Instructions to use jameslahm/lsnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use jameslahm/lsnet with timm:
import timm model = timm.create_model("hf_hub:jameslahm/lsnet", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d92dbe5a0e7ada25de965d82116560a537182d10d2ac93e5b90b1be10d3ddb0
- Size of remote file:
- 46.1 MB
- SHA256:
- a7cba4bf513af2a72c4b5cc5604f6de28df54aaac58aa4a4333634006faef687
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