Instructions to use toolevalxm/MedVision-RadNet-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-RadNet-Clinical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-RadNet-Clinical") 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("toolevalxm/MedVision-RadNet-Clinical") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-RadNet-Clinical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9390f75998977c317070afb5db1a623724de6512fc7faec61d2b3792b3b4102
- Size of remote file:
- 40 Bytes
- SHA256:
- 2b931384ec9a7c347045bff44b1f9c0f1534948a06547264ac19433a748094c1
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