README.md

---
license: other
license_name: research-only
license_link: LICENSE
language:
- zh
task_categories:
- object-detection
- image-to-text
tags:
- vlm
- ood
- bbox
- grounding
- chinese
- scanned-document
- historical
pretty_name: gansulishuzhi — VLM figure-bbox detection on 1970s scanned book pages
size_categories:
- n<1K
---

# gansulishuzhi

A small out-of-distribution benchmark for vision-language model **figure-bbox
detection**. The input is a 200 DPI scan of a 1970s Chinese botanical book
page; the model must output pixel-coordinate bounding boxes for every figure
on the page (photos, line drawings, illustrations). No OCR is evaluated.

- **61 pages**, **64 figures**, all bboxes hand-verified
- **Page resolution**: 1395×2037 px (200 DPI render)
- **Language**: Chinese (scan content; English / Chinese prompt examples below)

## Why OOD

Modern VLM grounding stacks train on clean web documents. These pages have:

- 50-year-old print, ink bleed, scanner noise
- Mixed figure styles: black-and-white photos, line-drawn botanical
  illustrations, small inset diagrams
- Handwritten margin notes
- Irregular layout — captions and body text interleaved, page numbers,
  section headings

It's a narrow but unforgiving probe of whether a VLM's grounding generalises
beyond clean modern documents.

## Files

```
manifest.jsonl     61 rows, one per page
pages/             page_<NNN>.png   — model input
overlays/          page_<NNN>.png   — same image with red GT bboxes (visual QA)
```

## Manifest schema

`manifest.jsonl`, one JSON object per line:

```json
{
  "id": "page_014",
  "page": 14,
  "image_path": "pages/page_014.png",
  "image_size_px": [1395, 2037],
  "gt_bboxes": [[168, 264, 719, 1055], [456, 1199, 1247, 1751]]
}
```

`gt_bboxes` is `[[x0, y0, x1, y1], ...]` in image-pixel coords, origin
top-left, (x0,y0) = top-left, (x1,y1) = bottom-right.

## How to load

```python
from huggingface_hub import snapshot_download
import json
from pathlib import Path

root = Path(snapshot_download(repo_id="yunfengwang/gansulishuzhi", repo_type="dataset"))
samples = [json.loads(l) for l in (root / "manifest.jsonl").read_text().splitlines() if l.strip()]
print(samples[0])
# image at: root / samples[0]["image_path"]
```

## Suggested prompt

Ask the model for a JSON list of `[x0, y0, x1, y1]` integer pixel coords at
the actual image resolution. A working Chinese prompt:

> 这是一页扫描书页，可能包含 0 张、1 张或多张图（照片、线描、插图等，不含正文/标题/页眉/页脚/表格）。
> 请检测页面中每一张图的边界框，以 JSON 列表输出，每个元素为 `[x0, y0, x1, y1]` 整数像素坐标。
> 坐标原点在图像左上角，x 向右、y 向下；相对当前图像分辨率 (W×H)。仅框住图本身，不要包含 caption 文字或周围正文。若页面无图，输出 `[]`。
> 只输出 JSON，不要任何解释或代码块标记。

## Recommended metric

Greedy max-IoU match between predicted and ground-truth bboxes. For threshold
T: a matched pair counts as TP iff its IoU ≥ T. Report micro precision /
recall / F1 at IoU 0.3 / 0.5 / 0.7, plus mean IoU of matched pairs. No mAP
because the suggested output schema has no per-bbox confidence.

A reference implementation lives in the companion code repo:
[github.com/vra/gansulishuzhi](https://github.com/vra/gansulishuzhi)
(`uv sync && uv run eval.py --model-id <id> --output runs/<name>.jsonl`).

## License

The page images are scans of a 1970s Chinese reference work on Gansu pear
cultivars. They are provided **for non-commercial research use only**.
Downstream users are responsible for compliance with applicable copyright
law in their jurisdiction. The accompanying manifest, code, and metrics are
released under the MIT license.

## Citation

If you use this dataset, please cite the companion repo:

```bibtex
@misc{gansulishuzhi2026,
  title  = {gansulishuzhi: An OOD benchmark for VLM figure-bbox detection on 1970s scanned Chinese book pages},
  author = {Wang, Yunfeng},
  year   = {2026},
  url    = {https://huggingface.co/datasets/yunfengwang/gansulishuzhi}
}
```