UWMadAbility/VRSight
Object Detection
•
Updated
•
19
•
1
Datasets:
Search is not available for this dataset
image
imagewidth (px) 640
640
| objects
dict |
|---|---|
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End of preview. Expand
in Data Studio
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:
@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
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:
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:
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.)
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:
@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
- 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.
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