Datasets:

movensys
/

cube-detection-obb

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.

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

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

pip install ultralytics huggingface_hub

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

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

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},
}