Datasets:
File size: 2,542 Bytes
8fed9dd 0308b8d 8fed9dd 0308b8d 8fed9dd | 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 | # VisToolBench Dataset
A benchmark dataset for evaluating vision-language models on tool-use tasks.
## Dataset Statistics
- **Total samples**: 1204
- **Single-turn**: 603
- **Multi-turn**: 601
## Schema
| Column | Type | Description |
|--------|------|-------------|
| `id` | string | Unique task identifier |
| `turncase` | string | Either "single-turn" or "multi-turn" |
| `num_turns` | int | Number of conversation turns (1 for single-turn) |
| `prompt_category` | string | Task category (e.g., "medical", "scientific", "general") |
| `eval_focus` | string | What aspect is being evaluated (e.g., "visual_reasoning", "tool_use") |
| `prompt` | string | The user prompt/question. For multi-turn, turns are prefixed with `[Turn N]` |
| `golden_answer` | string | The reference/ground-truth answer |
| `image` | Image | Primary image for the task (displayed in HF viewer) |
| `images` | List[Image] | All images associated with the task |
| `num_images` | int | Total number of images |
| `tool_trajectory` | string | JSON string of tool calls made (if applicable) |
| `rubrics` | string | JSON string of evaluation rubrics with weights and metadata |
## Rubrics Format
Each rubric entry contains:
- `description`: What the rubric evaluates
- `weight`: Importance weight (1-5)
- `objective/subjective`: Whether evaluation is objective or subjective
- `explicit/implicit`: Whether the answer is explicit or implicit in the image
- `category`: List of categories (e.g., "instruction following", "truthfulness")
- `critical`: Whether this is a critical rubric ("yes"/"no")
- `final_answer`: Whether this relates to the final answer ("yes"/"no")
## Usage
```python
from datasets import load_dataset
# Load the dataset
ds = load_dataset("path/to/dataset")
# Access a sample
sample = ds['train'][0]
print(sample['prompt'])
print(sample['image']) # PIL Image
# Parse rubrics
import json
rubrics = json.loads(sample['rubrics'])
for rubric_id, rubric in rubrics.items():
print(f"{rubric['description']} (weight: {rubric['weight']})")
```
## Splits
- `train`: Full dataset (1204 samples)
## Citation
```
@article{guo2025beyond,
title={Beyond seeing: Evaluating multimodal llms on tool-enabled image perception, transformation, and reasoning},
author={Guo, Xingang and Tyagi, Utkarsh and Gosai, Advait and Vergara, Paula and Park, Jayeon and Montoya, Ernesto Gabriel Hern{\'a}ndez and Zhang, Chen Bo Calvin and Hu, Bin and He, Yunzhong and Liu, Bing and others},
journal={arXiv preprint arXiv:2510.12712},
year={2025}
}
```
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