Vit Base Patch16 Siglip Gap 384.v2 Webli
A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from This gap variant uses global average pooling and has the attention pooling head removed.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 328 MB
- Versión
- vRevision 38ceeac
- Actualizado
- 27 sept 2026
- Descargas
- 82
- Fuente
- Hugging Face
Descripción de la fuente
Model card for vit_base_patch16_siglip_gap_384.v2_webli
A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/ViT-B-16-SigLIP2-384. This gap variant uses global average pooling and has the attention pooling head removed.
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}
}
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 27 sept
Artefactos del modelo
2 artefactos
Artefactos del modelo
2 artefactosExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- tags: - timm - transformers - image-feature-extraction - siglip - siglip2 library_name: timm license: apache-2.0 datasets: - webli --- # Model card for vit_base_patch16_siglip_gap_384.v2_webli A SigLIP 2 ViT (image encoder only) for `timm`. Equivalent to image tower from https://huggingface.co/timm/ViT-B-16-SigLIP2-384. This `gap` variant uses global average pooling and has the attention pooling head removed. ## 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: 82 downloads · 0 likes · Pipeline image-feature-extraction · Library timm · Repo timm/vit_base_patch16_siglip_gap_384.v2_webli
