weld and Ranjay Krishna},
      year={2026},
      eprint={2603.24575},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.24575},
}
```