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
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Siglip2ImageProcessorFast", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_num_patches": 256, | |
| "patch_size": 16, | |
| "processor_class": "Siglip2Processor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098 | |
| } | |