Instructions to use timm/caformer_b36.sail_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/caformer_b36.sail_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/caformer_b36.sail_in1k", pretrained=True) - Transformers
How to use timm/caformer_b36.sail_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/caformer_b36.sail_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/caformer_b36.sail_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f8a102bb5d88c3e098c499c10c538d5b200392ebaa44a5bc1631e97ab7f0a58
- Size of remote file:
- 395 MB
- SHA256:
- 78046f3d4a3679602219289a151c1cbb3100288ac12980e1baa837b4afa35a72
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