Instructions to use RISys-Lab/ReasonSigLIP2-go16-384-S0-Rea with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RISys-Lab/ReasonSigLIP2-go16-384-S0-Rea 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-S0-Rea") 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-S0-Rea") model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonSigLIP2-go16-384-S0-Rea") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe49e037472be2a4269bca64ed326ee0db6089ffc0e7ee38ec7f8690a402b586
- Size of remote file:
- 3.74 GB
- SHA256:
- 6d0b5b58f43c10b53f339901cf9540c8a0a6d0fc46f24ad7329e1d9d051dcc06
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