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Upernet Convnext Base

Developed by smp-hub
UPerNet image segmentation model based on ConvNeXt architecture, suitable for semantic segmentation tasks
Downloads 57
Release Time : 4/12/2025

Model Overview

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

Model Features

Efficient Architecture
Combines ConvNeXt-base encoder and UPerNet decoder to provide accurate segmentation results while maintaining efficiency
Pre-trained Support
Provides pre-trained weights for quick deployment and fine-tuning
Easy to Use
Offers a simple API through the segmentation_models.pytorch library, simplifying model loading and inference processes

Model Capabilities

Image Semantic Segmentation
Pixel-Level Classification
Scene Understanding

Use Cases

Computer Vision
Scene Parsing
Segments and identifies different objects in complex scenes
Can accurately identify objects from 150 categories
Autonomous Driving
Used for road scene understanding, identifying elements like pedestrians, vehicles, and roads
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