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
Download pytorch_model.bin from Cem13/med_complication_classifaction: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/Cem13/med_complication_classifaction/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cem13/med_complication_classifaction/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cem13/med_complication_classifaction/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- 15355345c607fed9edf4e44739ad6acae7c3d527d661b3631fcf02d89dff8fe4
- Size of remote file:
- 1.22 GB
- SHA256:
- 759a837126c3c9ddccd9edf0c74c4b7ef44896124966394e042d41359a6161d7
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