Instructions to use unionpoint/tf_efficientnetv2_s.ft_plantdoc_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use unionpoint/tf_efficientnetv2_s.ft_plantdoc_384 with timm:
import timm model = timm.create_model("hf_hub:unionpoint/tf_efficientnetv2_s.ft_plantdoc_384", pretrained=True) - Transformers
How to use unionpoint/tf_efficientnetv2_s.ft_plantdoc_384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="unionpoint/tf_efficientnetv2_s.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/tf_efficientnetv2_s.ft_plantdoc_384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b63495801c51f1b51d4883e217f6de28b34ee43cbc5f3ec4c921c9bbf8831c8
- Size of remote file:
- 81.8 MB
- SHA256:
- eaeaab892ee247c5d786be39d5144b72f9195c89219d8df25d66e95625964e62
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