# MMSegmentation — OpenMMLab Semantic Segmentation Toolbox

> MMSegmentation is a comprehensive semantic segmentation toolbox based on PyTorch, providing 50+ segmentation methods with a unified benchmark and modular design.

## Install

Save as a script file and run:

# MMSegmentation — OpenMMLab Semantic Segmentation Toolbox

## Quick Use
```bash
pip install mmsegmentation mmengine mmcv
python -c "
from mmseg.apis import init_model, inference_model
config = 'configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
checkpoint = 'pspnet_r50-d8_512x1024_40k_cityscapes.pth'
model = init_model(config, checkpoint, device='cuda:0')
result = inference_model(model, 'demo.png')
print(result.pred_sem_seg.data.shape)
"
```

## Introduction
MMSegmentation provides a unified platform for training and evaluating semantic segmentation models. Part of the OpenMMLab ecosystem, it offers a modular design that lets researchers mix and match backbones, decoders, and loss functions to rapidly prototype new architectures.

## What MMSegmentation Does
- Implements 50+ segmentation methods including DeepLab, PSPNet, and SegFormer
- Supports 15+ benchmark datasets such as Cityscapes, ADE20K, and PASCAL VOC
- Provides a modular config system to compose models from reusable components
- Offers pre-trained weights for immediate inference and fine-tuning
- Scales training across multiple GPUs with distributed data parallel

## Architecture Overview
MMSegmentation follows a registry-based architecture where backbones, decode heads, losses, and datasets are registered as interchangeable modules. A Python config file declares which components to assemble. The training loop is managed by MMEngine, which handles logging, checkpointing, and distributed coordination.

## Self-Hosting & Configuration
- Install mmsegmentation, mmengine, and mmcv via pip
- Download pre-trained checkpoints from the model zoo
- Modify config files to point to your dataset directory
- Adjust batch size and learning rate for your GPU memory
- Launch distributed training with torchrun or slurm scripts

## Key Features
- 50+ architectures with consistent training and evaluation APIs
- Modular config system for rapid experimentation
- Rich model zoo with pre-trained weights on major benchmarks
- Support for Transformer-based and CNN-based segmentation
- Built-in visualization tools for prediction overlays

## Comparison with Similar Tools
- **Detectron2** — broader scope (detection + segmentation); MMSeg focuses deeply on semantic segmentation
- **torchvision** — fewer architectures and no unified config system
- **segmentation_models.pytorch** — simpler API but lacks MMSeg's breadth of methods
- **PaddleSeg** — similar scope within the PaddlePaddle ecosystem

## FAQ
**Q: Can I use custom datasets?**
A: Yes. Implement a dataset class or convert your data to a supported format like Cityscapes.

**Q: Does it support instance segmentation?**
A: No. Use MMDetection for instance and panoptic segmentation tasks.

**Q: Which backbone gives the best accuracy?**
A: Swin Transformer and BEiT backbones currently lead ADE20K benchmarks when paired with UPerNet.

**Q: Can I export models for deployment?**
A: Yes. Use MMDeploy to convert models to ONNX, TensorRT, or OpenVINO formats.

## Sources
- https://github.com/open-mmlab/mmsegmentation
- https://mmsegmentation.readthedocs.io/

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Source: https://tokrepo.com/en/workflows/asset-7c63094f
Author: Script Depot