Instructions to use yshsdfv/siglip2-base-patch16-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yshsdfv/siglip2-base-patch16-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="yshsdfv/siglip2-base-patch16-naflex") 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("yshsdfv/siglip2-base-patch16-naflex") model = AutoModelForZeroShotImageClassification.from_pretrained("yshsdfv/siglip2-base-patch16-naflex") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "Siglip2Model" | |
| ], | |
| "initializer_factor": 1.0, | |
| "model_type": "siglip2", | |
| "text_config": { | |
| "model_type": "siglip2_text_model", | |
| "vocab_size": 256000 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.49.0.dev0", | |
| "vision_config": { | |
| "model_type": "siglip2_vision_model" | |
| } | |
| } | |