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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "Unsqueeze", | |
| "Div", | |
| "Conv", | |
| "Mul", | |
| "MatMul", | |
| "ConstantOfShape", | |
| "Expand", | |
| "Equal", | |
| "Where", | |
| "Sqrt", | |
| "Sub", | |
| "Add", | |
| "Slice", | |
| "Shape", | |
| "Gather", | |
| "ReduceMean", | |
| "Softmax", | |
| "Pow", | |
| "Constant", | |
| "Concat", | |
| "Transpose", | |
| "Gemm", | |
| "Reshape", | |
| "Erf" | |
| ], | |
| "weight_type": "QUInt8" | |
| } | |
| } | |
| } |