# OVERVIEW

This chapter introduces you to the framework of MMDetection, and provides links to detailed tutorials about MMDetection.

## What is MMDetection

![image](https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png)

MMDetection is an object detection toolbox that contains a rich set of object detection, instance segmentation, and panoptic segmentation methods as well as related components and modules, and below is its whole framework:

MMDetection consists of 7 main parts, apis, structures, datasets, models, engine, evaluation and visualization.

- **apis** provides high-level APIs for model inference.
- **structures** provides data structures like bbox, mask, and DetDataSample.
- **datasets** supports various dataset for object detection, instance segmentation, and panoptic segmentation.
  - **transforms** contains a lot of useful data augmentation transforms.
  - **samplers** defines different data loader sampling strategy.
- **models** is the most vital part for detectors and contains different components of a detector.
  - **detectors** defines all of the detection model classes.
  - **data_preprocessors** is for preprocessing the input data of the model.
  - **backbones** contains various backbone networks.
  - **necks** contains various neck components.
  - **dense_heads** contains various detection heads that perform dense predictions.
  - **roi_heads** contains various detection heads that predict from RoIs.
  - **seg_heads** contains various segmentation heads.
  - **losses** contains various loss functions.
  - **task_modules** provides modules for detection tasks. E.g. assigners, samplers, box coders, and prior generators.
  - **layers** provides some basic neural network layers.
- **engine** is a part for runtime components.
  - **runner** provides extensions for [MMEngine's runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html).
  - **schedulers** provides schedulers for adjusting optimization hyperparameters.
  - **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 detection results.

## How to Use this Guide

Here is a detailed step-by-step guide to learn more about MMDetection:

1. For installation instructions, please see [get_started](get_started.md).

2. Refer to the below tutorials for the basic usage of MMDetection.

   - [Train and Test](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#train-test)

   - [Useful Tools](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#useful-tools)

3. Refer to the below tutorials to dive deeper:

   - [Basic Concepts](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts)
   - [Component Customization](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#component-customization)

4. For users of MMDetection 2.x version, we provide a guide to help you adapt to the new version. You can find it in the [migration guide](./migration/migration.md).