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.gitattributes

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LICENSE

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+MIT License
+
+Copyright (c) 2026 MOVENSYS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this dataset and associated documentation files (the "Dataset"), to deal
+in the Dataset without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Dataset, and to permit persons to whom the Dataset is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Dataset.
+
+THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS
+IN THE DATASET.

README.md

@@ -1,3 +1,153 @@
----
-license: mit
----
+---
+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},
+}
+```

dataset.yaml

@@ -0,0 +1,10 @@
+path: .
+train: train/images
+val: val/images
+test: test/images
+
+names:
+  0: green_cube
+  1: yellow_cube
+  2: blue_cube
+  3: red_cube

test/labels/00014.txt

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test/labels/00017.txt

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test/labels/00019.txt

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test/labels/00051.txt

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test/labels/00190.txt

@@ -0,0 +1,4 @@
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test/labels/00191.txt

@@ -0,0 +1,4 @@
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test/labels/00205.txt

@@ -0,0 +1,4 @@
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test/labels/00206.txt

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test/labels/00207.txt

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test/labels/00212.txt

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