Instructions to use timm/efficientnet_b1.ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/efficientnet_b1.ft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/efficientnet_b1.ft_in1k", pretrained=True) - Transformers
How to use timm/efficientnet_b1.ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/efficientnet_b1.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/efficientnet_b1.ft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21a248835a01956334915d5c53b233229abec87bca2dc8861b379a614fbc90fd
- Size of remote file:
- 31.6 MB
- SHA256:
- 5fffd7b0d6fec411a55e95469e0e38a15a2eb3a49a72fa9ed7a09112e73cf417
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