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Vit Giantopt Patch16 Siglip Gap 256.v2 Webli
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Vit Giantopt Patch16 Siglip Gap 256.v2 Webli

Developed by timm
SigLIP 2 ViT image encoder, using global average pooling, with attention pooling head removed, designed specifically for timm
Image Classification
Transformers
Open Source License:Apache-2.0#Multimodal Visual Encoding#Global Average Pooling#High Semantic Understanding
Downloads 17
Release Time : 2/21/2025

Model Overview

This is a Vision Transformer model based on SigLIP 2, specifically designed for image feature extraction. It replaces the attention pooling head with global average pooling (GAP), making it suitable for tasks requiring efficient image feature representation.

Model Features

SigLIP 2 Architecture
Based on the improved SigLIP 2 architecture, with enhanced semantic understanding and feature extraction capabilities
Global Average Pooling
Uses global average pooling (GAP) instead of attention pooling head, simplifying the model structure
Large-scale Pretraining
Pretrained on the webli dataset, providing strong visual representation capabilities

Model Capabilities

Image Feature Extraction
Visual Semantic Understanding
Dense Feature Representation

Use Cases

Computer Vision
Image Retrieval
Extracts image features for similar image retrieval
Visual Localization
Provides dense feature representation for visual localization tasks
Multimodal Applications
Vision-Language Pretraining
Serves as a visual encoder for vision-language models
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