Instructions to use google/siglip2-so400m-patch16-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/siglip2-so400m-patch16-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/siglip2-so400m-patch16-512") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("google/siglip2-so400m-patch16-512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from google/siglip2-so400m-patch16-512: direct link, hf CLI and curl.
- Browser
- Download file 394 Bytes
-
https://huggingface.co/google/siglip2-so400m-patch16-512/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://google/siglip2-so400m-patch16-512/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/siglip2-so400m-patch16-512/resolve/main/preprocessor_config.json
394 Bytes
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "SiglipProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| } | |
| } | |