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
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-descriptionmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.