Datasets:
File size: 7,630 Bytes
f63b073 8fac124 f63b073 d759b41 ed7bbd4 1e55ba7 f63b073 5666da2 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | ---
pretty_name: OpenVisTool-42K
license: other
task_categories:
- image-text-to-text
language:
- en
tags:
- multimodal
- tool-use
- agentic-vision
- visual-reasoning
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/*.jsonl
---
# OpenVisTool-42K
OpenVisTool-42K contains 42,048 outcome-valid, tool-use-gain-filtered visual
tool-use trajectories across Chart, GUI Grounding, Table, Web-to-HTML, and
Visual Search. Each example preserves the teacher's reasoning, function calls,
tool observations, and final answer in the ms-swift agent format. All trajectories were synthesized using Qwen3.5-Plus
as the teacher model. The teacher generated the reasoning traces, function
calls, and final answers, while tool observations were obtained by executing
the corresponding tools. This dataset accompanies [OpenVisTool: An Open Recipe for Synthesizing Instructive Visual Tool-Use Trajectories](https://arxiv.org/abs/2608.08557).
## Record schema
Every line is one JSON object:
```json
{
"id": "chart-00000000",
"domain": "Chart",
"tools": "[{\"type\":\"function\",...}]",
"messages": [
{"role": "system", "content": "..."},
{"role": "user", "content": "<image>\n..."},
{"role": "assistant", "content": "<think>...</think>"},
{"role": "tool_call", "content": "{\"name\":\"crop\",...}"},
{"role": "tool_response", "content": "<image>\n..."}
],
"images": ["images/chart/ab/ab...ef.jpg"]
}
```
- `id`: stable ID derived from the row index in the published domain shard.
- `domain`: one of `Chart`, `GUI Grounding`, `Table`, `Web-to-HTML`, or
`Visual Search`.
- `tools`: a JSON-encoded string containing the function schemas expected by
ms-swift. A decoded copy is available as `metadata/tools.json`.
- `messages`: ordered agent messages. Roles are `system`, `user`, `assistant`,
`tool_call`, and `tool_response`.
- `images`: paths relative to the snapshot root. Entries align, in order, with
every `<image>` token across `messages`. Duplicate entries are intentional
when the same image is viewed more than once.
Paths such as `/mnt/data/example.png` inside messages are runtime paths in the
agent's sandbox. They are not paths to the downloaded snapshot and should not
be rewritten during training.
## Source composition and provenance
Only source images and queries were used as task input; pre-existing source
reasoning traces were not used as supervision. The teacher-generated
trajectories were selected by outcome validity and measured tool-use gain.
| Domain | Source dataset | Records | License |
|---|---|---:|---|
| Chart | [ChartVerse-SFT-600K](https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-600K) | 13,537 | Apache 2.0 |
| GUI Grounding | [AgentNet](https://huggingface.co/datasets/xlangai/AgentNet) | 1,170 | MIT |
| GUI Grounding | [OS-Atlas](https://huggingface.co/datasets/OS-Copilot/OS-Atlas-data) | 6,279 | Apache 2.0 |
| GUI Grounding | [UGround](https://huggingface.co/datasets/osunlp/UGround-V1-Data) | 3,517 | CC BY-NC-SA 4.0 |
| Table | [CoSyn-400K](https://huggingface.co/datasets/allenai/CoSyn-400K) | 4,645 | ODC-BY 1.0 |
| Table | [TABLET-Small](https://huggingface.co/datasets/alonsoapp/TABLET-Small) | 285 | CC BY 4.0 |
| Visual Search | [DeepEyesV2-RL](https://huggingface.co/datasets/honglyhly/DeepEyesV2_RL) | 738 | Not specified |
| Visual Search | [Vero-600K](https://huggingface.co/datasets/zlab-princeton/Vero-600k) | 1,203 | Apache 2.0 |
| Web-to-HTML | [VinciCoder-1.6M-SFT](https://huggingface.co/datasets/DocTron-Hub/VinciCoder-1.6M-SFT) | 10,674 | Not specified |
Licenses are those declared by the upstream dataset cards. Source-specific
terms continue to apply where an upstream dataset aggregates other datasets.
