Instructions to use google/efficientnet-b5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b5") 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-b5") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/efficientnet-b5: direct link, hf CLI and curl.
- Browser
- Download file 123 MB
-
https://huggingface.co/google/efficientnet-b5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/efficientnet-b5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/efficientnet-b5/resolve/main/pytorch_model.bin
123 MB
- Xet hash:
- ba12434f765ea291ab19203b5e7df6d759ffb48b3d66b9c0404a4347c1b0d1c9
- Size of remote file:
- 123 MB
- SHA256:
- 43b7525bac833e25ea66b9ce5985827b204c184941da86893e8b7a41f7127927
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