ViT-gopt-16-SigLIP2-384-ONNX

ONNX export of timm/ViT-gopt-16-SigLIP2-384

This model is an export of timm/ViT-gopt-16-SigLIP2-384. It can be used with the open_clip_inference rust crate, or any other ONNX Runtime based implementation.

Usage with open_clip_inference in Rust:

use open_clip_inference::Clip;
use std::path::Path;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let model_id = "RuteNL/ViT-gopt-16-SigLIP2-384-ONNX";
    let mut clip = Clip::from_hf(model_id).build().await?;

    let img = image::open(Path::new("assets/img/cat_face.jpg")).expect("Failed to load image");
    let texts = &[
        "A photo of a cat",
        "A photo of a dog",
        "A photo of a beignet",
    ];

    let results = clip.classify(&img, texts)?;

    for (text, prob) in results {
        println!("{}: {:.2}%", text, prob * 100.0);
    }

    Ok(())
}

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Model card for ViT-gopt-16-SigLIP2-384

Model Details

A SigLIP 2 Vision-Lanuage model trained on WebLI.

This model has been converted for use in OpenCLIP from the original JAX checkpoints in Big Vision.

Model Details

• Model Type: Contrastive Image-Text, Zero-Shot Image Classification.
• Original: https://github.com/google-research/big_vision
• Dataset: WebLI
• Papers:
  • SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features: https://arxiv.org/abs/2502.14786
  • Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343

Model Usage

import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch >= 2.31.0, timm >= 1.0.15

model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-gopt-16-SigLIP2-384')
tokenizer = get_tokenizer('hf-hub:timm/ViT-gopt-16-SigLIP2-384')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

Citation

@article{tschannen2025siglip,
  title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features},
  author={Tschannen, Michael and Gritsenko, Alexey and Wang, Xiao and Naeem, Muhammad Ferjad and Alabdulmohsin, Ibrahim and Parthasarathy, Nikhil and Evans, Talfan and Beyer, Lucas and Xia, Ye and Mustafa, Basil and H'enaff, Olivier and Harmsen, Jeremiah and Steiner, Andreas and Zhai, Xiaohua},
  year={2025},
  journal={arXiv preprint arXiv:2502.14786}
}        

@article{zhai2023sigmoid,
  title={Sigmoid loss for language image pre-training},
  author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
  journal={arXiv preprint arXiv:2303.15343},
  year={2023}
}

@misc{big_vision,
  author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
  title = {Big Vision},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/google-research/big_vision}}
}