Instructions to use prithivMLmods/RESISC45-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/RESISC45-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/RESISC45-SigLIP2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/RESISC45-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/RESISC45-SigLIP2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 362d8f5b1bfe9bfb879f211ade6fb6053207e72a44ea782c0f059389c9725268
- Size of remote file:
- 5.3 kB
- SHA256:
- 5371dc4f3b7759a694db9f7dd1b77d5c2e28b4074a617a682427c213e9fb3ec6
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