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:
- 811c92f38cf32ab65420b3c0216fa32aad1dddf8966bf734cad629703ab87556
- Size of remote file:
- 94 MB
- SHA256:
- bcad7797ca2092616f539f6de19c4a642fb7b748974862c5b86f1206bd7953a4
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