---
license: mit
task_categories:
  - object-detection
tags:
  - yolo
  - obb
  - oriented-bounding-box
  - cubes
  - robotics
  - pick-and-place
pretty_name: Cube Detection on Monopoly Board Background (OBB)
size_categories:
  - n<1K
---

# Cube Detection on Monopoly Board Background (OBB)

A small oriented-bounding-box (OBB) detection dataset of colored cubes placed on a Movensys "Monopoly" board background. Intended for fine-tuning YOLO-style OBB detectors used in pick-and-place / robotic manipulation pipelines.

## Classes

| ID | Name         |
|----|--------------|
| 0  | green_cube   |
| 1  | yellow_cube  |
| 2  | blue_cube    |
| 3  | red_cube     |

## Splits

| Split | Images | Labels |
|-------|--------|--------|
| train | 104    | 104    |
| val   | 29     | 29     |
| test  | 16     | 16     |
| **total** | **149** | **149** |

## Image format

- Resolution: 1280 × 720, RGB JPEG
- Captured from a top-down camera over a printed Movensys Monopoly board, with colored cubes placed at varying positions and orientations

## 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`):
```
0 0.0522 0.1119 0.1013 0.0214 0.1529 0.1101 0.1038 0.2005
3 0.2423 0.0615 0.3122 0.0615 0.3122 0.1869 0.2423 0.1869
```

## Directory layout

```
.
├── dataset.yaml
├── train/
│   ├── images/   # *.jpg
│   └── labels/   # *.txt
├── val/
│   ├── images/
│   └── labels/
└── test/
    ├── images/
    └── labels/
```

## Usage

### Download

```bash
hf download movensys/cube-detection-monoply-background-obb \
    --repo-type dataset \
    --local-dir ./cube-detection-monoply-background-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/cube-detection-monoply-background-obb
train: train/images
val: val/images
test: test/images
names:
  0: green_cube
  1: yellow_cube
  2: blue_cube
  3: red_cube
```

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.