Instructions to use timm/deit3_base_patch16_224.fb_in22k_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/deit3_base_patch16_224.fb_in22k_ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/deit3_base_patch16_224.fb_in22k_ft_in1k", pretrained=True) - Transformers
How to use timm/deit3_base_patch16_224.fb_in22k_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/deit3_base_patch16_224.fb_in22k_ft_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/deit3_base_patch16_224.fb_in22k_ft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a573b7d3264a2b6959d071f4d9ef38c9deb734e2734b8db00e1d469081581bf
- Size of remote file:
- 346 MB
- SHA256:
- fc4f78c5dd69903cc8aa8f22e2e9e604756873f47f04ce450ba1241fcfaf1fec
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