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:
- 644e60bb2d4b231b7eda65aca36f1ef86b32fe72a4d0a6f35d63f41b5613aac2
- Size of remote file:
- 36.9 MB
- SHA256:
- d79af2536c5df7e469f45bc9f739e1c8d9ba61897de57f836b6e363e3e3a0d1f
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