MMDetection: Open MMLab Detection Toolbox and Benchmark
This provides a flexible toolkit for the computer vision research community to reimplement and develop detection methods, though it is incremental as it builds on existing techniques.
The authors introduced MMDetection, an open-source toolbox and benchmark for object detection and instance segmentation, which includes over 200 pre-trained models and supports various methods and modules to facilitate research and development in the field.
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.