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 classifier-resnet50.pt from HaseLab/mahjong-models: direct link, hf CLI and curl.
- Browser
- Download file 94.7 MB
-
https://huggingface.co/HaseLab/mahjong-models/resolve/main/classifier-resnet50.pt
- Command line
-
hf download hf://HaseLab/mahjong-models/classifier-resnet50.pt
-
curl -L -o classifier-resnet50.pt https://huggingface.co/HaseLab/mahjong-models/resolve/main/classifier-resnet50.pt
94.7 MB
- Xet hash:
- 1c457f1e2e7cd7941e46df0cfae0e25c1f17b393388abdc496c0b6bb7305e716
- Size of remote file:
- 94.7 MB
- SHA256:
- 9137340ef518e33bfbe9893b0bfb69871465bed54103693d1f8a8408f7a3da19
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