Datasets:
Re-release: asymmetric bbox rule, invoice section, icon targets, refreshed data
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +6 -3
- pointerbench-pro/README.md +16 -15
- pointerbench-pro/REPRODUCE.md +3 -0
- pointerbench-pro/__pycache__/eval.cpython-312.pyc +0 -0
- pointerbench-pro/data/test/metadata.jsonl +0 -0
- pointerbench-sheets/README.md +4 -4
- pointerbench-sheets/__pycache__/eval.cpython-312.pyc +0 -0
- pointerbench-sheets/data/test/0000.png +2 -2
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README.md
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@@ -43,7 +43,7 @@ The suite has three subsets:
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| Subset | Examples | What it tests |
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| --- | ---: | --- |
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| `pointerbench-sheets` | 500 | Spreadsheet cells, colors, headers, edges, corners, and relative positions |
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| `pointerbench-text` | 500 | Words, characters, punctuation, caret positions, chrome text,
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| `pointerbench-pro` | 500 | Icons, text, and mixed GUI targets across 100 professional applications |
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All images are synthetic 1024x768 PNG screenshots. The datasets contain no
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```
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Point tasks are correct when the predicted point lands inside the target bbox.
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Bbox tasks, used in Pointerbench-Text,
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## Evaluation
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| Subset | Examples | What it tests |
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| --- | ---: | --- |
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| `pointerbench-sheets` | 500 | Spreadsheet cells, colors, headers, edges, corners, and relative positions |
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| `pointerbench-text` | 500 | Words, characters, punctuation, caret positions, chrome text, text bounding boxes, and invoice fields |
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| `pointerbench-pro` | 500 | Icons, text, and mixed GUI targets across 100 professional applications |
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All images are synthetic 1024x768 PNG screenshots. The datasets contain no
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```
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Point tasks are correct when the predicted point lands inside the target bbox.
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Bbox tasks, used in Pointerbench-Text, use an asymmetric overlap rule: a hit
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requires the ground truth to be almost fully covered (coverage >= 0.90) and the
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prediction to stay reasonably tight around it (precision >= 0.70). This
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penalises predictions that cut off part of the target far more than predictions
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that wrap it with some margin.
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## Evaluation
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pointerbench-pro/README.md
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| element_type | count | example instruction |
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| ------------ | ----- | -------------------------------- |
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| `icon` | 190 | *Find a packet.*
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| `other` | 156 | *Select the red-coat clip.* |
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| `text` | 154 | *Switch to Home.* |
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- Multiple platforms (Windows, macOS, Linux, plus a few embedded/mobile).
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- Functional intent prompts (not literal element names), matching the
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ScreenSpot-Pro task style.
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## Schema
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```json
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{
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"file_name": "
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"id": "
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"instruction": "
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"bbox": [
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"point": [
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"answer_type": "point",
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"eval": {"type": "point_in_bbox", "bbox": [
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"data_type": "icon",
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"element_type": "icon",
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"app": "
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"app_slug": "
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"app_category": "development",
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"platform": "Windows
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"source_id": "
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"source_file": "clicks_icons_pro_3k.ndjson",
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"image_size": [1024, 768]
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}
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### Load the data
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Via HuggingFace `datasets`
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```python
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from datasets import load_dataset
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ds = load_dataset("
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ex = ds[0]
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ex["image"] # PIL.Image, 1024x768
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ex["instruction"] # "Begin a performance recording."
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title = {Pointerbench-Pro: A GUI Grounding Benchmark for Professional Software},
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author = {Pointerbench-Pro contributors},
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year = {2026},
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url = {https://github.com/
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}
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```
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| element_type | count | example instruction |
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| ------------ | ----- | -------------------------------- |
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| `icon` | 190 | *Select the icon that performs this action: Find a packet.* |
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| `other` | 156 | *Select the red-coat clip.* |
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| `text` | 154 | *Switch to Home.* |
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- Multiple platforms (Windows, macOS, Linux, plus a few embedded/mobile).
