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Pspnet Tu Resnet18

Developed by smp-test-models
PSPNet is a deep learning model for semantic segmentation that uses pyramid pooling modules to capture multi-scale contextual information
Downloads 213
Release Time : 12/23/2024

Model Overview

PyTorch-implemented PSPNet model for image semantic segmentation tasks, supporting various encoder architectures and pretrained weights

Model Features

Multi-scale Context Capture
Effectively captures contextual information at different scales through the Pyramid Pooling Module (PSP)
Flexible Encoder Selection
Supports various pretrained encoders (e.g., ResNet) and custom depths
Batch Normalization Support
The pyramid pooling module can optionally use batch normalization layers to improve training stability

Model Capabilities

Image Semantic Segmentation
Multi-scale Feature Extraction
Support for Custom Class Numbers

Use Cases

Computer Vision
Scene Parsing
Pixel-level semantic segmentation of complex scenes
Medical Image Analysis
Segmentation of organs or lesion areas in medical imaging
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