Instructions to use google/efficientnet-b7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b7") 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-b7") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b7") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aae9328417e75282a068c53a6fb8f1ab1489c331e17375e2948c99a580c0ed8e
- Size of remote file:
- 267 MB
- SHA256:
- 1507b6ab500a3295efc33827882a30d33896c1d4cce78fae683829df53611cf6
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