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Vit Base Patch16 Siglip 256.webli I18n
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Vit Base Patch16 Siglip 256.webli I18n

Developed by timm
ViT-B-16 vision Transformer model based on SigLIP, containing only the image encoder, utilizing raw attention pooling
Image Classification
Transformers
Open Source License:Apache-2.0#Multilingual Visual Encoding#Raw Attention Pooling#Zero-shot Image Understanding
Downloads 16
Release Time : 12/24/2024

Model Overview

This model is a vision Transformer specifically designed for image feature extraction, trained using the SigLIP (Sigmoid Loss for Language-Image Pre-training) method, suitable for visual tasks in multilingual scenarios.

Model Features

SigLIP Training Method
Uses Sigmoid Loss for language-image pre-training, enhancing the model's performance in multimodal tasks
Raw Attention Pooling
Retains the original attention mechanism for feature pooling without introducing additional pooling layers
Multilingual Support
Trained with multilingual scenarios in mind, suitable for international applications
Efficient Image Encoding
Based on the ViT architecture, capable of efficiently extracting image features

Model Capabilities

Image feature extraction
Visual representation learning
Multimodal task support

Use Cases

Computer Vision
Image Classification
Can serve as a base feature extractor for image classification tasks
Visual Search
Used as a feature extraction component for building visual search engines
Multimodal Applications
Image-Text Matching
Works with text models to achieve image-text matching tasks
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