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:
- c3e41d1cfbf0608490abeed21c0d5b4726fbe9edeeeedd0d9ce7d13d76689fc6
- Size of remote file:
- 267 MB
- SHA256:
- deaabd166873c57ce324cedef66d1b74ce0ff360207707f5eec155f59c1ec802
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