open-mmlab/mmsegmentation
Mmsegmentation
MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testing, and inference loops, with hook-based lifecycle extension for attaching user-defined logic at specific events.
The toolbox distinguishes itself through its extensibility and breadth of capabilities. It supports open-vocabulary segmentation, allowing arbitrary text queries to be mapped to pixel labels by combining vision-language models with segmentation decoders. It also integrates monocular depth estimation as a first-class capability, adding depth prediction heads to segmentation backbones within the same architecture. The framework provides a standardized benchmark suite for comparing algorithms across multiple datasets and metrics, and includes a complete dataset management system that handles format conversion, folder structure organization, multi-dataset mixing, and even direct dataset downloads.
Beyond its core segmentation functionality, MMSegmentation offers deep customization at every level of the pipeline. Users can extend the framework with custom backbones, decode heads, loss functions, optimizers, data transforms, evaluation metrics, and training hooks, all through a consistent registry pattern. The toolbox manages the entire data lifecycle from raw annotation conversion through preprocessing pipeline assembly, and supports model conversion for production deployment with API-based serving. Training and evaluation are controlled through configurable loop configurations, with checkpoint resumption, real-time progress monitoring, and result visualization built in.
Features
- PyTorch Semantic Segmentation Libraries - A deep learning toolkit built on PyTorch for building and customizing segmentation architectures with interchangeable backbones and heads.
- Segmentation Model Training - Trains semantic segmentation models on custom datasets using a unified pipeline with configurable backbones, decoders, and loss functions.
- Custom Pipeline Assemblers - Constructs tailored segmentation workflows by plugging together interchangeable components for backbone, head, and data processing.
- Segmentation Model Testing - Evaluates a trained segmentation model against a test dataset and reports standard performance metrics.
