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
Download lsnet_s_distill.pth from jameslahm/lsnet: direct link, hf CLI and curl.
- Browser
- Download file 67 MB
-
https://huggingface.co/jameslahm/lsnet/resolve/main/lsnet_s_distill.pth
- Command line
-
hf download hf://jameslahm/lsnet/lsnet_s_distill.pth
-
curl -L -o lsnet_s_distill.pth https://huggingface.co/jameslahm/lsnet/resolve/main/lsnet_s_distill.pth
67 MB
- Xet hash:
- 7873e9f78fe705d28ab0bed0cab60d39efdc4746c118f11cb08162af4bd75365
- Size of remote file:
- 67 MB
- SHA256:
- f6dd8268738fa5ef41319260f0f5a09806fb49b59d4ec78bdbddd57fc98770d2
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