Instructions to use SeyedAli/Food-Image-Classification-VIT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SeyedAli/Food-Image-Classification-VIT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SeyedAli/Food-Image-Classification-VIT") 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("SeyedAli/Food-Image-Classification-VIT") model = AutoModelForImageClassification.from_pretrained("SeyedAli/Food-Image-Classification-VIT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1b2542b72555d774f34c41482d2b65541e478568a2508b02a2fc348efb19c4d
- Size of remote file:
- 344 MB
- SHA256:
- fec6030c124b4f9dc9553669f6eb9545b8b3a4ac993facbe3663ac150da74f57
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