Tool-produced crops, masks, bounding-box visualizations, and HTML renderings
are packaged alongside original inputs because they are observations in the
training trajectories. These derivatives remain subject to any applicable
terms of the original image corpus.
# OpenVisTool-42K
OpenVisTool-42K contains 42,048 outcome-valid, tool-use-gain-filtered visual
tool-use trajectories across Chart, GUI Grounding, Table, Web-to-HTML, and
Visual Search. Each example preserves the teacher's reasoning, function calls,
tool observations, and final answer in the ms-swift agent format. All trajectories were synthesized using Qwen3.5-Plus
as the teacher model. The teacher generated the reasoning traces, function
calls, and final answers, while tool observations were obtained by executing
the corresponding tools.
## Record schema
Every line is one JSON object:
```json
{
"id": "chart-00000000",
"domain": "Chart",
"tools": "[{\"type\":\"function\",...}]",
"messages": [
{"role": "system", "content": "..."},
{"role": "user", "content": "<image>\n..."},
{"role": "assistant", "content": "<think>...</think>"},
{"role": "tool_call", "content": "{\"name\":\"crop\",...}"},
{"role": "tool_response", "content": "<image>\n..."}
],
"images": ["images/chart/ab/ab...ef.jpg"]
}
```
- `id`: stable ID derived from the row index in the published domain shard.
- `domain`: one of `Chart`, `GUI Grounding`, `Table`, `Web-to-HTML`, or
`Visual Search`.
- `tools`: a JSON-encoded string containing the function schemas expected by
ms-swift. A decoded copy is available as `metadata/tools.json`.
- `messages`: ordered agent messages. Roles are `system`, `user`, `assistant`,
`tool_call`, and `tool_response`.
- `images`: paths relative to the snapshot root. Entries align, in order, with
every `<image>` token across `messages`. Duplicate entries are intentional
when the same image is viewed more than once.
Paths such as `/mnt/data/example.png` inside messages are runtime paths in the
agent's sandbox. They are not paths to the downloaded snapshot and should not
be rewritten during training.
## Source composition and provenance
Only source images and queries were used as task input; pre-existing source
reasoning traces were not used as supervision. The teacher-generated
trajectories were selected by outcome validity and measured tool-use gain.
| Domain | Source dataset | Records | License |
|---|---|---:|---|
| Chart | [ChartVerse-SFT-600K](https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-600K) | 13,537 | Apache 2.0 |
| GUI Grounding | [AgentNet](https://huggingface.co/datasets/xlangai/AgentNet) | 1,170 | MIT |
| GUI Grounding | [OS-Atlas](https://huggingface.co/datasets/OS-Copilot/OS-Atlas-data) | 6,279 | Apache 2.0 |
| GUI Grounding | [UGround](https://huggingface.co/datasets/osunlp/UGround-V1-Data) | 3,517 | CC BY-NC-SA 4.0 |
| Table | [CoSyn-400K](https://huggingface.co/datasets/allenai/CoSyn-400K) | 4,645 | ODC-BY 1.0 |
| Table | [TABLET-Small](https://huggingface.co/datasets/alonsoapp/TABLET-Small) | 285 | CC BY 4.0 |
| Visual Search | [DeepEyesV2-RL](https://huggingface.co/datasets/honglyhly/DeepEyesV2_RL) | 738 | Not specified |
| Visual Search | [Vero-600K](https://huggingface.co/datasets/zlab-princeton/Vero-600k) | 1,203 | Apache 2.0 |
| Web-to-HTML | [VinciCoder-1.6M-SFT](https://huggingface.co/datasets/DocTron-Hub/VinciCoder-1.6M-SFT) | 10,674 | Not specified |
Licenses are those declared by the upstream dataset cards. Source-specific
terms continue to apply where an upstream dataset aggregates other datasets.
Tool-produced crops, masks, bounding-box visualizations, and HTML renderings
are packaged alongside original inputs because they are observations in the
training trajectories. These derivatives remain subject to any applicable
terms of the original image corpus. |