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Vit Base Patch16 Siglip 224.webli
Vision Transformer model based on SigLIP, containing only the image encoder part, using original attention pooling mechanism
Downloads 330
Release Time : 12/24/2024
Model Overview
This model is based on the SigLIP (Sigmoid Loss for Language-Image Pre-training) Vision Transformer architecture, specifically designed for image feature extraction tasks. It adopts the standard ViT-B-16 structure with an input resolution of 224x224 pixels.
Model Features
SigLIP Pre-training
Uses Sigmoid loss function for language-image pre-training, optimizing image representation learning
Pure Image Encoder
Contains only the image encoding part, focusing on visual feature extraction tasks
Original Attention Pooling
Maintains original attention mechanism for feature pooling without introducing additional parameters
Standard ViT Architecture
Based on the widely validated ViT-B/16 structure with 16x16 patch size and 224x224 input resolution
Model Capabilities
Image Feature Extraction
Visual Representation Learning
Image Classification
Image Retrieval
Use Cases
Computer Vision
Image Classification
Used as a feature extractor for image classification tasks
Image Retrieval
Extracts image features for similarity search and retrieval systems
Multimodal Systems
Serves as a visual encoder for multimodal (image-text) systems
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