Object Detection
ultralytics
ONNX
yolo11
onnxruntime
playing-cards
card-detection
casino
live-game-defender
Eval Results (legacy)
Instructions to use sroot/lgd-cards-gen3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use sroot/lgd-cards-gen3 with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("sroot/lgd-cards-gen3") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| train: | |
| - /home/novakj/projects/casino-table-monitor/vision/data/rf_ow27d_v4/train/images | |
| - /home/novakj/projects/casino-table-monitor/vision/runs/finetune/video-campaign-2/dataset/images/train | |
| - /home/novakj/projects/casino-table-monitor/vision/data/video_real/images/train | |
| - /home/novakj/projects/casino-table-monitor/vision/data/video_real_day2/images/train | |
| val: | |
| - /home/novakj/projects/casino-table-monitor/vision/data/video_real/images/val | |
| - /home/novakj/projects/casino-table-monitor/vision/data/video_real_day2/images/val | |
| names: | |
| 0: 10C | |
| 1: 10D | |
| 2: 10H | |
| 3: 10S | |
| 4: 2C | |
| 5: 2D | |
| 6: 2H | |
| 7: 2S | |
| 8: 3C | |
| 9: 3D | |
| 10: 3H | |
| 11: 3S | |
| 12: 4C | |
| 13: 4D | |
| 14: 4H | |
| 15: 4S | |
| 16: 5C | |
| 17: 5D | |
| 18: 5H | |
| 19: 5S | |
| 20: 6C | |
| 21: 6D | |
| 22: 6H | |
| 23: 6S | |
| 24: 7C | |
| 25: 7D | |
| 26: 7H | |
| 27: 7S | |
| 28: 8C | |
| 29: 8D | |
| 30: 8H | |
| 31: 8S | |
| 32: 9C | |
| 33: 9D | |
| 34: 9H | |
| 35: 9S | |
| 36: AC | |
| 37: AD | |
| 38: AH | |
| 39: AS | |
| 40: JC | |
| 41: JD | |
| 42: JH | |
| 43: JS | |
| 44: KC | |
| 45: KD | |
| 46: KH | |
| 47: KS | |
| 48: QC | |
| 49: QD | |
| 50: QH | |
| 51: QS | |