Instructions to use Adit-jain/soccana with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Adit-jain/soccana 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("Adit-jain/soccana", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download Model/train_batch51680.jpg from Adit-jain/soccana: direct link, hf CLI and curl.
- Browser
- Download file 334 kB
-
https://huggingface.co/Adit-jain/soccana/resolve/16f133f2ca92fd76b4f46cbdefad03f09d0d7bb5/Model/train_batch51680.jpg
- Command line
-
hf download hf://Adit-jain/soccana@16f133f2ca92fd76b4f46cbdefad03f09d0d7bb5/Model/train_batch51680.jpg
-
curl -L -o train_batch51680.jpg https://huggingface.co/Adit-jain/soccana/resolve/16f133f2ca92fd76b4f46cbdefad03f09d0d7bb5/Model/train_batch51680.jpg
334 kB

- Xet hash:
- ae8700165cdd4c05583f5b78de109736fba3801b2d98f10f82f487d3633f867c
- Size of remote file:
- 334 kB
- SHA256:
- 407a862a0fa250d50ec5bf2870ecac2eaf364526ed17e5828c3771aa3f75156f
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