Instructions to use agent593/Thyroid-Ultrasound-Image-Classification-EfficientNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agent593/Thyroid-Ultrasound-Image-Classification-EfficientNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="agent593/Thyroid-Ultrasound-Image-Classification-EfficientNetModel") 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("agent593/Thyroid-Ultrasound-Image-Classification-EfficientNetModel") model = AutoModelForImageClassification.from_pretrained("agent593/Thyroid-Ultrasound-Image-Classification-EfficientNetModel") - Notebooks
- Google Colab
- Kaggle
File size: 828 Bytes
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"_valid_processor_keys": [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"rescale_offset",
"do_normalize",
"image_mean",
"image_std",
"include_top",
"return_tensors",
"data_format",
"input_data_format"
],
"crop_size": {
"height": 289,
"width": 289
},
"do_center_crop": false,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "EfficientNetImageProcessor",
"image_std": [
0.47853944,
0.4732864,
0.47434163
],
"include_top": true,
"resample": 0,
"rescale_factor": 0.00392156862745098,
"rescale_offset": false,
"size": {
"height": 224,
"width": 224
}
}
|