Instructions to use HaseLab/mahjong-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use HaseLab/mahjong-models with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("HaseLab/mahjong-models", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from HaseLab/mahjong-models: direct link, hf CLI and curl.
- Browser
- Download file 223 Bytes
-
https://huggingface.co/HaseLab/mahjong-models/resolve/main/.gitattributes
- Command line
-
hf download hf://HaseLab/mahjong-models/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/HaseLab/mahjong-models/resolve/main/.gitattributes
223 Bytes
| *.pt filter=lfs diff=lfs merge=lfs -text | |
| *.bin filter=lfs diff=lfs merge=lfs -text | |
| *.onnx filter=lfs diff=lfs merge=lfs -text | |
| *.mlmodel filter=lfs diff=lfs merge=lfs -text | |
| *.mlpackage/** filter=lfs diff=lfs merge=lfs -text | |