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:
- a71b5f991e513cf5bcde9ffc5df8db4962dc57b1c00e749531f9d4e1bb861c58
- Size of remote file:
- 687 MB
- SHA256:
- 26ae95bbd3ad4380821502da8269b682ac908a13704ad9681fe6fe2e7df95ac4
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