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*.csv` is California housing data from the 1990 US | |
| Census; more information is available at: | |
| https://developers.google.com/machine-learning/crash-course/california-housing-data-description | |
| * `mnist_*.csv` is a small sample of the | |
| [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is | |
| described at: http://yann.lecun.com/exdb/mnist/ | |
| * `anscombe.json` contains a copy of | |
| [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it | |
| was originally described in | |
| Anscombe, 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](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json). | |