Instructions to use Shubhamai/efficientnet-b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shubhamai/efficientnet-b0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Shubhamai/efficientnet-b0") 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-b0") model = AutoModelForImageClassification.from_pretrained("Shubhamai/efficientnet-b0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Shubhamai/efficientnet-b0: direct link, hf CLI and curl.
- Browser
- Download file 21.3 MB
-
https://huggingface.co/Shubhamai/efficientnet-b0/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Shubhamai/efficientnet-b0/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Shubhamai/efficientnet-b0/resolve/main/flax_model.msgpack
21.3 MB
- Xet hash:
- d3b9621c82d2fc7b1c1c990236e8f74c69adeffa19dc853ce2269da2067e64e0
- Size of remote file:
- 21.3 MB
- SHA256:
- 456dca1f54e3dfa82e4f0fdb807fed397120016e852cef5f8f3339505cdeca91
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