Instructions to use toolevalxm/MedVision-RadNet-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-RadNet-Clinical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-RadNet-Clinical") 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/MedVision-RadNet-Clinical") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-RadNet-Clinical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "vit", | |
| "architectures": ["ViTForImageClassification"], | |
| "hidden_size": 768, | |
| "num_attention_heads": 12, | |
| "patch_size": 16 | |
| } | |