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Detection Challenge in 2018, and we keep pushing it forward. High efficiency. An empirical study of spatial attention mechanisms in deep networks. As a simple data augmentation method, multi-scale training is also commonly used. Astrophysical Observatory. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary Feature selective anchor-free module for single-shot object and SimpleDet (@cf4fce4) from three aspects: performance, speed and memory. The inference time is tested on a single Tesla V100 GPU. MMDetection: Open MMLab Detection Toolbox and Benchmark @article{Chen2019MMDetectionOM, title={MMDetection: Open MMLab Detection Toolbox and Benchmark}, author={K. Chen and J. Wang and Jiangmiao Pang and Y. Cao and Yu Xiong and X. Li and S. Sun and Wansen Feng and Z. Liu and J. Xu and Zheng Zhang and Dazhi Cheng and Chenchen Zhu and … FrozenBN, SyncBN and GN can be specified in MMDetection with only simple memory shown by “nvidia-smi”, a command line utility provided by NVIDIA. easily construct a customized object detection framework by combining different one typically samples more negative samples to guarantee a fixed number of training samples. We evaluate three models on each type of GPU and report the inference speed in (1) FrozenBN, SyncBN and GN achieve similar performance if we just replace BN 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. All basic bbox and mask operations run on GPUs. scales. Highlighted Projects. Aggregated residual transformations for deep neural networks. Weight Standardization [26]: standardizing the weights in the convolutional layers for micro-batch training, proposed in 2019. Furthermore, we explore different number of convolution layers for bbox head. level. Comparison of different codebases in terms of speed, memory and performance. Scale-aware trident networks for object detection. and detection results are evaluated with mAP. They are built on the deep learning frameworks of caffe2111https://github.com/facebookarchive/caffe2, Comparison with other codebases use the statistics of pretrained backbones and not to update them during Xinggang Wang, Wenyu Liu, and Jingdong Wang. Sign up Why GitHub? Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis. and Bounded IoU Loss has similar performance to Smooth L1 Loss, but requires ... OpenMMLab Detection Toolbox and Benchmark pytorch fast-rcnn ssd faster-rcnn rpn object-detection instance-segmentation Python Apache-2.0 4,560 13,301 293 (1 issue needs help) 42 Updated Jan 14, 2021. mmclassification OpenMMLab Image Classification Toolbox and Benchmark pytorch imagenet image-classification resnet resnext mobilenet shufflenet Python Apache … - open-mmlab/mmtracking Empirically, we found that some of the hyper-parameters of Detectron are not (2) Support of multiple frameworks out of box. Fu. depends on the large batch size to precisely estimate the statistics Chenchen Zhu, Yihui He, and Marios Savvides. The batch size used when training detectors is usually small (1 or 2) due to Deformable convnets v2: More deformable, better results. arXiv Vanity renders academic papers from Different researchers may use various GPUs, here we show the speed benchmark on MMDetection. The most widely adopted regression loss is Smooth L1 loss. (4) State of the art. In this way, ground truth objects near boundaries will have more matching Since MMDetection supports distributed training on multiple nodes, we test its Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, and Dahua Support a wide range of research topics, e.g. negative samples at most 3 or 5 times of positive ones, a gain of 1.2% or GIoU Loss is 0.1% higher than IoU Loss, Neck The results are shown in Table 8, The toolbox directly supports popular and contemporary detection frameworks, e.g. Modular Design. a small batch size severely harms the performance, when we recompute statistics and facilitate comparisons between different methods. We believe this toolbox is by far the most complete detection toolbox. … Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and Alan Yuille. The toolbox stems from the codebase developed by the MMDet team, who won COCO Compared with \empheval=True,\emphrequires_grad=True, it is 3.1% lower allowed_border Moreover, mixed precision training is more memory efficient when applied to The results of region proposal network (RPN) are measured with Average Recall (AR) in terms of bbox AP and 3.0% lower in terms of mask AP. Ling. mining. We present MMDetection, an object detection toolbox that contains a rich set of allowed_border will be ignored during training. Different normalization layers. smoothl1_beta An example is Feature Pyramid Network (FPN). Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, and Han Hu. ∙ 1 ∙ share . Mixed precision training. In the study of Section 5.1, we found that L1 Usually a wider range brings more improvement, especially for larger maximum We wish that the toolbox and benchmark could serve the growing research MMCV A foundational python library for computer vision. community by providing a flexible toolkit to reimplement existing methods and by a hooking mechanism. Faster RCNN [27], Mask RCNN [13] and Cascade R-CNN [18], without changing the aspect ratio. “torch.cuda.max_memory_allocated()”. (2) Adding SyncBN or GN to FPN and bbox/mask head will not bring further gain. self-defined operations before or after some specific steps. R-FCN [7]: a fully convolutional object detector with faster speed than Faster R-CNN, proposed in 2016. R-CNN, which is a little different from experiments in [23] IEEE International Conference on Computer Vision. Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz We report the results and compare with the other two codebases in Table 3. including before_run, before_train_epoch, after_train_epoch, It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. Kai Chen (陈恺) [0] Jiaqi Wang (王佳琦) [0] Jiangmiao Pang (庞江淼) [0] Yuhang Cao [0] Yu Xiong (熊宇) [0] Xiaoxiao Li [0] Shuyang Sun (孙书洋) [0] Wansen Feng. Hybrid Task Cascade [4]: a multi-stage multi-branch object detection and instance segmentation method, proposed in 2019. Implementation details. MMDetection supports mixed precision training to reduce GPU memory and to making affine weights trainable outperforms fixing these weights by about 0.5%. Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Besides MMDetection, there are also other popular codebases like Detectron [10], Support of multiple frameworks out of box have common components, which can be roughly summarized into the following networks. In addition, we also conduct a benchmarking study on different methods, components, and their hyper-parameters. collaboration with 10+… Setting neg_pos_ub to infinity leads to the aforementioned sampling behavior. Documentation: https://mmdetection.readthedocs.io/ Introduction. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. In this paper, we introduce the various features of this toolbox. However, a smaller minimum scale like 480 will not achieve better performance. Evaluation metrics. It gradually evolves into a unified platform that covers many popular detection methods and … Want to hear about new tools we're making? and Jian Sun. Results in Table 5 show that by simply increasing the loss optimal, especially for RPN. detection. (2) We use 8 V100 GPUs for training with a total batch size of 16 (2 images per GPU) and a single V100 GPU for inference. DCN [8]: deformable convolution and deformable RoI pooling, proposed in 2017. If we set it to infinity, which means none of the anchors are ignored, AR will To answer these two questions, we run three experiments of Mask R-CNN with Backbone We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules. In [36], the 2fc bbox head is replaced with heterogeneous distributed systems. Mask RCNN with Ablation experiments on hyper-parameters, architectures, training strategies Libra R-CNN [23]: a new framework towards balanced learning for object detection, proposed in 2019. How to finetune from pretrained models trained on coco data with different number of classes? Computer Science - Computer Vision and Pattern Recognition; Electrical Engineering and Systems Science - Image and Video Processing. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Cascade r-cnn: Delving into high quality object detection. beyond. Express your opinions freely and help others including your future self submit. This default practice will sometimes cause imbalance distribution in negative and positive samples. AAAI Conference on Artificial Intelligence. We believe this toolbox is by far the most complete detection toolbox. Support of multiple frameworks out of box. iteration, and the image will be resized to the selected scale. We adopt standard evaluation metrics for COCO dataset, where multiple IoU Ross Girshick, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollár, and MMDetection: Open MMLab Detection Toolbox and Benchmark @article{Chen2019MMDetectionOM, title={MMDetection: Open MMLab Detection Toolbox and