---
license: apache-2.0
task_categories:
- visual-question-answering
- object-detection
language:
- en
tags:
- vision
- spatial-reasoning
- bounding-box
- vstar-bench
size_categories:
- n<1K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  - name: category
    dtype: string
  - name: question_id
    dtype: string
  - name: label
    dtype: string
  - name: bbox_target
    list:
      list: int64
  - name: bbox_category
    dtype: string
  - name: target_object
    dtype: 'null'
  - name: bbox_source
    dtype: string
  - name: original_image_size
    list: int64
  splits:
  - name: test
    num_bytes: 122037795
    num_examples: 191
  download_size: 121916164
  dataset_size: 122037795
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# vstar-bench with Bounding Box Annotations

## Dataset Description

This dataset extends [lmms-lab/vstar-bench](https://huggingface.co/datasets/lmms-lab/vstar-bench) by adding bounding box annotations for target objects. The bounding box information was extracted from [craigwu/vstar_bench](https://huggingface.co/datasets/craigwu/vstar_bench) and mapped to the lmms-lab version.

### Key Features

- **Visual Spatial Reasoning**: Tests understanding of spatial relationships in images
- **Bounding Box Annotations**: Each sample includes target object bounding boxes
- **Multiple Choice QA**: 4-way multiple choice questions about spatial relationships
- **High Coverage**: 100.0% of samples have bounding box annotations

## Dataset Statistics

- **Total Samples**: 191
- **Samples with Bbox**: 191
- **Coverage**: 100.0%

## Dataset Structure

Each sample contains:

- `image`: PIL Image object
- `text`: Question text (includes target object mention)
- `bbox_target`: Bounding box coordinates [x, y, width, height]
- `target_object`: Name of the target object
- `label`: Correct answer (A/B/C/D)
- `question_id`: Unique question identifier
- `category`: Question category (e.g., "relative_position")

### Example

```python
from datasets import load_dataset

dataset = load_dataset("jae-minkim/vstar-bench-with-bbox")
sample = dataset['test'][0]

print(f"Question: {sample['text']}")
print(f"Target Object: {sample['target_object']}")
print(f"Bounding Box: {sample['bbox_target']}")
print(f"Answer: {sample['label']}")
```

## Usage

### Load Dataset

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("jae-minkim/vstar-bench-with-bbox")

# Access test split
test_data = dataset['test']

# Iterate over samples
for sample in test_data:
    image = sample['image']
    bbox = sample['bbox_target']  # [x, y, width, height]
    question = sample['text']
    answer = sample['label']
```

### Visualize Bounding Boxes

```python
import matplotlib.pyplot as plt
import matplotlib.patches as patches

sample = dataset['test'][0]

fig, ax = plt.subplots(1, 1, figsize=(10, 8))
ax.imshow(sample['image'])

if sample['bbox_target']:
    x, y, w, h = sample['bbox_target']
    rect = patches.Rectangle(
        (x, y), w, h,
        linewidth=3, edgecolor='red', facecolor='none'
    )
    ax.add_patch(rect)
    ax.text(x, y-10, sample['target_object'], 
           color='red', fontsize=12, fontweight='bold')

ax.set_title(sample['text'])
ax.axis('off')
plt.show()
```

## Applications

This dataset is particularly useful for:

1. **Vision-Language Model Evaluation**: Testing spatial reasoning capabilities
2. **Attention Visualization**: Analyzing if models attend to correct regions
3. **Token Shifting Research**: Redirecting high-norm tokens to target regions
4. **Grounded QA**: Question answering with explicit spatial grounding

## Source Datasets

- **lmms-lab/vstar-bench**: Base dataset with questions and images
- **craigwu/vstar_bench**: Source of bounding box annotations

## Citation

If you use this dataset, please cite the original vstar-bench paper:

```bibtex
@article{wu2023vstar,
  title={V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs},
  author={Wu, Penghao and Xie, Saining},
  journal={arXiv preprint arXiv:2312.14135},
  year={2023}
}
```

## License

Apache 2.0 (following the original vstar-bench license)

## Acknowledgments

- Original dataset: [lmms-lab/vstar-bench](https://huggingface.co/datasets/lmms-lab/vstar-bench)
- Bounding box source: [craigwu/vstar_bench](https://huggingface.co/datasets/craigwu/vstar_bench)