OmniSeg – Unified Image & Video Segmentation Toolkit

OmniSeg is a unified segmentation toolkit designed for both images and videos, providing a flexible environment for experimenting with modern segmentation models and workflows.
It focuses on bridging research experimentation and practical usability through a streamlined interface and modular architecture.

Overview

OmniSeg aims to provide a comprehensive environment for segmentation research and experimentation.
The toolkit supports both static image segmentation and temporal segmentation in videos, allowing researchers to analyze spatial and temporal patterns within visual data.

Key ideas behind OmniSeg include:

• Unified workflow for image and video segmentation
• Support for modern deep learning segmentation architectures
• Tools for visual inspection, annotation, and model evaluation
• Modular architecture for integrating custom research models
• Designed for research, experimentation, and rapid prototyping

Status

Development in progress

OmniSeg is currently under active development.
The project will be open sourced soon, along with documentation and example workflows for research and experimentation.

Planned Features

• Image and video segmentation pipelines
• Model comparison and evaluation tools
• Visualization and annotation utilities
• Support for custom deep learning segmentation models
• Research-friendly workflow for dataset analysis