Upernet Convnext Base
UPerNet image segmentation model based on ConvNeXt architecture, suitable for semantic segmentation tasks
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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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