timm / vit_large_patch16_siglip_256.v2_webli

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image-feature-extraction

Introduction of vit_large_patch16_siglip_256.v2_webli

Model Details of vit_large_patch16_siglip_256.v2_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
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}
        }
        

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vit_large_patch16_siglip_256.v2_webli is an open source model from GitHub that offers a free installation service, and any user can find vit_large_patch16_siglip_256.v2_webli on GitHub to install. At the same time, huggingface.co provides the effect of vit_large_patch16_siglip_256.v2_webli install, users can directly use vit_large_patch16_siglip_256.v2_webli installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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