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 .gitattributes from Lab-Rasool/sybil: direct link, hf CLI and curl.
- Browser
- Download file 430 Bytes
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https://huggingface.co/Lab-Rasool/sybil/resolve/main/.gitattributes
- Command line
-
hf download hf://Lab-Rasool/sybil/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/Lab-Rasool/sybil/resolve/main/.gitattributes
430 Bytes
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