Instructions to use unionpoint/vit_small_plus_patch16_dinov3.ft_plantdoc_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use unionpoint/vit_small_plus_patch16_dinov3.ft_plantdoc_384 with timm:
import timm model = timm.create_model("hf_hub:unionpoint/vit_small_plus_patch16_dinov3.ft_plantdoc_384", pretrained=True) - Transformers
How to use unionpoint/vit_small_plus_patch16_dinov3.ft_plantdoc_384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="unionpoint/vit_small_plus_patch16_dinov3.ft_plantdoc_384") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("unionpoint/vit_small_plus_patch16_dinov3.ft_plantdoc_384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5da0b995e3ca08ff6c6995e9402fcd3d83c8abceb8c412d6217624aa14860bb2
- Size of remote file:
- 115 MB
- SHA256:
- 4178cb5b7c07d9d498c4b0f59be2dad5d1d39d89282bbbf9e44cd1743d032406
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