Datasets:
File size: 3,433 Bytes
90e79ee f913f9e e53e45e f913f9e 90e79ee 2871c69 90e79ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | ---
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, and text bounding boxes |
| `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, are correct when the predicted bbox
reaches the configured IoU threshold.
## 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.
|