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:
- 9b2cff4326b348ca89d1ad315480eab3dea64f664be76e7f090e108041b03d74
- Size of remote file:
- 111 MB
- SHA256:
- b84bcd99e0386c93f58fed72a83433a8fef59cc88b02dece853f379818359aba
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