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
| # Core dependencies for Sybil embedding extraction | |
| # Install with: pip install -r requirements.txt | |
| # HuggingFace Hub for model download | |
| huggingface_hub>=0.20.0 | |
| # PyTorch for model inference | |
| torch>=2.0.0 | |
| torchvision>=0.15.0 | |
| # Data processing | |
| numpy>=1.24.0 | |
| pandas>=2.0.0 | |
| # DICOM file handling | |
| pydicom>=2.4.0 | |
| # Image processing (required by Sybil model) | |
| Pillow>=10.0.0 | |
| # Model utilities | |
| scikit-learn>=1.3.0 | |
| tqdm>=4.65.0 | |
| # Optional but recommended for performance | |
| pyarrow>=14.0.0 | |
| # Note: For GPU support with CUDA 11.8, install PyTorch with: | |
| # pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 |