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# OVERVIEW
This chapter introduces you to the framework of MMDetection, and provides links to detailed tutorials about MMDetection.
## What is MMDetection
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).
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