Image Classification
Transformers
Safetensors
English
siglip
Bone
Fracture
Detection
SigLIP2
medical
biology
Instructions to use prithivMLmods/Bone-Fracture-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Bone-Fracture-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Bone-Fracture-Detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Bone-Fracture-Detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Bone-Fracture-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-554/optimizer.pt from prithivMLmods/Bone-Fracture-Detection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/Bone-Fracture-Detection/resolve/main/checkpoint-554/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/Bone-Fracture-Detection/checkpoint-554/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/Bone-Fracture-Detection/resolve/main/checkpoint-554/optimizer.pt
687 MB
- Xet hash:
- 4576cdfaf203344bd3e305094c723b27e2f2f8937400745f85714a76c453ab96
- Size of remote file:
- 687 MB
- SHA256:
- 5771b41da8e3643347399a0c80aec0dd26c40b66fa222c570e808337f96e65af
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