Instructions to use google/efficientnet-b6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b6") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("google/efficientnet-b6") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/efficientnet-b6: direct link, hf CLI and curl.
- Browser
- Download file 173 MB
-
https://huggingface.co/google/efficientnet-b6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/efficientnet-b6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/efficientnet-b6/resolve/main/pytorch_model.bin
173 MB
- Xet hash:
- 305d4e757b6cb65074645a7cc192d62d6bf406555dcab8c881bdebabe1fde441
- Size of remote file:
- 173 MB
- SHA256:
- c336d689252fd2ecc1bf82cf619c98637de93f469c4e9718f5830b9aefda42dc
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