Instructions to use Xenova/siglip-base-patch16-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/siglip-base-patch16-512 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('zero-shot-image-classification', 'Xenova/siglip-base-patch16-512');
Download preprocessor_config.json from Xenova/siglip-base-patch16-512: direct link, hf CLI and curl.
- Browser
- Download file 368 Bytes
-
https://huggingface.co/Xenova/siglip-base-patch16-512/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Xenova/siglip-base-patch16-512/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Xenova/siglip-base-patch16-512/resolve/main/preprocessor_config.json
368 Bytes
| { | |
| "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": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 512, | |
| "width": 512 | |
| } | |
| } | |