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Upernet Convnext Xlarge

Developed by smp-hub
UPerNet semantic segmentation model based on ConvNeXt-XLarge encoder, suitable for high-precision image segmentation tasks
Downloads 62
Release Time : 4/12/2025

Model Overview

This model adopts the UPerNet architecture combined with ConvNeXt-XLarge as the encoder, specifically designed for semantic segmentation tasks, capable of pixel-level classification of different objects in images

Model Features

High-performance encoder
Uses ConvNeXt-XLarge as the encoder, providing powerful feature extraction capabilities
UPerNet architecture
Employs the UPerNet decoder structure to effectively integrate multi-scale features
Pre-trained support
Provides pre-trained weights for quick deployment and use
Easy integration
Seamlessly integrates with the Albumentations library to simplify preprocessing

Model Capabilities

Image semantic segmentation
Pixel-level classification
Scene understanding

Use Cases

Computer vision
Scene parsing
Identify and segment different objects in complex scenes
Autonomous driving
Understand road scenes, identifying elements like roads, vehicles, and pedestrians
Medical imaging
Medical image segmentation
Segment organs or lesion areas in CT/MRI images
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