Instructions to use tarekziade/deit-tiny-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarekziade/deit-tiny-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tarekziade/deit-tiny-patch16-224") 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("tarekziade/deit-tiny-patch16-224") model = AutoModelForImageClassification.from_pretrained("tarekziade/deit-tiny-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 810 Bytes
50d4d1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"per_channel": true,
"reduce_range": true,
"per_model_config": {
"model": {
"op_types": [
"Constant",
"Gather",
"Pow",
"Unsqueeze",
"ConstantOfShape",
"Slice",
"Transpose",
"ReduceMean",
"MatMul",
"Add",
"Reshape",
"Sub",
"Shape",
"Conv",
"Erf",
"Expand",
"Concat",
"Softmax",
"Equal",
"Gemm",
"Mul",
"Where",
"Sqrt",
"Div"
],
"weight_type": "QUInt8"
}
}
} |