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OVERVIEW

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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.
    • 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.

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

  3. Refer to the below tutorials to dive deeper:

  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.