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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--> Drag one of the images below into the inference API field on the upper right.
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Additional images for testing can be found at:
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[Solar Dynamics Observatory Gallery](https://sdo.gsfc.nasa.gov/gallery/main
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You can use the following tags to further select images for testing:
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"coronal holes", "loops" or "flares"
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You can also choose "active regions" to get a general pool for testing.
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--> Drag one of the images below into the inference API field on the upper right.
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Additional images for testing can be found at:
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[Solar Dynamics Observatory Gallery](https://sdo.gsfc.nasa.gov/gallery/main)
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You can use the following tags to further select images for testing:
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"coronal holes", "loops" or "flares"
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You can also choose "active regions" to get a general pool for testing.
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