Upernet Convnext Base
UperNet is a framework for semantic segmentation that uses ConvNeXt as the backbone network and can predict semantic labels for each pixel.
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Release Time : 1/13/2023
Model Overview
UperNet is a semantic segmentation framework that includes a backbone network, Feature Pyramid Network (FPN), and Pyramid Pooling Module (PPM), suitable for scene understanding tasks.
Model Features
Multi-component Architecture
Includes a backbone network, Feature Pyramid Network (FPN), and Pyramid Pooling Module (PPM), offering flexible structure.
Compatible with Various Backbones
Supports embedding different visual backbone networks to adapt to various task requirements.
Pixel-level Prediction
Capable of predicting semantic labels for each pixel in an image.
Model Capabilities
Image Semantic Segmentation
Scene Understanding
Pixel-level Classification
Use Cases
Computer Vision
Autonomous Driving Scene Parsing
Used to identify road elements, vehicles, pedestrians, etc.
Medical Image Analysis
Used for organ or lesion segmentation in medical images
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