Instructions to use kenobi/SDO_VT1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenobi/SDO_VT1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kenobi/SDO_VT1") 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("kenobi/SDO_VT1") model = AutoModelForImageClassification.from_pretrained("kenobi/SDO_VT1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)
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(https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
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Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
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## Example Images
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# NASA Solar Dynamics Observatory Vision Transformer v.1 (SDO_VT1)
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## Example Images
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