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
| checkpoints/28a7cd44f5bcd3e6cc760b65c7e0d54d.ckpt filter=lfs diff=lfs merge=lfs -text | |
| checkpoints/56ce1a7d241dc342982f5466c4a9d7ef.ckpt filter=lfs diff=lfs merge=lfs -text | |
| checkpoints/624407ef8e3a2a009f9fa51f9846fe9a.ckpt filter=lfs diff=lfs merge=lfs -text | |
| checkpoints/64a91b25f84141d32852e75a3aec7305.ckpt filter=lfs diff=lfs merge=lfs -text | |
| checkpoints/65fd1f04cb4c5847d86a9ed8ba31ac1a.ckpt filter=lfs diff=lfs merge=lfs -text | |