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.
翻译:我们介绍MM探测器,这是一个物体探测工具箱,包含一套丰富的物体探测和试样分解方法以及相关组成部分和模块。该工具箱从一个获得COCO 挑战2018探测轨迹的MMDet团队的代码库开始,逐渐演变为一个涵盖许多流行的探测方法和当代模块的统一平台。它不仅包括培训和推断代码,而且还为200多个网络模型提供加权值。我们认为,这个工具箱是迄今为止最完整的检测工具箱。在本文中,我们介绍这个工具箱的各种特征。此外,我们还就不同方法、组件及其超参数进行基准研究。我们希望该工具箱和基准能够为不断增长的研究界服务,提供一个灵活工具箱和基准,以重新实施现有方法并开发自己的新探测器。代码和模型可以在 https://github.com/open-mmlab/mmdetection查阅。该项目正在积极开发之中,我们将不断更新该文件。