Vit Base Patch16 Siglip Gap 224.webli
Vision Transformer model based on SigLIP, containing only the image encoder part, employing a global average pooling strategy
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
Featured Recommended AI Models