Instructions to use google/efficientnet-b2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b2") 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-b2") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dda398184fe21284b1618c6f4f96f866207d26e12187d91890f665688f14f987
- Size of remote file:
- 36.8 MB
- SHA256:
- 8630c0153a0cb6cfd26905b230e87124c509579c31ce1d64d050821ae3b37d36
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