Instructions to use toolevalxm/MedVisionNet-BenchmarkRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVisionNet-BenchmarkRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVisionNet-BenchmarkRepo") 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-BenchmarkRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVisionNet-BenchmarkRepo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf5e3b5a6d22ccb6967e558a524e9d8dc29885ac2acfc919ef5a90230e931269
- Size of remote file:
- 135 Bytes
- SHA256:
- c64c61abb2ea0fe92a9dffd418c24892add75ae1b26df2802792910d47e2a2be
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