Instructions to use siyah1/SWin-ViT-Xray with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use siyah1/SWin-ViT-Xray with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="siyah1/SWin-ViT-Xray") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("siyah1/SWin-ViT-Xray") model = AutoModelForImageClassification.from_pretrained("siyah1/SWin-ViT-Xray") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd8e8f4c2aec6347980ee211c1eb7c0c5386071295485b9d55df546ac7ae9f19
- Size of remote file:
- 348 MB
- SHA256:
- fa9e6968e8a1025e244421d19c5eef854c5fce8a150fbfab05dcdf1107144ce0
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