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:
- 07a14874c001a3161aab23711e0d4918192f7219957a60ca6e250eafd2fe55e9
- Size of remote file:
- 96.1 MB
- SHA256:
- 353a0cfff4010439d3da35127d698c876ffeb48dab2045912f2e7986aac1ddc8
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