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:
- ed7909ad3375c2da600275c66a8f19cac734f7d9988d3dc0c1b9f4c10ccd2cc9
- Size of remote file:
- 372 MB
- SHA256:
- de7735b9c19f11a56c6b5f961a56bbf3642ab81f80f42667c04d85b912688995
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