Efficient Backbone Network for Object Detection and Segmentation In this project, we release code for VoVNet-v2 backbone network (introduced by CenterMask) in mmdetectionv1.20 as a extention form. VoVNet can extract diverse feature representation efficiently by using One-Shot Aggregation (OSA) module that concatenates subsequent layers at once. Since the OSA module can capture multi-scale receptive fields, the diversifed feature maps allow object detection and segmentation to address multi-scale objects and pixels well, especially robust on small objects. VoVNet-v2 improves VoVNet-v1 by adding identity mapping that eases the optimization problem and effective SE (Squeeze-and-Excitation) that enhances the diversified feature representation.

thanks to https://github.com/youngwanLEE/vovnet-detectron2

The master branch works with PyTorch 1.1 to 1.4.

mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.

• Modular Design

  We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

• Support of multiple frameworks out of box

  The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.

• High efficiency

  All basic bbox and mask operations run on GPUs now. The training speed is faster than or comparable to other codebases, including Detectron, maskrcnn-benchmark and SimpleDet.

• State of the art

  The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.

Apart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.

This project is released under the Apache 2.0 license.

Please refer to INSTALL.md for installation and dataset preparation.

Please see GETTING_STARTED.md for the basic usage of MMDetection.

We appreciate all contributions to improve MMDetection. Please refer to CONTRIBUTING.md for the contributing guideline.

MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. 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.

This repo is currently maintained by Kai Chen (@hellock), Yuhang Cao (@yhcao6), Wenwei Zhang (@ZwwWayne), Jiarui Xu (@xvjiarui). Other core developers include Jiangmiao Pang (@OceanPang) and Jiaqi Wang (@myownskyW7).