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Ambubv9

Developed by Diamantis99
A PyTorch-based Unet image segmentation model supporting various encoder architectures, suitable for semantic segmentation tasks.
Image Segmentation
Safetensors
Open Source License:MIT#High-precision semantic segmentation#ResNet152 encoder#Medical image processing
Downloads 59
Release Time : 4/9/2025

Model Overview

This model is a semantic segmentation model based on the Unet architecture, using a pre-trained ResNet152 as the encoder, applicable for image segmentation tasks.

Model Features

Multiple encoder support
Supports various pre-trained encoders (e.g., ResNet152), allowing flexible selection based on needs.
High-performance segmentation
Outstanding performance on the IPD dataset, achieving a test set IoU of 0.995.
Easy to use
Provides simple API interfaces, supporting quick loading from pre-trained models.

Model Capabilities

Image segmentation
Semantic segmentation
Supports multiple encoders

Use Cases

Medical imaging
Organ segmentation
Used for organ segmentation tasks in medical imaging.
High-precision segmentation results with an IoU of 0.995
Remote sensing imagery
Land cover classification
Used for land cover classification and segmentation in remote sensing imagery.
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