Vit Base Patch16 Siglip Gap 384.v2 Webli

🚀 Model card for vit_base_patch16_siglip_gap_384.v2_webli

A SigLIP 2 ViT (image encoder only) for timm. It's equivalent to the image tower from https://huggingface.co/timm/ViT-B-16-SigLIP2-384. The gap variant uses global average pooling and has the attention pooling head removed.

🚀 Quick Start

This model is a SigLIP 2 ViT image encoder designed for the timm library. It offers a convenient way to extract image features with specific pooling mechanisms.

📚 Documentation

Model Details

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

@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}
        }
        

📄 License

This project is licensed under the Apache 2.0 license.