Instructions to use toolevalxm/RadiologyVisionNet-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/RadiologyVisionNet-Clinical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/RadiologyVisionNet-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/RadiologyVisionNet-Clinical") model = AutoModelForImageClassification.from_pretrained("toolevalxm/RadiologyVisionNet-Clinical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33f2b98a8507b1827c51eae675b4d85b317f56e160926c637e349f7419e1828a
- Size of remote file:
- 1.03 kB
- SHA256:
- efcb59f2a7d892dd86d7de6a900461f393fb7f222d873087972987ec913eb85a
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