Instructions to use google/efficientnet-b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/efficientnet-b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/efficientnet-b0") 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-b0") model = AutoModelForImageClassification.from_pretrained("google/efficientnet-b0", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/efficientnet-b0: direct link, hf CLI and curl.
- Browser
- Download file 21.4 MB
-
https://huggingface.co/google/efficientnet-b0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/efficientnet-b0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/efficientnet-b0/resolve/main/pytorch_model.bin
21.4 MB
- Xet hash:
- c611786c39f15d5da84ac6cce153bf8c62dd3a783fe0e449209ed916fa2408fd
- Size of remote file:
- 21.4 MB
- SHA256:
- 30753ee4fb7d6ac093423435b2648d8e295b3120f8f3ba00cc714b2c4a34de6f
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