Datasetpublic

movensys/dice-detection-obb

Dice Detection (OBB) with Pip Classification A small oriented-bounding-box (OBB) detection dataset of a wooden die captured from a top-down camera. Each annotation gives the die's rotated bounding box along with the visible pip count (1–6) as the class. Intended for fine-tuning YOLO-style OBB detectors used in pick-and-place / robotic manipulation pipelines. Classes ID Name 0 Five 1 Four 2 One 3 Six 4 Three 5 Two (Class IDs are not in… See the full description on the dataset page: https://huggingface.co/datasets/movensys/dice-detection-obb.

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Dataset Card

Dice Detection (OBB) with Pip Classification

A small oriented-bounding-box (OBB) detection dataset of a wooden die captured from a top-down camera. Each annotation gives the die's rotated bounding box along with the visible pip count (1–6) as the class. Intended for fine-tuning YOLO-style OBB detectors used in pick-and-place / robotic manipulation pipelines.

Classes

ID	Name
0	Five
1	Four
2	One
3	Six
4	Three
5	Two

(Class IDs are not in numerical order — they follow dataset.yaml as released. The class name corresponds to the pip count shown on the die's top face.)

Splits

Split	Images	Labels
train	125	125
val	36	36
test	19	19
total	180	180

Each image contains a single die.

Image format

—Resolution: 1280 × 720, RGB JPEG
—Captured from a top-down camera over a green tray, with a Movensys "Monopoly" board visible alongside the workspace

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):

2 0.4608 0.4944 0.5353 0.5021 0.5307 0.6450 0.4562 0.6374

Directory layout

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

Usage

Download

bash

hf download movensys/dice-detection-obb \
    --repo-type dataset \
    --local-dir ./dice-detection-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/dice-detection-obb
train: train/images
val: val/images
test: test/images
names:
  0: Five
  1: Four
  2: One
  3: Six
  4: Three
  5: Two

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.