Instructions to use philldevcoder/my_awesome_food_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philldevcoder/my_awesome_food_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="philldevcoder/my_awesome_food_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("philldevcoder/my_awesome_food_model") model = AutoModelForImageClassification.from_pretrained("philldevcoder/my_awesome_food_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5167727ca751a42f10c1b5bffa0ff6f7ccc130f411af7450a41be3792adf82d0
- Size of remote file:
- 5.78 kB
- SHA256:
- db0fce5d7e77b65f50946bd99767352c2819d0339d04e08386630552fec6e442
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