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End of preview. Expand in Data Studio
Rubik's Cube Oriented Bounding Box Detection
A dataset of 148 images (143 train / 5 val) of Rubik's cubes annotated with oriented bounding boxes (OBB) for color and face detection.
Classes
| ID | Name |
|---|---|
| 0 | Blue |
| 1 | Green |
| 2 | Orange |
| 3 | Red |
| 4 | White |
| 5 | Yellow |
| 6 | cube_face |
| 7 | side_face |
Note: class 7 (side_face) is effectively unused (only 1 instance in training).
Annotation Format
Each image has a corresponding list of objects. Each object is defined by:
category_id— integer class label (0–7)bbox— 8 normalized floats[x1, y1, x2, y2, x3, y3, x4, y4]representing the four corner points of an oriented (rotated) bounding box. All coordinates are normalized to [0, 1] relative to image width and height.
Dataset Structure
- train: 143 images, 1,706 annotated objects
- val: 5 images, 40 annotated objects
⚠️ The validation set is extremely small and shares scenery with training data. Evaluate with caution.
Usage
from datasets import load_dataset
ds = load_dataset("Alabi-Ayobami/rubiks-cube-obb")
# Access an image and its objects
example = ds["train"][0]
image = example["image"] # PIL Image
cats = example["objects_category_id"] # list[int]
bboxes = example["objects_bbox"] # list[list[float]] — each inner list has 8 floats
Model
A YOLOv8n-OBB model trained on this dataset achieved ~0.95 mAP@50.
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