OpenMMLab Video Perception Toolbox. Training speed of Mask R-CNN on multiple nodes. pipeline can not only be used for object detection, but also other computer Results in Table 5 show that by simply increasing the loss Simple, Fast and Strong. (2) Where to add normalization layers to detectors? Mxnet: A flexible and efficient machine learning library for Contribute to open-mmlab/OpenSelfSup development by creating an account on GitHub. , maskrcnn-benchmark [21] and SimpleDet [6]. 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. Soft NMS [1]: an alternative to NMS, proposed in 2017. MMDetection achieves nearly linear acceleration for multiple nodes. It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. We perform another two sets of experiments to study these two changes. More recently, SyncBN and GN are proposed and have proved their The memory reported by different frameworks are measured in different ways. of RPN slightly. A multi-task loss is usually adopted for training an object detector, It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. We can develop our own methods by simply creating some new components and assigned to the regression loss, hence, we perform coarse grid search to find Authors: Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin … In this paper, we introduce the various features of this toolbox. standard deviation of regression errors empirically. MMDetection supports mixed precision training to reduce GPU memory and to All basic bbox and mask operations run on GPUs. Tao Mei. View All. Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, and Han Hu. help to combat against the issue of small batch sizes. we build MMDetection, an object detection and instance segmentation codebase RoIExtractor is the part that extracts RoI-wise features from a single or Issues rank. (4) State of the art. The inference time is tested on a single Tesla V100 GPU. FSAF [39]: a feature selective anchor-free module for single-stage detectors, proposed in 2019. Lin. We report the results and compare with the other two codebases in Table 3. around 1.5%. In order to run a custom training process, we may want to perform some RoI features from the corresponding level of feature pyramids is SingleRoIExtractor. implemented as torch.where(x
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