ARG-NCTU/spscd_plus_buoy_yolo

YOLO Object Detection Dataset

SPSCD + SMD buoy only

Dataset Information

• Number of Classes: 13
• Format: YOLO (ultralytics)
• Splits: train, validation, test

Classes

0. Small Craft
1. Small Fishing Boat
2. Small Passenger Ship
3. Fishing Trawler
4. Large Passenger Ship
5. Sailing Boat
6. Speed Craft
7. Motorboat
8. Pleasure Yacht
9. Medium Ferry
10. Large Ferry
11. High Speed Craft
12. Buoy

Dataset Structure

dataset/
├── dataset.yaml          # Dataset configuration
├── train/
│   ├── images/          # Training images
│   └── labels/          # Training labels (YOLO format)
├── validation/
│   ├── images/          # Validation images
│   └── labels/          # Validation labels
└── test/
    ├── images/          # Test images
    └── labels/          # Test labels

YOLO Label Format

Each .txt file contains annotations in YOLO format:

<class_id> <x_center> <y_center> <width> <height>

All values are normalized to [0, 1] relative to image dimensions.

Usage

With Ultralytics

from ultralytics import RTDETR

# Train
model = RTDETR('rtdetr-l.pt')
model.train(data='dataset.yaml', epochs=100)

# Validate
model.val()

With Hugging Face Datasets

from datasets import load_dataset

# Load dataset
dataset = load_dataset('your-username/your-dataset')

# Access splits
train = dataset['train']
val = dataset['validation']
test = dataset['test']

Citation

If you use this dataset, please cite:

@dataset{your_dataset,
  title={Your Dataset Name},
  author={Your Name},
  year={2025}
}