U

Upernet Convnext Tiny

Developed by smp-hub
UPerNet image segmentation model based on ConvNeXt-Tiny encoder, suitable for semantic segmentation tasks
Downloads 149
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
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase