Instructions to use fcfrank10/food_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcfrank10/food_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fcfrank10/food_classification_model") 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("fcfrank10/food_classification_model") model = AutoModelForImageClassification.from_pretrained("fcfrank10/food_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e51ead88b4d2078d6ee446adf643622ca8ed959648cf054890779273d645f27
- Size of remote file:
- 5.18 kB
- SHA256:
- 9200398f09c3253cbb7e298299f633e87e6c77bbe8dcf421d182619c2a5c9efc
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