Image Classification
Transformers
PyTorch
English
sybil
medical
cancer
ct-scan
risk-prediction
healthcare
vision
Instructions to use Lab-Rasool/sybil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lab-Rasool/sybil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Lab-Rasool/sybil") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lab-Rasool/sybil", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __init__.py from Lab-Rasool/sybil: direct link, hf CLI and curl.
- Browser
- Download file 468 Bytes
-
https://huggingface.co/Lab-Rasool/sybil/resolve/main/__init__.py
- Command line
-
hf download hf://Lab-Rasool/sybil/__init__.py
-
curl -L -o __init__.py https://huggingface.co/Lab-Rasool/sybil/resolve/main/__init__.py
468 Bytes
| """Hugging Face Sybil model for lung cancer risk prediction""" | |
| from .configuration_sybil import SybilConfig | |
| from .modeling_sybil import ( | |
| SybilForRiskPrediction, | |
| SybilPreTrainedModel, | |
| SybilOutput, | |
| SybilEnsemble, | |
| ) | |
| from .image_processing_sybil import SybilImageProcessor | |
| __version__ = "1.0.0" | |
| __all__ = [ | |
| "SybilConfig", | |
| "SybilForRiskPrediction", | |
| "SybilPreTrainedModel", | |
| "SybilOutput", | |
| "SybilEnsemble", | |
| "SybilImageProcessor", | |
| ] |