vit_base_patch16_siglip_512.v2_webli Open-source Model - Accurately Extract Image Features to Boost Image Analysis!

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Vit Base Patch16 Siglip 512.v2 Webli

Developed by timm

Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset

Text-to-Image

Transformers

Open Source License:Apache-2.0 #Multimodal Visual Encoding #Dense Feature Extraction #Enhanced Semantic Understanding

Downloads 2,664

Release Time : 2/21/2025

Model Overview

This is a Vision Transformer (ViT) model based on the SigLIP 2 architecture, containing only the image encoder component. The model uses a patch size of 16, an input resolution of 512x512, and is pre-trained using a sigmoid loss function.

Model Features

SigLIP 2 Architecture

Utilizes the improved SigLIP 2 architecture with enhanced semantic understanding and localization capabilities

High-Resolution Processing

Supports high-resolution image input at 512x512

Dense Feature Extraction

Capable of extracting dense feature representations from images

Sigmoid Loss Function

Pre-trained using a sigmoid loss function to optimize vision-language alignment

Model Capabilities

Image Feature Extraction

Visual Semantic Understanding

Image Localization Analysis

Use Cases

Computer Vision

Image Retrieval

Extracts image features for similar image retrieval

Provides high-quality image embeddings

Vision-Language Tasks

Serves as a visual encoder for multimodal tasks

Enhanced visual semantic understanding capabilities

🚀 Model card for vit_base_patch16_siglip_512.v2_webli

This is a SigLIP 2 ViT (image encoder only) for timm, equivalent to the image tower from https://huggingface.co/timm/ViT-B-16-SigLIP2-512. It is designed for image feature extraction, leveraging the power of transformers in the timm library.

🚀 Quick Start

This model can be directly used in the timm library. You can refer to the official timm documentation for specific usage methods.

✨ Features

SigLIP 2 Architecture: Utilizes the advanced SigLIP 2 architecture, which offers improved semantic understanding, localization, and dense features.
Multilingual Support: Suitable for multilingual vision - language tasks.
Image Feature Extraction: Efficiently extracts features from images.

📚 Documentation

Model Details

Property	Details
Model Type	SigLIP 2 ViT (image encoder only)
Training Data	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}
        }

📄 License

This model is licensed under the Apache 2.0 license.

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