Instructions to use n1hal/Food_Model_Example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use n1hal/Food_Model_Example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="n1hal/Food_Model_Example") 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("n1hal/Food_Model_Example") model = AutoModelForImageClassification.from_pretrained("n1hal/Food_Model_Example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c763bcc02ca2b0fab663977ebf83e92c1063bb8003deac923d4a59e83618fa80
- Size of remote file:
- 344 MB
- SHA256:
- aad329f70b717b6c536de164a529d929c3e09cbaf357198c7e9f195650a82e94
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