Instructions to use gOLIVES/OLIVES_Dataset_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gOLIVES/OLIVES_Dataset_Models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gOLIVES/OLIVES_Dataset_Models") 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("gOLIVES/OLIVES_Dataset_Models") model = AutoModelForImageClassification.from_pretrained("gOLIVES/OLIVES_Dataset_Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 126 Bytes
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license: mit
datasets:
- gOLIVES/OLIVES_Dataset
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- medical
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