This chapter introduces you to the framework of MMSegmentation, and the basic conception of semantic segmentation. It also provides links to detailed tutorials about MMSegmentation.
## What is semantic segmentation?
Semantic segmentation is the task of clustering parts of an image together that belong to the same object class.
It is a form of pixel-level prediction because each pixel in an image is classified according to a category.
Some example benchmarks for this task are [Cityscapes](https://www.cityscapes-dataset.com/benchmarks/), [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) and [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/).
Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.
## What is MMSegmentation?
MMSegmentation is a toolbox that provides a framework for unified implementation and evaluation of semant
ic segmentation methods,
and contains high-quality implementations of popular semantic segmentation methods and datasets.
MMSeg consists of 7 main parts including apis, structures, datasets, models, engine, evaluation and visualization.
- **apis** provides high-level APIs for model inference.
- **structures** provides segmentation data structure `SegDataSample`.
- **datasets** supports various datasets for semantic segmentation.
- **transforms** contains a lot of useful data augmentation transforms.
- **models** is the most vital part for segmentors and contains different components of a segmentor.
- **segmentors** defines all of the segmentation model classes.
- **data_preprocessors** works for preprocessing the input data of the model.
- **backbones** contains various backbone networks that transform an image to feature maps.
- **necks** contains various neck components that connect the backbone and heads.
- **decode_heads** contains various head components that take feature map as input and predict segmentation results.
- **losses** contains various loss functions.
- **engine** is a part for runtime components that extends function of [MMEngine](https://github.com/open-mmlab/mmengine).
- **optimizers** provides optimizers and optimizer wrappers.
- **hooks** provides various hooks of the runner.
- **evaluation** provides different metrics for evaluating model performance.
- **visualization** is for visualizing segmentation results.
## How to use this documentation
Here is a detailed step-by-step guide to learn more about MMSegmentation:
1. For installation instructions, please see [get_started](getting_started.md).
2. For beginners, MMSegmentation is the best place to start the journey of semantic segmentation
as there are many SOTA and classic segmentation [models](model_zoo.md),
and it is easier to carry out a segmentation task by plugging together building blocks and convenient high-level apis.
Refer to the tutorials below for the basic usage of MMSegmentation:
