Instructions to use timm/focalnet_small_lrf.ms_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/focalnet_small_lrf.ms_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/focalnet_small_lrf.ms_in1k", pretrained=True) - Transformers
How to use timm/focalnet_small_lrf.ms_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/focalnet_small_lrf.ms_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/focalnet_small_lrf.ms_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e2461181addf5a4bf1c27c3c40813c4c2a2941b53264d407031ccb12a82f848
- Size of remote file:
- 202 MB
- SHA256:
- f616fc7706d7ad9fd833a4a8171b27e62e2917318a92324545dcac465ac0a3da
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