Instructions to use boxerab/gst-python-ml-football with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use boxerab/gst-python-ml-football 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("boxerab/gst-python-ml-football", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- football.onnx +3 -0
- football.pt +3 -0
- football_fp16.onnx +3 -0
- football_int8.onnx +3 -0
football.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:80c093f8f67e866232f3e31e71809071cf4f6c97914ab7c0cd82cbb8d6e30dfb
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size 101508645
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football.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:ffd531b4739e544b075479d6a41118931e82f8362d576258218e8fab2e4bdfa9
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size 51178706
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football_fp16.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e6589a1567088115f8e84564e938e09938f152b4b42902e525359bef601e350
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size 50859790
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football_int8.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:af48014db347b8821efd7d107819cec2959aeb99cbb99c272e6ea9e4bd938519
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size 30817706
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