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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6f902234f3d5c682030b15a69507db1aada5edaef4c6193985deb75cd4096a6
- Size of remote file:
- 4.03 kB
- SHA256:
- e7b2d7f567b06104db1c5133faa380e8039da2b8834be80cced742bacf3415e7
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