Datasets:
File size: 2,532 Bytes
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license: mit
task_categories:
- object-detection
tags:
- yolo
- obb
- oriented-bounding-box
- dice
- pip-classification
- robotics
- pick-and-place
pretty_name: Dice Detection (OBB) with Pip Classification
size_categories:
- n<1K
---
# Dice Detection (OBB) with Pip Classification
A small oriented-bounding-box (OBB) detection dataset of a wooden die captured from a top-down camera. Each annotation gives the die's rotated bounding box along with the visible pip count (1–6) as the class. Intended for fine-tuning YOLO-style OBB detectors used in pick-and-place / robotic manipulation pipelines.
## Classes
| ID | Name |
|----|-------|
| 0 | Five |
| 1 | Four |
| 2 | One |
| 3 | Six |
| 4 | Three |
| 5 | Two |
(Class IDs are not in numerical order — they follow `dataset.yaml` as released. The class name corresponds to the pip count shown on the die's top face.)
## Splits
| Split | Images | Labels |
|-------|--------|--------|
| train | 125 | 125 |
| val | 36 | 36 |
| test | 19 | 19 |
| **total** | **180** | **180** |
Each image contains a single die.
## Image format
- Resolution: 1280 × 720, RGB JPEG
- Captured from a top-down camera over a green tray, with a Movensys "Monopoly" board visible alongside the workspace
## Label format
YOLO OBB — one row per object, 9 values:
```
class_id x1 y1 x2 y2 x3 y3 x4 y4
```
All polygon coordinates are normalized to `[0, 1]` relative to image width/height. Vertices are given in order around the box.
Example (`train/labels/00001.txt`):
```
2 0.4608 0.4944 0.5353 0.5021 0.5307 0.6450 0.4562 0.6374
```
## Directory layout
```
.
├── dataset.yaml
├── train/
│ ├── images/ # *.jpg
│ └── labels/ # *.txt
├── val/
│ ├── images/
│ └── labels/
└── test/
├── images/
└── labels/
```
## Usage
### Download
```bash
hf download movensys/dice-detection-obb \
--repo-type dataset \
--local-dir ./dice-detection-obb
```
### Train with Ultralytics YOLO (OBB)
After download, update the `path:` field in `dataset.yaml` to point at the local copy:
```yaml
path: /absolute/path/to/dice-detection-obb
train: train/images
val: val/images
test: test/images
names:
0: Five
1: Four
2: One
3: Six
4: Three
5: Two
```
Then:
```python
from ultralytics import YOLO
model = YOLO("yolo11n-obb.pt")
model.train(data="dataset.yaml", epochs=100, imgsz=1280)
```
## License
Released under the MIT License.
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