Vit Base Patch16 Siglip Gap 384.v2 Webli

🚀 vit_base_patch16_siglip_gap_384.v2_webli模型卡片

這是一個適用於timm的SigLIP 2 ViT（僅圖像編碼器），等同於來自https://huggingface.co/timm/ViT-B-16-SigLIP2-384的圖像塔。此gap變體使用全局平均池化，並移除了注意力池化頭。

🚀 快速開始

本模型是專為timm庫設計的SigLIP 2 ViT圖像編碼器，可用於圖像特徵提取等任務。它與https://huggingface.co/timm/ViT-B-16-SigLIP2-384的圖像塔等效，且gap變體在結構上進行了優化，移除了注意力池化頭並採用全局平均池化。

📚 詳細文檔

模型詳情

引用信息

@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}
        }
        

📄 許可證

本項目採用Apache-2.0許可證。