MMSegmentation is an open-source semantic segmentation toolbox and benchmark built on PyTorch, designed for researchers and developers. It offers a unified framework for implementing and comparing various semantic segmentation models, accelerating research and development in the field.

How It Works

MMSegmentation employs a modular design, decomposing the semantic segmentation pipeline into configurable components. This allows users to easily construct custom segmentation frameworks by combining different backbones, heads, and data augmentation strategies. The toolbox supports a wide array of state-of-the-art segmentation algorithms out-of-the-box, facilitating rapid experimentation and benchmarking.

Quick Start & Requirements

• Installation: pip install mmsegmentation
• Prerequisites: PyTorch 1.6+
• Documentation: https://mmsegmentation.readthedocs.io/en/latest/
• Colab Tutorial: https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/docs/en/get_started.ipynb

Highlighted Details

• Supports a vast range of semantic segmentation models, including PSPNet, DeepLabV3+, SegFormer, and Mask2Former.
• Includes a comprehensive model zoo with pre-trained weights and benchmark results.
• Offers extensive tutorials for configuration, dataset preparation, training, inference, and deployment.
• Actively updated with support for new algorithms like SAN and CAT-Seg, and tasks like monocular depth estimation.

Maintenance & Community

The project is part of the OpenMMLab ecosystem, indicating strong backing and active development. The latest release (v1.2.0) was on October 12, 2023.

Licensing & Compatibility

• License: Apache 2.0
• Compatibility: Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The v1.x release introduced significant changes from v0.x, requiring users to consult a migration guide for existing projects. The master branch is slated for removal, emphasizing the need to use the main or dev-1.x branches.