Vit So400m Patch16 Siglip 256.v2 Webli
SigLIP 2 ViT model, containing only the image encoder part for image feature extraction, trained on the WebLI dataset.
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Release Time : 2/21/2025
Model Overview
This is a Vision Transformer (ViT) model based on the SigLIP 2 architecture, specifically designed for image feature extraction. It employs a Sigmoid loss function for language-image pretraining, offering improved semantic understanding and localization capabilities.
Model Features
SigLIP 2 Architecture
Utilizes the improved SigLIP 2 architecture for better semantic understanding and localization capabilities.
Sigmoid Loss Function
Employs Sigmoid loss for language-image pretraining, enhancing model performance.
Dense Feature Extraction
Capable of extracting dense image features, suitable for various downstream vision tasks.
Model Capabilities
Image Feature Extraction
Semantic Understanding
Image Localization
Use Cases
Computer Vision
Image Retrieval
Uses extracted image features for similar image retrieval.
Visual Question Answering
Serves as the image encoder for visual question answering systems.
Multimodal Applications
Image-Text Matching
Used to evaluate the matching degree between images and text descriptions.
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