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Manet Tu Resnet18
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Manet Tu Resnet18

Developed by smp-test-models
A PyTorch-based semantic segmentation model utilizing multi-scale attention mechanisms, suitable for image segmentation tasks
Image Segmentation
Safetensors
Open Source License:MIT#Medical Image Segmentation#PyTorch Implementation#Pre-trained Encoder
Downloads 216
Release Time : 12/23/2024

Model Overview

MAnet is a deep learning model for semantic segmentation, featuring an encoder-decoder architecture with specially designed multi-scale attention modules to capture contextual information at different scales, improving segmentation accuracy.

Model Features

Multi-scale Attention Mechanism
Captures contextual information at different scales simultaneously through innovative attention modules, enhancing segmentation accuracy
Pre-trained Encoder Support
Supports transfer learning with various ImageNet pre-trained encoders (e.g., ResNet)
Flexible Architecture Configuration
Customizable encoder depth, decoder channel count, and other parameters to meet diverse needs

Model Capabilities

Image Semantic Segmentation
Multi-class Pixel-level Classification
Medical Image Analysis
Remote Sensing Image Analysis

Use Cases

Medical Imaging
Organ Segmentation
Segmentation of specific organs or lesion areas in CT/MRI scan images
Autonomous Driving
Road Scene Understanding
Segmentation of key elements such as roads, vehicles, and pedestrians
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