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Vit Base Patch16 Siglip Gap 224.webli
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Vit Base Patch16 Siglip Gap 224.webli

Developed by timm
Vision Transformer model based on SigLIP, containing only the image encoder part, employing a global average pooling strategy
Image Classification
Transformers
Open Source License:Apache-2.0#SigLIP Image Encoding#Global Average Pooling#Zero-shot Visual Tasks
Downloads 178
Release Time : 12/24/2024

Model Overview

This model is the visual encoder component in the SigLIP framework, designed specifically for image feature extraction, suitable for tasks requiring efficient visual representation

Model Features

SigLIP Optimized Architecture
Utilizes an improved Vision Transformer structure from the SigLIP framework, optimizing image representation capabilities
Global Average Pooling
Uses Global Average Pooling (GAP) instead of traditional CLS token, potentially enhancing feature stability
Efficient Feature Extraction
Optimized specifically for image feature extraction tasks, outputting compact visual representation vectors

Model Capabilities

Image feature extraction
Visual representation learning
Image content analysis

Use Cases

Computer Vision
Image Retrieval System
Extracts image features for similarity search
Efficiently generates compact image representation vectors
Multimodal Learning
Serves as a visual encoder in conjunction with other modality models
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