Vit So400m Patch14 Siglip 224.v2 Webli
A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 1,59 GB
- Versão
- vRevision 897c2e2
- Atualizado
- 28 de set. de 2026
- Downloads
- 8,5 mil
- Fonte
- Hugging Face
Descrição da fonte
Model card for vit_so400m_patch14_siglip_224.v2_webli
A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/ViT-SO400M-14-SigLIP2.
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
@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}
}
Fontes
1 fonte
Fontes
1 fonteVerificado 28 de set.
Artefatos de modelo
2 artefatos
Artefatos de modelo
2 artefatosTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- tags: - timm - transformers - image-feature-extraction - siglip - siglip2 library_name: timm license: apache-2.0 datasets: - webli --- # Model card for vit_so400m_patch14_siglip_224.v2_webli A SigLIP 2 ViT (image encoder only) for `timm`. Equivalent to image tower from https://huggingface.co/timm/ViT-SO400M-14-SigLIP2. ## 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: 8487 downloads · 0 likes · Pipeline image-feature-extraction · Library timm · Repo timm/vit_so400m_patch14_siglip_224.v2_webli
