README.md

---
license: cc-by-4.0
task_categories:
- object-detection
language:
- en
tags:
- computer-vision
- object-detection
- yolo
- virtual-reality
- vr
- accessibility
- social-vr
pretty_name: DISCOVR - Virtual Reality UI Object Detection Dataset
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: class_id
      list: int64
    - name: center_x
      list: float32
    - name: center_y
      list: float32
    - name: width
      list: float32
    - name: height
      list: float32
  splits:
  - name: train
    num_bytes: 536592914
    num_examples: 15207
  - name: test
    num_bytes: 29938152
    num_examples: 839
  - name: validation
    num_bytes: 59182849
    num_examples: 1645
  download_size: 613839753
  dataset_size: 625713915
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
---

# DIgitial Social Context Objects in VR (DISCOVR): A Social Virtual Reality Object Detection Dataset

## Dataset Description

DISCOVR is an object detection dataset for identifying user interface elements and interactive objects in virtual reality (VR) and social VR environments. The dataset contains **17,691 annotated images** across **30 object classes** commonly found in 17 top social VR applications & VR demos.

This dataset is designed to support research in VR accessibility, automatic UI analysis, and assistive technologies for virtual environments.

### The entire dataset is available to download at once at https://huggingface.co/datasets/UWMadAbility/DISCOVR/blob/main/dataset.zip

### Pretrained YOLOv8 weights are available at https://huggingface.co/UWMadAbility/VRSight

### If you use DISCOVR in your work, please cite our work VRSight for which it was developed:

```bibtex
@inproceedings{killough2025vrsight,
  title={VRSight: An AI-Driven Scene Description System to Improve Virtual Reality Accessibility for Blind People},
  author={Killough, Daniel and Feng, Justin and Ching, Zheng Xue and Wang, Daniel and Dyava, Rithvik and Tian, Yapeng and Zhao, Yuhang},
  booktitle={Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology},
  pages={1--17},
  year={2025}
}
```

---

### Dataset Summary

- **Total Images:** 17,691
  - Training: 15,207 images
  - Validation: 1,645 images  
  - Test: 839 images
- **Classes:** 30 object categories
- **Format:** YOLOv8
- **License:** CC BY 4.0

## Object Classes

The dataset includes 30 classes of VR UI elements and interactive objects:

| ID | Class Name | Description |
|----|------------|-------------|
| 0 | avatar | User representations (human avatars) |
| 1 | avatar-nonhuman | Non-human avatar representations |
| 2 | button | Interactive buttons |
| 3 | campfire | Campfire objects (social gathering points) |
| 4 | chat box | Text chat interface elements |
| 5 | chat bubble | Speech/thought bubbles |
| 6 | controller | VR controller representations |
| 7 | dashboard | VR OS dashboard |
| 8 | guardian | Boundary/guardian system indicators (blue grid/plus signs) |
| 9 | hand | Hand representations |
| 10 | hud | Heads-up display elements |
| 11 | indicator-mute | Mute status indicators |
| 12 | interactable | Generic interactable objects |
| 13 | locomotion-target | Movement/teleportation targets |
| 14 | menu | Menu interfaces |
| 15 | out of bounds | Out-of-bounds warnings (red circle) |
| 16 | portal | Portal/doorway objects |
| 17 | progress bar | Progress indicators |
| 18 | seat-multiple | Multi-person seating |
| 19 | seat-single | Single-person seating |
| 20 | sign-graphic | Graphical signs |
| 21 | sign-text | Text-based signs |
| 22 | spawner | Object spawning points |
| 23 | table | Tables and surfaces |
| 24 | target | Target/aim points |
| 25 | ui-graphic | Graphical UI elements |
| 26 | ui-text | Text UI elements |
| 27 | watch | Watch/time displays |
| 28 | writing surface | Whiteboards/drawable surfaces |
| 29 | writing utensil | Drawing/writing tools |

## Dataset Structure

```
DISCOVR/
├── train/
│   ├── images/        # 15,207 training images (.jpg)
│   └── labels/        # YOLO format annotations (.txt)
├── validation/
│   ├── images/        # 1,645 validation images
│   └── labels/        # YOLO format annotations
├── test/
│   ├── images/        # 839 test images
│   └── labels/        # YOLO format annotations
└── data.yaml          # Dataset configuration file
```

### Annotation Format

Annotations are in YOLO format with normalized coordinates:
```
<class_id> <center_x> <center_y> <width> <height>
```

All coordinates are normalized to [0, 1] range relative to image dimensions.

