Image Feature Extraction
timm
PyTorch
Safetensors
radiology
medical-imaging
xray
ct
mri
ultrasound
foundation-model
vision-transformer
self-supervised
dino
dinov2
Eval Results (legacy)
Instructions to use Snarcy/OmniRad-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Snarcy/OmniRad-base with timm:
import timm model = timm.create_model("hf_hub:Snarcy/OmniRad-base", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecbfa0fb1113e1e598ce3e52757e313a10f60c470477d56e4fb9c021b90ee377
- Size of remote file:
- 343 MB
- SHA256:
- 775909569ec0f6656a3e5eb69f48bff8e57883d1d9257fc2b3af7800f1196880
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