Update README as datacard, version 1

README.md

@@ -1,40 +1,295 @@
----
-license: cc-by-4.0
-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-*
----
+---
+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-*
+---
+
+# DISCOVR: Virtual Reality UI 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 VR applications.
+
+This dataset is designed to support research in VR accessibility, automatic UI analysis, and assistive technologies for virtual environments.
+
+### 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.