Deeplabv3plus Tu Resnet18
PyTorch-based semantic segmentation model supporting multiple encoder architectures
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Release Time : 12/23/2024
Model Overview
DeepLabV3Plus is a deep learning model for image semantic segmentation, employing an encoder-decoder architecture that supports various pre-trained encoders, suitable for a wide range of computer vision segmentation tasks.
Model Features
Multiple encoder support
Supports various pre-trained encoder architectures (e.g., ResNet), allowing flexible selection of encoders suitable for different tasks
Efficient segmentation
Utilizes the DeepLabV3Plus architecture combined with ASPP modules to efficiently handle features at different scales
Pre-trained weights
Provides pre-trained encoder weights on ImageNet, facilitating transfer learning
Model Capabilities
Image semantic segmentation
Multi-class pixel-level classification
Supports custom input channels
Supports custom output classes
Use Cases
Medical imaging
Organ segmentation
Segmentation of specific organs or tissues in CT or MRI images
Autonomous driving
Road scene understanding
Segmentation of key elements such as roads, vehicles, and pedestrians
Remote sensing
Land use classification
Segmentation of land cover types in satellite images
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