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Cbzlvzw

Developed by Diamantis99
A PyTorch-based semantic segmentation model that supports various encoder architectures, suitable for image segmentation tasks.
Image Segmentation
Safetensors
Open Source License:MIT#High-precision semantic segmentation#ResNet152 encoder#Medical image analysis
Downloads 70
Release Time : 4/9/2025

Model Overview

This model is a semantic segmentation model based on the U-Net architecture, supporting various pre-trained encoders (e.g., ResNet152), and can be used for various image segmentation tasks.

Model Features

Multiple encoder support
Supports various pre-trained encoder architectures (e.g., ResNet152), allowing flexible selection based on requirements.
High-performance segmentation
Achieves 92.3% IoU on the IPD dataset, demonstrating excellent segmentation performance.
PyTorch compatibility
Fully implemented in PyTorch, making it easy to integrate into existing PyTorch workflows.

Model Capabilities

Image segmentation
Semantic segmentation
Medical image analysis
Satellite image processing

Use Cases

Medical imaging
Organ segmentation
Used for organ identification and segmentation in CT or MRI images
High-precision organ boundary identification
Remote sensing
Land cover classification
Segmentation of different land cover types in satellite images
Accurate identification of various land features
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