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- Functional intent prompts (not literal element names), matching the
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ScreenSpot-Pro task style. Icon rows explicitly ask for the icon itself so
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nearby text labels are not valid substitutes.
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## Schema
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```json
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{
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"file_name": "0005.png",
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"id": "pbp_0005",
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"instruction": "Select the icon that performs this action: Find a packet.",
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"bbox": [194, 47, 214, 70],
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"point": [204, 58],
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"answer_type": "point",
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"eval": {"type": "point_in_bbox", "bbox": [194, 47, 214, 70]},
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"data_type": "icon",
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"element_type": "icon",
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"app": "Wireshark",
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"app_slug": "wireshark",
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"app_category": "development",
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"platform": "Windows 10",
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"source_id": "images_wireshark_02_el4",
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"source_file": "clicks_icons_pro_3k.ndjson",
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"image_size": [1024, 768]
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}
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### Load the data
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Via HuggingFace `datasets`:
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```python
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from datasets import load_dataset
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ds = load_dataset("WarmwindOS/pointerbench", data_dir="pointerbench-pro", split="test")
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ex = ds[0]
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ex["image"] # PIL.Image, 1024x768
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ex["instruction"] # "Begin a performance recording."
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title = {Pointerbench-Pro: A GUI Grounding Benchmark for Professional Software},
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author = {Pointerbench-Pro contributors},
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year = {2026},
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url = {https://github.com/warmwindOS/pointerbench}
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}
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```
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pointerbench-pro/REPRODUCE.md
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(`clicks_dreamed_gui_intent.ndjson`).
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- Targets are labeled as `icon`, `text`, or `other` for analysis, but the public
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set does not force a fixed icon/text ratio.
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- Candidates are balanced across applications by round-robin selection with a
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fixed seed, then shuffled. The current 500-example set contains 190 icon, 154
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text, and 156 other targets across 100 applications.
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(`clicks_dreamed_gui_intent.ndjson`).
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- Targets are labeled as `icon`, `text`, or `other` for analysis, but the public
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set does not force a fixed icon/text ratio.
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- Icon-target instructions are rewritten with varied wrappers that explicitly
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ask for the icon itself, unless the original instruction already says "icon".
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This prevents nearby text labels from becoming plausible click targets.
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- Candidates are balanced across applications by round-robin selection with a
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fixed seed, then shuffled. The current 500-example set contains 190 icon, 154
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text, and 156 other targets across 100 applications.
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pointerbench-sheets/README.md
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### Load the data
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Via 🤗 `datasets`
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```python
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from datasets import load_dataset
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ds = load_dataset("
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ex = ds[0]
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ex["image"] # PIL.Image, 1024x768
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ex["instruction"] # "Click cell E33."
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so every ground-truth box is pixel-exact. The set is **held out**: it is built
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with a generation seed disjoint from any training data, so no benchmark sheet is
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reused for training. The generator and the exact build command live in the
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-
[source repo](https://github.com/
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[`REPRODUCE.md`](REPRODUCE.md).
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## Limitations
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title = {Pointerbench-Sheets: A GUI Grounding Benchmark for Spreadsheets},
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author = {Pointerbench-Sheets contributors},
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year = {2026},
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url = {https://github.com/
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}
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```
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### Load the data
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Via 🤗 `datasets`:
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```python
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from datasets import load_dataset
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ds = load_dataset("WarmwindOS/pointerbench", data_dir="pointerbench-sheets", split="test")
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ex = ds[0]
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ex["image"] # PIL.Image, 1024x768
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ex["instruction"] # "Click cell E33."
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so every ground-truth box is pixel-exact. The set is **held out**: it is built
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with a generation seed disjoint from any training data, so no benchmark sheet is
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reused for training. The generator and the exact build command live in the
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[source repo](https://github.com/warmwindOS/pointerbench); see
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[`REPRODUCE.md`](REPRODUCE.md).
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## Limitations
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title = {Pointerbench-Sheets: A GUI Grounding Benchmark for Spreadsheets},
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author = {Pointerbench-Sheets contributors},
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year = {2026},
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url = {https://github.com/warmwindOS/pointerbench}
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}
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```
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