---
license: mit
task_categories:
  - object-detection
tags:
  - yolo
  - obb
  - oriented-bounding-box
  - cubes
  - robotics
  - synthetic
size_categories:
  - n<1K
pretty_name: Colored Cubes OBB Detection
---

# Colored Cubes OBB Detection Dataset

A small object-detection dataset for **oriented bounding box (OBB)** detection of
four colored cubes (green, yellow, blue, red). Intended for training and
benchmarking YOLO-OBB style models in robotic-manipulation and pick-and-place
contexts.

## Dataset Summary

- **Task:** Oriented bounding box detection (4-point polygon per object)
- **Classes:** 4 — `green_cube`, `yellow_cube`, `blue_cube`, `red_cube`
- **Images:** 215 total · 1280×720 JPEG
- **Format:** Ultralytics YOLO-OBB
- **Splits:**

  | Split | Images | green | yellow | blue | red |
  |-------|-------:|------:|-------:|-----:|----:|
  | train |    150 |   150 |    153 |  147 | 150 |
  | val   |     43 |    43 |     44 |   42 |  43 |
  | test  |     22 |    22 |     22 |   22 |  22 |

  Every image contains all four cubes.

## Directory Layout

```
.
├── dataset.yaml          # Ultralytics data config
├── train/
│   ├── images/           # 00001.jpg …
│   └── labels/           # 00001.txt …
├── val/
│   ├── images/
│   └── labels/
└── test/
    ├── images/
    └── labels/
```

## Label Format

Each `labels/*.txt` has one object per line, in YOLO-OBB format:

```
class_id  x1 y1  x2 y2  x3 y3  x4 y4
```

- `class_id` — integer 0–3 (see `dataset.yaml`)
- `x*, y*` — polygon corner coordinates, **normalized** to `[0, 1]` by image
  width/height, traversed in order (TL → TR → BR → BL).

Example:

```
0 0.3460 0.5683  0.4078 0.5917  0.3890 0.7493  0.3271 0.7259
```

## Usage

### With Ultralytics YOLO

```bash
pip install ultralytics huggingface_hub
```

```python
from huggingface_hub import snapshot_download
from ultralytics import YOLO

local_dir = snapshot_download(
    repo_id="<your-username>/cubes-obb",
    repo_type="dataset",
)

model = YOLO("yolo11n-obb.pt")
model.train(data=f"{local_dir}/dataset.yaml", epochs=100, imgsz=1280)
```

### Loading labels manually

```python
from pathlib import Path

def load_obb(label_path):
    out = []
    for line in Path(label_path).read_text().splitlines():
        parts = line.split()
        cls = int(parts[0])
        coords = list(map(float, parts[1:]))  # 8 floats
        out.append((cls, coords))
    return out
```

## Class Mapping

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

## Author

Mohsin Ali — [Movensys](https://movensys.com)

## Collection & Annotation

Images were captured for a cube pick-and-place / OBB-detection research
workflow. Labels are in Ultralytics YOLO-OBB polygon format.

## Limitations

- **Small scale (215 images).** Fine for fine-tuning a pretrained OBB model,
  too small to train from scratch.
- **Every image contains all four cubes in similar scenes.** Models trained
  here may not generalize to scenes with missing cubes, unseen backgrounds,
  occlusion, or varying lighting.
- **Single resolution (1280×720).** Resize / letterbox if your pipeline
  expects another size.

## License

Released under the MIT License. See `LICENSE`.

## Citation

If you use this dataset, please cite:

```
@misc{cubes_obb_dataset,
  title        = {Colored Cubes OBB Detection Dataset},
  author       = {Mohsin Ali},
  year         = {2026},
  howpublished = {Hugging Face Datasets},
}
```