Vit Large Patch16 Siglip Gap 384.v2 Webli

🚀 Model card for vit_large_patch16_siglip_gap_384.v2_webli

This is a SigLIP 2 ViT (image encoder only) designed for timm. It is equivalent to the image tower from https://huggingface.co/timm/ViT-L-16-SigLIP2-384. The gap variant of this model uses global average pooling and removes the attention pooling head.

🚀 Quick Start

This model is ready - to - use in timm for image feature extraction tasks.

✨ Features

• A SigLIP 2 based ViT image encoder for timm.
• Utilizes global average pooling (gap variant).
• Removes the attention pooling head.

📚 Documentation

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}
        }

📄 License

This model is licensed under the Apache 2.0 license.