---
license: cc-by-4.0
task_categories:
- image-to-text
- visual-question-answering
language:
- en
- de
- fr
- es
- it
- nl
tags:
- gui-grounding
- computer-use
- benchmark
- screenshots
- synthetic-data
- spreadsheets
- text-grounding
- professional-apps
pretty_name: Pointerbench
size_categories:
- 1K<n<10K
---

# Pointerbench

Pointerbench is a small GUI grounding benchmark suite for computer-use models.
Each example has one screenshot, one instruction, target geometry in absolute
pixels, and a binary evaluation rule.

Links:

- GitHub: https://github.com/warmwindOS/pointerbench
- Blog post: https://about.warmwind.com/pointer-bench/
- Pointer 1.5 post: https://about.warmwind.com/pointer-1-5-teaching-ai-to-click/
- Add your model to the official benchmark leaderboard: https://warmwind.com/contact
- 🔴 Placeholder: Pointer 1.5 model GitHub repository will be added later.

The suite has three subsets:

| Subset | Examples | What it tests |
| --- | ---: | --- |
| `pointerbench-sheets` | 500 | Spreadsheet cells, colors, headers, edges, corners, and relative positions |
| `pointerbench-text` | 500 | Words, characters, punctuation, caret positions, chrome text, text bounding boxes, and invoice fields |
| `pointerbench-pro` | 500 | Icons, text, and mixed GUI targets across 100 professional applications |

All images are synthetic 1024x768 PNG screenshots. The datasets contain no
scraped user data and no PII.

## Layout

Each subset is self-contained:

```text
pointerbench-sheets/
  data/test/metadata.jsonl
  data/test/0000.png
  eval.py
  README.md
  REPRODUCE.md

pointerbench-text/
  data/test/metadata.jsonl
  data/test/0000.png
  eval.py
  README.md
  REPRODUCE.md

pointerbench-pro/
  data/test/metadata.jsonl
  data/test/0000.png
  eval.py
  README.md
  REPRODUCE.md
```

## Schema

Each metadata row includes:

```json
{
  "file_name": "0000.png",
  "id": "pbs_0000",
  "instruction": "Click cell E11.",
  "bbox": [x1, y1, x2, y2],
  "point": [x, y],
  "answer_type": "point",
  "eval": {"type": "point_in_bbox", "bbox": [x1, y1, x2, y2]},
  "data_type": "cell",
  "category": "cell_ref",
  "image_size": [1024, 768]
}
```

Point tasks are correct when the predicted point lands inside the target bbox.
Bbox tasks, used in Pointerbench-Text, use an asymmetric overlap rule: a hit
requires the ground truth to be almost fully covered (coverage >= 0.90) and the
prediction to stay reasonably tight around it (precision >= 0.70). This
penalises predictions that cut off part of the target far more than predictions
that wrap it with some margin.

## Evaluation

Run the scorer inside a subset folder:

```bash
python eval.py --predictions preds.jsonl
```

Predictions are JSONL rows with an `id` and either a `point` or `bbox`, depending
on `answer_type`.

Recommended inference prompt:

```bash
python eval.py --show-system-prompt
```

```text
You are evaluating Pointerbench, a GUI grounding benchmark. You will receive one 1024x768 screenshot and one task instruction. Use absolute pixel coordinates with origin at the top-left of the image. Do not return normalized coordinates. Do not crop or resize the coordinate frame. For point tasks, return JSON like {"point": [x, y]}. For bounding-box tasks, return JSON like {"bbox": [x0, y0, x1, y1]}.
```

You can edit the prompt for your inference stack. Keep the 1024x768 absolute
pixel coordinate frame fixed, and report any image resizing or multi-step zoom
strategy with your results.

See each subset README for the exact distribution, schema details, and examples.

## License

Dataset images and annotations are released under CC BY 4.0. The included
evaluation scripts are released under MIT.