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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lab-Rasool/sybil", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 468 Bytes
cf14762 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | """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",
] |