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Vit Large Patch16 Siglip 384.v2 Webli
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Vit Large Patch16 Siglip 384.v2 Webli

Developed by timm
A vision Transformer model based on the SigLIP 2 architecture, designed for image feature extraction, pretrained on the webli dataset
Text-to-Image
Transformers
Open Source License:Apache-2.0#Multimodal Visual Encoding#Dense Feature Extraction#Zero-shot Transfer
Downloads 4,265
Release Time : 2/21/2025

Model Overview

This model is the visual encoder part described in the SigLIP 2 paper, adopting the ViT-Large architecture, focusing on efficient image feature extraction and multimodal understanding capabilities

Model Features

SigLIP 2 Architecture
Uses an improved Sigmoid loss function for pretraining, enhancing the model's multimodal understanding capabilities
High-Resolution Processing
Supports 384x384 resolution input, suitable for processing high-quality images
Dense Feature Extraction
Capable of generating rich image feature representations, applicable to downstream visual tasks

Model Capabilities

Image feature extraction
Multimodal understanding
Visual semantic encoding

Use Cases

Computer Vision
Image Retrieval
Utilizes extracted image features for similar image search
High-precision retrieval performance
Visual Question Answering
Serves as a visual encoder for multimodal question-answering systems
Improved question-answering accuracy
Multimodal Applications
Image-Text Matching
Evaluates the matching degree between images and text descriptions
Improved cross-modal alignment capabilities
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