Instructions to use jameslahm/lsnet_b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use jameslahm/lsnet_b with timm:
import timm model = timm.create_model("hf_hub:jameslahm/lsnet_b", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76919b43839c02d6a50e04bb0c814326de4435ecff31f8a2d5d2a690deda3f1c
- Size of remote file:
- 94 MB
- SHA256:
- db9d9ebeff477790d2893ad6730bd170fc80177a09a912bcc36d30bd9a52cd62
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