Open-source Semantic Segmentation Model of PSPNet-tu-ResNet18 - Accurately Capture Multi-scale Contextual Information

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

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

Image Semantic Segmentation

Multi-scale Feature Extraction

Support for Custom Class Numbers

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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