| license: mit | |
| task_categories: | |
| - object-detection | |
| tags: | |
| - yolo | |
| - computer-vision | |
| - pool | |
| - billiards | |
| size_categories: | |
| - n<1K | |
| # Pool Detection Dataset | |
| This is a YOLO format dataset for object detection tasks. | |
| ## Dataset Structure | |
| ``` | |
| pool/ | |
| train/ | |
| images/ | |
| labels/ | |
| valid/ | |
| images/ | |
| labels/ | |
| test/ | |
| images/ | |
| labels/ | |
| data.yaml | |
| ``` | |
| ## Classes | |
| The dataset contains 16 classes representing pool balls (numbered 0-15). | |
| ## Usage | |
| ### In Python | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("nhantu2107/pool-detection") | |
| ``` | |
| ### In Kaggle | |
| 1. Go to your Kaggle notebook | |
| 2. Click "Add data" -> "Hugging Face" | |
| 3. Search for "pool-detection" | |
| 4. Click "Add" | |
| ### With YOLOv8/YOLOv11 | |
| ```python | |
| from ultralytics import YOLO | |
| model = YOLO('yolov8n.pt') | |
| model.train(data='data.yaml', epochs=100) | |
| ``` | |
| ## Citation | |
| If you use this dataset, please cite appropriately. | |