  We present MMDetection, an object detection toolbox that contains a rich set
of object detection and instance segmentation methods as well as related
components and modules. The toolbox started from a codebase of MMDet team who
won the detection track of COCO Challenge 2018. It gradually evolves into a
unified platform that covers many popular detection methods and contemporary
modules. It not only includes training and inference codes, but also provides
weights for more than 200 network models. We believe this toolbox is by far the
most complete detection toolbox. In this paper, we introduce the various
features of this toolbox. In addition, we also conduct a benchmarking study on
different methods, components, and their hyper-parameters. We wish that the
toolbox and benchmark could serve the growing research community by providing a
flexible toolkit to reimplement existing methods and develop their own new
detectors. Code and models are available at
https://github.com/open-mmlab/mmdetection. The project is under active
development and we will keep this document updated.
