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Unetplusplus Tu Resnet18
A semantic segmentation model based on PyTorch, utilizing an improved UNet++ architecture, suitable for image segmentation tasks.
Downloads 215
Release Time : 12/23/2024
Model Overview
UnetPlusPlus is a semantic segmentation model from the segmentation_models.pytorch library, featuring an improved UNet++ architecture. It supports various encoders and pre-trained weights, making it suitable for tasks like medical imaging and remote sensing image segmentation.
Model Features
Improved UNet++ Architecture
Utilizes the UNet++ architecture with dense skip connections to enhance feature fusion and improve segmentation accuracy.
Multiple Encoder Support
Supports various pre-trained encoders (e.g., ResNet, EfficientNet) for flexible backbone network selection.
Pre-trained Weights
Supports ImageNet pre-trained weights to accelerate model convergence and enhance performance.
Modular Design
Features a modular design for easy customization of decoder channel counts and attention mechanisms.
Model Capabilities
Image Segmentation
Semantic Segmentation
Medical Image Analysis
Remote Sensing Image Processing
Use Cases
Medical Imaging
Organ Segmentation
Used for organ segmentation in CT or MRI images to assist in medical diagnosis.
Remote Sensing
Land Cover Classification
Used for land cover classification and segmentation in satellite or aerial images.
Autonomous Driving
Road Scene Segmentation
Used for segmenting roads, vehicles, and pedestrians in autonomous driving scenarios.
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