Instructions to use jameslahm/lsnet_t with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use jameslahm/lsnet_t with timm:
import timm model = timm.create_model("hf_hub:jameslahm/lsnet_t", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f96a6760f1dc22a1f76f732fa11870f46adc851f15a17f07c9df031cacc4fc66
- Size of remote file:
- 46.2 MB
- SHA256:
- 9167eaf4c6d121f2d9a294e52e5c22994b3c16755773931bd74655d54384c93a
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