Instructions to use toolevalxm/MedVisionNet-DiagnosticAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVisionNet-DiagnosticAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVisionNet-DiagnosticAI") 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-DiagnosticAI") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVisionNet-DiagnosticAI") - Notebooks
- Google Colab
- Kaggle
Upload MedVisionNet-DiagnosticAI model with epoch_100 checkpoint and evaluation results
c5eef80 verified - Xet hash:
- 07c20be7fe3d6ea44e4bb755054117c00a689c072658536263ff2c400c93e955
- Size of remote file:
- 28 Bytes
- SHA256:
- 22776d3d8f6e4b943782a8899d1cbe4fa65a191345ffafb8d02bd7ad5a50b93f
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