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Vit Base Patch16 Siglip 512.webli
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Vit Base Patch16 Siglip 512.webli

Developed by timm
Vision Transformer model based on SigLIP architecture, containing only the image encoder part, using original attention pooling mechanism
Image Classification
Transformers
Open Source License:Apache-2.0#Vision-Language Pretraining#Zero-shot Image Classification#Attention Pooling
Downloads 702
Release Time : 12/24/2024

Model Overview

This model is a Vision Transformer based on the SigLIP architecture, focusing on image feature extraction tasks. It adopts the Vision Transformer (ViT) structure and is particularly suitable for downstream tasks requiring high-quality image representations.

Model Features

SigLIP Architecture
Adopts the SigLIP architecture, focusing on image encoding tasks with efficient attention mechanisms
Original Attention Pooling
Uses original attention pooling method to retain more image feature information
ViT-B-16 Foundation
Based on Vision Transformer Base 16 architecture, balancing performance and computational efficiency

Model Capabilities

Image feature extraction
Visual representation learning

Use Cases

Computer Vision
Image Classification
Used as a feature extractor for image classification tasks
Visual Search
Provides high-quality image representations for visual search systems
Multimodal Applications
Image-Text Matching
Serves as a visual encoder for cross-modal matching tasks
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