Instructions to use google/efficientnet-b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b4") 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-b4") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 292bb77ca0f62fce40183a9f0a670e49f2355ceefac516bd22bce7752735640e
- Size of remote file:
- 78.1 MB
- SHA256:
- 1d1a4d8dbb2acec2f437d386ccdc0e267c46596391284cdbe3fe4993b6f8df39
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