Instructions to use RISys-Lab/ReasonSigLIP2-go16-384-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RISys-Lab/ReasonSigLIP2-go16-384-S2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="RISys-Lab/ReasonSigLIP2-go16-384-S2") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("RISys-Lab/ReasonSigLIP2-go16-384-S2") model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonSigLIP2-go16-384-S2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 547351d07bb6187f2f8a972f389cd27fe37334101a0bb86045ac0b370cd0021b
- Size of remote file:
- 3.74 GB
- SHA256:
- f7dfe31838d131cfd06d8c142d43480625670c5c4ff947c44a83596d3b9f220e
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