README.md

---
license: apache-2.0
task_categories:
- video-classification
- question-answering
tags:
- spatial-reasoning
- video-qa
- 3d-understanding
- arkitscenes
size_categories:
- 1K<n<10K
viewer: true
---

# Vsi_Mcq

## Dataset Description

This dataset contains **1000 video-based spatial reasoning questions** from the ARKitScenes dataset. Each sample includes a video showing an indoor scene and a question about spatial relationships between objects from a specific viewpoint.

## Dataset Structure

This dataset follows the VideoFolder format. The structure is:

```
dataset/
├── train/
│   ├── videos/
│   │   ├── video1.mp4
│   │   ├── video2.mp4
│   │   └── ...
│   └── metadata.csv
├── validation/
│   ├── videos/
│   │   ├── video1.mp4
│   │   └── ...
│   └── metadata.csv
└── README.md
```

### Metadata Format

Each `metadata.csv` contains:
- `file_name`: Path to the video file (relative to split directory)
- `original_file_name`: Original filename from source dataset
- `dataset_source`: Source dataset identifier
- `question_type`: Type of spatial reasoning question
- `question`: The question text with detailed instructions
- `options`: JSON string of multiple choice options
- `ground_truth`: Correct answer (A, B, C, or D)

## Question Types

The dataset includes the following spatial reasoning question types:

- `obj_appearance_order`
- `object_rel_direction_easy`
- `object_rel_direction_hard`
- `object_rel_direction_medium`
- `object_rel_distance`
- `route_planning`

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("advaitgupta/vsi_mcq")

# Access train/validation splits
train_dataset = dataset['train']
val_dataset = dataset['validation']

# Example: Get first sample
sample = train_dataset[0]
print(sample['question'])
print(sample['options'])
print(sample['ground_truth'])

# The video can be accessed via sample['file_name']
```

### Processing Videos

```python
import cv2
import json

# Load metadata to get video info
metadata = train_dataset.to_pandas()

# Process a video
video_path = metadata.iloc[0]['file_name']
question = metadata.iloc[0]['question']
options = json.loads(metadata.iloc[0]['options'])

# Load video with OpenCV
cap = cv2.VideoCapture(video_path)
# ... your video processing code
```

## Dataset Statistics

- **Total samples**: 1000
- **Video format**: MP4
- **Source datasets**: arkitscenes, scannet, scannetpp
- **Question types**: 6

## Example Questions

### Object Directional Reasoning (Medium)
*"If I am standing by the stove and facing the sofa, is the tv to my left, right, or back? An object is to my back if I would have to turn at least 135 degrees in order to face it."*

### Object Directional Reasoning (Hard)  
*"If I am standing by the bathtub and facing the table, is the toilet to my front-left, front-right, back-left, or back-right? The directions refer to the quadrants of a Cartesian plane (if I am standing at the origin and facing along the positive y-axis)."*

## Data Quality

- All video files have been validated to exist
- Metadata is consistent across all samples
- Questions include clear instructions for spatial reasoning

## Citation

If you use this dataset, please cite the original ARKitScenes paper:

```bibtex
@article{arkitscenes2021,
  title={3D Scene Understanding with ARKitScenes},
  author={Baruch, Gilad and others},
  journal={arXiv preprint arXiv:2111.08897},
  year={2021}
}
```

## License

This dataset is released under the Apache 2.0 license.