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
| { | |
| "model_type": "vit", | |
| "architectures": ["ViTForImageClassification"], | |
| "image_size": 224, | |
| "num_channels": 1, | |
| "num_labels": 14 | |
| } |