UniSeg – Unified Segmentation & Annotation Framework

UniSeg is a lightweight research framework designed for experimenting with segmentation and annotation workflows in a simple and modular environment.
The framework supports building, testing, and analyzing segmentation models while also providing tools for dataset annotation, visualization, and evaluation.

The goal of UniSeg is to make it easier for researchers and students to prototype segmentation ideas, annotate datasets, compare models, and analyze results without complex setup.

Overview

UniSeg focuses on creating a unified and minimal framework for segmentation research.
It is designed to simplify experimentation with different segmentation approaches while keeping the workflow reproducible and easy to extend.

Key ideas behind UniSeg include:

• Lightweight framework for segmentation research
• Modular pipeline for testing multiple models
• Simplified dataset handling and preprocessing
• Built for rapid experimentation and reproducibility

Status

Development in progress

UniSeg is currently under development and will be open sourced soon.
Documentation and example workflows will be provided with the initial public release.

Planned Features

• Modular segmentation model pipeline
• Dataset preprocessing and evaluation utilities
• Experiment tracking and result comparison
• Support for multiple segmentation architectures