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Fpn Tu Resnet18
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Fpn Tu Resnet18

Developed by smp-test-models
A PyTorch-implemented FPN image segmentation model that supports various encoder architectures, suitable for semantic segmentation tasks.
Image Segmentation
Safetensors
Open Source License:MIT#Image Segmentation#Pyramid Network#Pre-trained Encoder
Downloads 217
Release Time : 12/23/2024

Model Overview

This model is a semantic segmentation model based on the Feature Pyramid Network (FPN), capable of efficiently handling image segmentation tasks. It supports multiple pre-trained encoders and provides flexible configuration options to meet different needs.

Model Features

Multiple Encoder Support
Supports various pre-trained encoder architectures including ResNet.
Flexible Configuration Options
Offers multiple parameter configurations, such as encoder depth and decoder channel count, to adapt to different task requirements.
Pre-trained Weights
Supports initialization using ImageNet pre-trained weights.

Model Capabilities

Image Semantic Segmentation
Medical Image Analysis
Satellite Image Parsing
Autonomous Driving Scene Understanding

Use Cases

Medical Imaging
Organ Segmentation
Used for organ identification and segmentation in CT or MRI images.
Remote Sensing
Land Use Classification
Analyzes land use types in satellite images.
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