Instructions to use toolevalxm/MedVision-DiagnosticModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-DiagnosticModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-DiagnosticModel") 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-DiagnosticModel") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-DiagnosticModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6aee00358b001b1c59645538a00cf98b6faef6cb3a4419c4b693c7f0b0a1224c
- Size of remote file:
- 45 Bytes
- SHA256:
- 10dab7d2ea4774e5e5f524086dd2979e68f0eb5f6eb1f46bbe2905f57888ce71
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