| --- |
| 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}, |
| } |
| ``` |
|
|
