Instructions to use mLStudent33/food_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mLStudent33/food_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mLStudent33/food_classifier") 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("mLStudent33/food_classifier") model = AutoModelForImageClassification.from_pretrained("mLStudent33/food_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from mLStudent33/food_classifier: direct link, hf CLI and curl.
- Browser
- Download file 344 MB
-
https://huggingface.co/mLStudent33/food_classifier/resolve/main/tf_model.h5
- Command line
-
hf download hf://mLStudent33/food_classifier/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/mLStudent33/food_classifier/resolve/main/tf_model.h5
344 MB
- Xet hash:
- 5b73cf673d1db9d2459f04b56b3653a6574a2a7cd42cfc13004a0bb6459d44d3
- Size of remote file:
- 344 MB
- SHA256:
- 22ad92b9a2f9e220c93a4b698a9767c370a5d3ee9b81815bda0c8b783624a6f9
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