Instructions to use toolevalxm/RadiologyVisionNet-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/RadiologyVisionNet-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/RadiologyVisionNet-TestRepo") 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-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/RadiologyVisionNet-TestRepo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 499da8853300c09d7b0cf30398c2f15ba749abebcbd7de234aab33ccddfd203f
- Size of remote file:
- 30 Bytes
- SHA256:
- 0c664f9c0c843cbde4346b3e741da20545b855409bd8f8003288d96a8b6c2690
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