vit_base_patch16_siglip_224.v2_webli Open-source Model - Accurately Extract Image Features with Powerful Capabilities!

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Vit Base Patch16 Siglip 224.v2 Webli

Developed by timm

ViT model based on SigLIP 2, focused on image feature extraction, trained on the webli dataset

Text-to-Image

Transformers

Open Source License:Apache-2.0 #Multimodal Visual Encoding #Sigmoid Loss Optimization #Dense Feature Extraction

Downloads 1,992

Release Time : 2/21/2025

Model Overview

This is a Vision Transformer model based on the SigLIP 2 architecture, specifically designed for image feature extraction tasks. It serves as the image encoder component in the SigLIP 2 model and is suitable for various computer vision applications.

Model Features

SigLIP 2 Architecture

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

Dense Feature Extraction

Capable of generating high-quality dense image feature representations

Webli Dataset Training

Pretrained on the large-scale webli dataset, offering broad knowledge coverage

Model Capabilities

Image Feature Extraction

Visual Semantic Understanding

Image Localization

Use Cases

Computer Vision

Image Retrieval

Uses extracted image features for similar image search

High-precision retrieval results

Visual Question Answering

Serves as a visual encoder for VQA systems

Improved understanding of image content

Property	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

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Vit Base Patch16 Siglip 224.v2 Webli

Model Overview

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