Instructions to use toolevalxm/MedVisionNet-ClinicalTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVisionNet-ClinicalTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVisionNet-ClinicalTest") 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/MedVisionNet-ClinicalTest") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVisionNet-ClinicalTest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b59e9da96a64479f02cc04df480005244e09bd4fdb92c16cfa69da68dc4d3eb2
- Size of remote file:
- 1.07 kB
- SHA256:
- 2e061624dbc2f2de3563b6fa2a68ac5cb2fdef7fadaeb2b9a4eaeb2574050b12
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