Or9ksv4
PyTorch-based Unet image segmentation model supporting various encoder architectures and pre-trained weights
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Release Time : 4/9/2025
Model Overview
This is a PyTorch-implemented Unet architecture image segmentation model primarily used for semantic segmentation tasks. The model supports various encoder architectures (e.g., ResNet152) and pre-trained weights, with flexible decoder parameter configuration.
Model Features
Multiple encoder support
Supports mainstream encoder architectures like ResNet and can load ImageNet pre-trained weights
Flexible decoder configuration
Customizable decoder parameters including channel numbers, batch normalization, and attention mechanisms
High-performance segmentation
Achieves 94.5% IoU on the IPD dataset
Model Capabilities
Image semantic segmentation
Medical image analysis
Remote sensing image processing
Use Cases
Medical imaging
Organ segmentation
Used for organ identification and segmentation in CT/MRI images
High-precision segmentation results
Remote sensing
Land cover classification
Land use classification in satellite images
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