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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/RESISC45-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/RESISC45-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-985/optimizer.pt from prithivMLmods/RESISC45-SigLIP2: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/RESISC45-SigLIP2/resolve/main/checkpoint-985/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/RESISC45-SigLIP2/checkpoint-985/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/RESISC45-SigLIP2/resolve/main/checkpoint-985/optimizer.pt
687 MB
- Xet hash:
- c05b7aeaa0c02c4606449231bc8bd15fa27acf0c2d713ad88119b4a27c510417
- Size of remote file:
- 687 MB
- SHA256:
- b1355263d8f83f260a7aa62e7c4130be86af2a237592e67960054e3e7e2ad031
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