Upernet Convnext Tiny
UPerNet image segmentation model based on ConvNeXt-Tiny encoder, suitable for semantic segmentation tasks
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Release Time : 4/12/2025
Model Overview
This is an image segmentation model using ConvNeXt-Tiny as the encoder in the UPerNet architecture, specifically designed for semantic segmentation tasks, supporting recognition of 150 categories
Model Features
Efficient Encoder
Uses ConvNeXt-Tiny as the encoder to improve efficiency while maintaining performance
Multi-category Support
Supports semantic segmentation for 150 categories
Pre-trained Weights
Provides model weights pre-trained on the ADE20K dataset
Easy Integration
Can be easily integrated into existing projects through the segmentation_models_pytorch library
Model Capabilities
Image Semantic Segmentation
Multi-category Recognition
Scene Understanding
Use Cases
Computer Vision
Scene Parsing
Performs semantic segmentation on complex scenes to identify different objects and regions
Can output pixel-level classification results
Autonomous Driving
Used for road and obstacle recognition in autonomous driving systems
Medical Image Analysis
Can be used for tissue or organ segmentation in medical images
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