Zero-Shot Image Classification
Transformers
Safetensors
siglip2_vision_model
feature-extraction
vision
Instructions to use ostris/siglip2-base-patch16-style-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ostris/siglip2-base-patch16-style-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ostris/siglip2-base-patch16-style-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 AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ostris/siglip2-base-patch16-style-naflex") model = AutoModel.from_pretrained("ostris/siglip2-base-patch16-style-naflex") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Siglip2ImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_num_patches": 256, | |
| "patch_size": 16, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098 | |
| }, | |
| "processor_class": "Siglip2Processor" | |
| } | |