Section 2 for the full list. Simple, Fast and Strong. SSD [19]: a classic and widely used single-stage detector with simple model architecture, proposed in 2015. beyond. We evaluate three models on each type of GPU and report the inference speed in It is noted that other hardwares of these servers are not exactly the same, Proceedings of the European Conference on Computer Vision tackles the problem of small batch size, such as Synchronized BN Besides MMDetection, there are also other popular codebases like Detectron [10], Or, have a go at fixing it yourself – the renderer is open source! usually much smaller than in classification, and the typical solution is to Megdet: A large mini-batch object detector. State of the art while caffe-style ResNet uses a 1x1 stride-2 convolutional layer followed by while increasing the loss weight will not bring further gain. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary Faster RCNN, Mask RCNN, RetinaNet, etc. Dataset. (4) More convolution layers in bbox head will lead to higher performance. The inference time is tested on a single Tesla V100 GPU. open-mmlab/mmdetection official. It not only includes training and inference codes, but also provides weights for more than 200 network models. Corpus ID: 189927886. Yanghao Li, Yuntao Chen, Naiyan Wang, and Zhaoxiang Zhang. In object detection, the batch size is is more memory saving. [2020-06] We have released OpenSelfSup Toolbox v0.1. Fu. We add this new hyper-parameter for sampling positive and negative anchors. Guided Anchoring [34]: a new anchoring scheme that predicts sparse and arbitrary-shaped anchors, proposed in 2019. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. In the bottleneck residual block, pytorch-style ResNet uses a 1x1 Towards the goal of providing a high-quality codebase and unified benchmark, open-mmlab. and Bounded IoU Loss has similar performance to Smooth L1 Loss, but requires components, and their hyper-parameters. The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage. The blue bar shows the performance of MMDetection and the yellow bar indicates linear speedup upper bound. 06/17/2019 ∙ by Kai Chen, et al. Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, and Junjie Yan. Results in Table 5 show that by simply increasing the loss Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Sign up to our mailing list for occasional updates. 1000×600, and now 1333×800 is typically adopted. We define some timepoints where users may register any executable methods (hooks), BN settings. Title: MMDetection: Open MMLab Detection Toolbox and Benchmark. The master branch works with PyTorch 1.1 to 1.4. mmdetection is an open source object detection toolbox based on PyTorch. We hope that the study can benefit future research our experience and best practice for training object detectors. scales and randomly pick a scale from them, the other is to define a scale don’t have to squint at a PDF. than or comparable to other codebases, including Detectron [10] In [36], the 2fc bbox head is replaced with (SBN is short for SyncBN.). Faster RCNN [27], Mask RCNN [13] and Cascade R-CNN [18], We use the train split for training and report the performance The remaining sections are organized as follows. and detection results are evaluated with mAP. Hybrid Task Cascade [4] and FCOS [32]. implemented as torch.where(x
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