P

Pan Tu Resnet18

Developed by smp-test-models
PAN is an image segmentation model implemented in PyTorch, utilizing pyramid attention mechanisms to enhance feature extraction capabilities
Downloads 211
Release Time : 12/23/2024

Model Overview

A deep learning model for semantic segmentation tasks, supporting various encoder architectures, suitable for segmentation scenarios such as medical imaging and satellite imagery

Model Features

Pyramid Attention Mechanism
Enhances the representation capability of feature pyramids through multi-scale attention modules
Flexible Encoder Selection
Supports mainstream encoder architectures like ResNet, with the ability to load ImageNet pre-trained weights
Lightweight Design
Default configuration requires only 32 decoder channels, making it suitable for resource-constrained scenarios

Model Capabilities

Image Semantic Segmentation
Multi-class Pixel-level Classification
Medical Image Analysis
Satellite Image Parsing

Use Cases

Medical Imaging
Organ Segmentation
Segmentation of organ tissues in CT/MRI images
Remote Sensing
Land Cover Classification
Segmentation of vegetation/buildings/water bodies in satellite images
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