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Vit So400m Patch14 Siglip Gap 378.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.

ArtOtherVersionRevision 7245d05

Modellquelle

Art
Other
Dateigröße
1,54 GB
Version
vRevision 7245d05
Aktualisiert
27. Sept. 2026
Downloads
69
Quelle
Hugging Face

Quellenbeschreibung

Model card for vit_so400m_patch14_siglip_gap_378.v2_webli

A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/ViT-SO400M-14-SigLIP2-378. This gap variant uses global average pooling and has the attention pooling head removed.

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}
        }
        

Quellen

1 Quelle
Hugging FacePrimärVersion 7245d053b3c9746853986bd6a2c42c33b66e15f1
huggingface.co/timm/vit_so400m_patch14_siglip_gap_378.v2_webli/tree/7245d053b3c9746853986bd6a2c42c33b66e15f1

Verifiziert 27. Sept.

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model.safetensors

safetensors · 1,54 GB · SHA-256 81d6cb1b28be…702e · Hugging Face

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pytorch_model.bin

bin · 1,54 GB · SHA-256 dccb6c969986…44f1 · Hugging Face

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Quellenauszüge

2 Auszüge

--- tags: - timm - transformers - image-feature-extraction - siglip - siglip2 library_name: timm license: apache-2.0 datasets: - webli --- # Model card for vit_so400m_patch14_siglip_gap_378.v2_webli A SigLIP 2 ViT (image encoder only) for `timm`. Equivalent to image tower from https://huggingface.co/timm/ViT-SO400M-14-SigLIP2-378. 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: 69 downloads · 0 likes · Pipeline image-feature-extraction · Library timm · Repo timm/vit_so400m_patch14_siglip_gap_378.v2_webli