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
File size: 127 Bytes
0be40e0 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:0c664f9c0c843cbde4346b3e741da20545b855409bd8f8003288d96a8b6c2690
size 30
|