Instructions to use Veritone/siglip2-so400m-patch16-512-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-so400m-patch16-512-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-so400m-patch16-512-vision") 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("Veritone/siglip2-so400m-patch16-512-vision") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-so400m-patch16-512-vision") - Notebooks
- Google Colab
- Kaggle
File size: 394 Bytes
6c1fbcb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"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
}
}
|