Instructions to use google/efficientnet-b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("google/efficientnet-b1") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73d9abe17ee35de6fbd09b5690477f46971ef9deca6cedb961a65cab326b0291
- Size of remote file:
- 31.6 MB
- SHA256:
- d9133346b26bcac8fd22b3ca575c346b2280a6a41f2cffc99821899a57eed63f
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