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Vit Large Patch16 Siglip 256.v2 Webli

A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from

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1.18 GB
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vRevision 19c59e9
Updated
Sep 27, 2026
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Hugging Face

Source description

Model card for vit_large_patch16_siglip_256.v2_webli

A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/ViT-L-16-SigLIP2-256.

Model Details

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}
        }
        
@inproceedings{zhai2023sigmoid,
          title={Sigmoid loss for language image pre-training},
          author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
          booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
          pages={11975--11986},
          year={2023}
        }
        

Sources

1 source
Hugging FacePrimaryVersion 19c59e9f40a22029e4116ea7a0f03e38e13a17c3
huggingface.co/timm/vit_large_patch16_siglip_256.v2_webli/tree/19c59e9f40a22029e4116ea7a0f03e38e13a17c3

Verified Sep 27

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model.safetensors

safetensors · 1.18 GB · SHA-256 ffe422ca7824…907b · Hugging Face

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pytorch_model.bin

bin · 1.18 GB · SHA-256 d001a0b11869…4c1b · Hugging Face

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Source excerpts

2 excerpts

--- tags: - timm - transformers - image-feature-extraction - siglip - siglip2 library_name: timm license: apache-2.0 datasets: - webli --- # Model card for vit_large_patch16_siglip_256.v2_webli A SigLIP 2 ViT (image encoder only) for `timm`. Equivalent to image tower from https://huggingface.co/timm/ViT-L-16-SigLIP2-256. ## Model Details - **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 ## Citation ```bibtex @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} } ``` ```bibtex @inproceedings{zhai2023sigmoid, title={Sigmoid loss for language image pre-training}, author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={11975--11986}, year={2023} } ```

Source context: 106436 downloads · 2 likes · Pipeline image-feature-extraction · Library timm · Repo timm/vit_large_patch16_siglip_256.v2_webli