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: 628 Bytes
a3386b3 cf14762 a3386b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # 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 |