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:
- 51ad965fae5987f9ec2b2fd571436c86824c808df240a58ae2ffa502ae852d2b
- Size of remote file:
- 178 MB
- SHA256:
- 3b26b8ad7228cede39b5518cae70ad8a0e8eb53298477d957d08837507576d3a
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