Image Classification
Transformers
TensorBoard
ONNX
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use spolivin/food-vit-tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spolivin/food-vit-tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="spolivin/food-vit-tutorial") 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("spolivin/food-vit-tutorial") model = AutoModelForImageClassification.from_pretrained("spolivin/food-vit-tutorial") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- onnx/model.onnx +3 -0
- onnx/model_quantized.onnx +3 -0
onnx/model.onnx
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