Instructions to use Cem13/med_complication_classifaction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cem13/med_complication_classifaction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Cem13/med_complication_classifaction") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Cem13/med_complication_classifaction") model = AutoModelForImageClassification.from_pretrained("Cem13/med_complication_classifaction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
92a36f7 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:4de3cea24e6f72d85ca56d0b1d934558031b5c89eec018305c9bedfec4dfb212
size 4024
|