Instructions to use Shubhamai/efficientnet-b7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubhamai/efficientnet-b7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Shubhamai/efficientnet-b7") 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("Shubhamai/efficientnet-b7") model = AutoModelForImageClassification.from_pretrained("Shubhamai/efficientnet-b7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Shubhamai/efficientnet-b7: direct link, hf CLI and curl.
- Browser
- Download file 267 MB
-
https://huggingface.co/Shubhamai/efficientnet-b7/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Shubhamai/efficientnet-b7/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Shubhamai/efficientnet-b7/resolve/main/flax_model.msgpack
267 MB
- Xet hash:
- 94672d3330ef65145e108f813b1811973c68fcec43806d20bad493c8ea1adad3
- Size of remote file:
- 267 MB
- SHA256:
- 7d394996f10cc5a49fbcc5faf91d8f649df5c26ccaa96826d975bad060dc1e98
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