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Iiihi24

Developed by Diamantis99
A PyTorch-based semantic segmentation model supporting various encoder architectures, suitable for image segmentation tasks.
Image Segmentation
Safetensors
Open Source License:MIT#High-precision semantic segmentation#ResNet152 encoder#Medical image processing
Downloads 115
Release Time : 4/9/2025

Model Overview

Unet is a classic semantic segmentation model with an encoder-decoder structure, supporting various pre-trained encoders (e.g., ResNet152), suitable for medical imaging, remote sensing, and other segmentation tasks.

Model Features

Flexible encoder selection
Supports various pre-trained encoders (e.g., ResNet152) for flexible adaptation to different task requirements
Modular design
Configurable decoder channels, attention mechanisms, and other parameters for easy model tuning
Pre-trained support
Encoders can utilize ImageNet pre-trained weights to enhance model performance

Model Capabilities

Image segmentation
Semantic segmentation
Medical image analysis
Remote sensing image processing

Use Cases

Medical imaging
Organ segmentation
Segment specific organs or lesion areas in CT/MRI images
Test dataset IoU reached 0.924
Remote sensing
Land cover classification
Segment different land cover types in satellite/aerial images
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