Upernet Swin Tiny
UPerNet is a semantic segmentation model based on the ConvNeXt-Tiny architecture, suitable for image segmentation tasks.
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Release Time : 4/12/2025
Model Overview
This model adopts the UPerNet architecture combined with ConvNeXt-Tiny as the encoder, specifically designed for semantic segmentation tasks, capable of accurately identifying and segmenting different object categories in images.
Model Features
Efficient Segmentation
Uses ConvNeXt-Tiny as the encoder to provide accurate segmentation results while maintaining efficient inference.
Pre-trained Support
Provides pre-trained model weights for quick deployment and use.
Multi-Class Segmentation
Supports semantic segmentation for 150 categories, suitable for complex scenes.
Model Capabilities
Image Semantic Segmentation
Multi-Class Object Recognition
Scene Understanding
Use Cases
Computer Vision
Scene Parsing
Segmentation and recognition of different objects in complex scenes
Accurately identifies and segments objects from 150 categories
Autonomous Driving
Used for road scene understanding, identifying elements such as vehicles, pedestrians, and roads
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