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Vit So400m Patch14 Siglip Gap 896.pali Pt
Vision model based on SigLIP image encoder, employing global average pooling, part of the PaliGemma project
Downloads 15
Release Time : 12/26/2024
Model Overview
This model is a visual feature extraction model focused on image understanding tasks, utilizing SigLIP architecture with optimized global average pooling processing
Model Features
SigLIP Image Encoder
Image encoder using SigLIP architecture with efficient visual feature extraction capabilities
Global Average Pooling
Optimized feature representation using Global Average Pooling (GAP) technology
High-Resolution Processing
Supports high-resolution image input up to 896 pixels
Model Capabilities
Image feature extraction
Visual representation learning
Image understanding
Use Cases
Computer Vision
Image Classification
Can be used to build image classification systems
Visual Question Answering
Serves as the visual encoding component for multimodal models
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