Instructions to use google/siglip-base-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip-base-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip-base-patch16-384") 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("google/siglip-base-patch16-384") model = AutoModelForZeroShotImageClassification.from_pretrained("google/siglip-base-patch16-384") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "SiglipModel" | |
| ], | |
| "initializer_factor": 1.0, | |
| "model_type": "siglip", | |
| "text_config": { | |
| "model_type": "siglip_text_model" | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.0.dev0", | |
| "vision_config": { | |
| "image_size": 384, | |
| "model_type": "siglip_vision_model" | |
| } | |
| } | |