---

## Usage

### With Hugging Face Datasets

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("UWMadAbility/DISCOVR")

# Access individual splits
train_data = dataset['train']
val_data = dataset['validation']
test_data = dataset['test']

# Example: Get first training image and its annotations
sample = train_data[0]
image = sample['image']
objects = sample['objects']

print(f"Number of objects: {len(objects['class_id'])}")
print(f"Class IDs: {objects['class_id']}")
print(f"Bounding boxes: {list(zip(objects['center_x'], objects['center_y'], objects['width'], objects['height']))}")
```

### With YOLOv8/Ultralytics

First, download the dataset and create a `data.yaml` file:

```yaml
path: ./DISCOVR
train: train/images
val: validation/images
test: test/images

nc: 30
names:
  0: avatar
  1: avatar-nonhuman
  2: button
  3: campfire
  4: chat box
  5: chat bubble
  6: controller
  7: dashboard
  8: guardian
  9: hand
  10: hud
  11: indicator-mute
  12: interactable
  13: locomotion-target
  14: menu
  15: out of bounds
  16: portal
  17: progress bar
  18: seat-multiple
  19: seat-single
  20: sign-graphic
  21: sign-text
  22: spawner
  23: table
  24: target
  25: ui-graphic
  26: ui-text
  27: watch
  28: writing surface
  29: writing utensil
```

Then train a model:

```python
from ultralytics import YOLO

# Load a pretrained model
model = YOLO('yolov8n.pt')

# Train the model
results = model.train(
    data='data.yaml',
    epochs=100,
    imgsz=640,
    batch=16
)

# Validate the model
metrics = model.val()

# Make predictions
results = model.predict('path/to/vr_image.jpg')
```

### With Transformers (DETR, etc.)

```python
from datasets import load_dataset
from transformers import AutoImageProcessor, AutoModelForObjectDetection

# Load dataset
dataset = load_dataset("UWMadAbility/DISCOVR")

# Load model and processor
processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")

# Process image
sample = dataset['train'][0]
inputs = processor(images=sample['image'], return_tensors="pt")

# Note: You'll need to convert YOLO format to COCO format for DETR
# YOLO: (center_x, center_y, width, height) normalized
# COCO: (x_min, y_min, width, height) in pixels
```

---

## Applications

This dataset can be used for:

- **VR Accessibility Research**: Automatically detecting and describing UI elements for users with disabilities
- **UI/UX Analysis**: Analyzing VR interface design patterns
- **Assistive Technologies**: Building screen readers and navigation aids for VR
- **Automatic Testing**: Testing VR applications for UI consistency
- **Content Moderation**: Detecting inappropriate content in social VR spaces
- **User Behavior Research**: Understanding how users interact with VR interfaces

## Citation

If you use this dataset in your research, please cite our publication using DISCOVR, called VRSight:

```bibtex
@inproceedings{killough2025vrsight,
  title={VRSight: An AI-Driven Scene Description System to Improve Virtual Reality Accessibility for Blind People},
  author={Killough, Daniel and Feng, Justin and Ching, Zheng Xue and Wang, Daniel and Dyava, Rithvik and Tian, Yapeng and Zhao, Yuhang},
  booktitle={Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology},
  pages={1--17},
  year={2025}
}
```

## License

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

You are free to:
- Share — copy and redistribute the material
- Adapt — remix, transform, and build upon the material

Under the following terms:
- Attribution — You must give appropriate credit

## Contact

For questions, issues, or collaborations:
- Main Codebase: https://github.com/MadisonAbilityLab/VRSight
- This Repository: [UWMadAbility/DISCOVR](https://huggingface.co/datasets/UWMadAbility/DISCOVR)
- Organization: UW-Madison Ability Lab

## Acknowledgments
This dataset was created by Daniel K., Justin, Daniel W., ZX, Ricky, Abhinav, and the MadAbility Lab at the University of Wisconsin-Madison to support research in VR accessibility and assistive technologies.