MMDetection: Open MMLab Detection Toolbox and Benchmark

TLDR

MMDetection originated from the MMDet team’s 2018 COCO Challenge‑winning codebase and has evolved into a unified platform covering popular detection methods and modules, with ongoing active development. The authors present MMDetection as a comprehensive toolbox and benchmark, aiming to provide a flexible toolkit for reimplementing existing methods and developing new detectors. MMDetection includes training and inference code, weights for over 200 network models, and supports benchmarking of methods, components, and hyper‑parameters. The authors claim MMDetection is the most complete detection toolbox available. Code and pretrained models are hosted at https://github.com/open-mmlab/mmdetection.

Abstract

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.