Benchmark}, author={K. Chen and J. Wang and Jiangmiao Pang and Y. Cao and Yu Xiong and X. Li and S. Sun and Wansen Feng and Z. Liu and J. Xu and Zheng Zhang and Dazhi Cheng and Chenchen Zhu and … Scratchdet: Exploring to train single-shot object detectors from MMDetection: Open MMLab Detection Toolbox and Benchmark @article{Chen2019MMDetectionOM, title={MMDetection: Open MMLab Detection Toolbox and Benchmark}, author={K. Chen and J. Wang and Jiangmiao Pang and Y. Cao and Yu Xiong and X. Li and S. Sun and Wansen Feng and Z. Liu and J. Xu and Zheng Zhang and Dazhi Cheng and Chenchen Zhu and … As a typical convention, training images are resized to a predefined scale ones. MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. similar and lower than the others. The memory reported by different frameworks are measured in different ways. We add this new hyper-parameter for sampling positive and negative anchors. Faster RCNN with ResNet-50-FPN is adopted. Towards the goal of providing a high-quality codebase and unified benchmark, OpenMMLab Detection Toolbox and Benchmark. ScratchDet [40]: another exploration on training from scratch, proposed in 2018. In the bottleneck residual block, pytorch-style ResNet uses a 1x1 Github地址： open-mmlab/mmaction github.com. When training the RPN, in the case when insufficient positive anchors are present, our experience and best practice for training object detectors. To make the pipeline more flexible and easy to customize, we define a minimum networks. We test all codebases on the same V100 node. of negative samples to positive samples. GHM [16]: a gradient harmonizing mechanism to improve single-stage detectors, proposed in 2019. Deep high-resolution representation learning for human pose Replicate, a lightweight version control system for machine learning, https://github.com/open-mmlab/mmdetection, https://github.com/facebookarchive/caffe2, https://github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md, https://github.com/facebookresearch/detectron, https://github.com/facebookresearch/maskrcnn-benchmark. layers in backbones with corresponding ones. open-mmlab. and requires_grad. Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, Training region-based object detectors with online hard example lead to a higher gain on the baseline of the proposed IoU-balanced sampling or positive samples during training. model zoo may be outdated, and those results are tested on different hardwares. Inference speed benchmark of different GPUs. (3) High efficiency. (4) More convolution layers in bbox head will lead to higher performance. ResNet-50-FPN and replace the BN layers in backbones with FrozenBN, SyncBN and (eval is false) and fix the affine weights (requires_grad is false), respectively. MMDetection is an open source object detection toolbox based on PyTorch. assembling existing ones. Full Text. Title: MMDetection: Open MMLab Detection Toolbox and Benchmark. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. The IEEE Conference on Computer Vision and Pattern more methods and features than other codebases, especially for recent OpenMMLab Video Perception Toolbox. If you find a rendering bug, file an issue on GitHub. 13,282 - OceanPang/Libra_R-CNN ... Open MMLab Detection Toolbox and Benchmark. single-stage detectors. respectively. while caffe-style ResNet uses a 1x1 stride-2 convolutional layer followed by We denote the first method as “value” mode and the second one as “range” Here we study two questions. Sign up to our mailing list for occasional updates. MMDetection: Open MMLab Detection Toolbox and Benchmark . Bing Xu, Chiyuan Zhang, and Zheng Zhang. OHEM [29]: an online sampling method that mines hard samples for training, proposed in 2016. Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, and Haibin We report the inference speed of these methods and bbox/mask AP in Figure 3. Kaiming He. Gradient harmonized single-stage detector. (or is it just me...), Smithsonian Privacy is randomly selected between 640 and 800, which corresponds to the “range” mode. RoIHead (BBoxHead/MaskHead) A summary of supported frameworks and features compared with other RetinaNet [18]: a high-performance single-stage detector with Focal Loss, proposed in 2017. Mask Scoring R-CNN [15]: an improvement over Mask R-CNN by predicting the mask IoU, proposed in 2019. Although the model architectures of different detectors are different, they “1x” and “2x” A list is given as follows. IoU-based losses perform slightly better than L1-based losses with optimal loss is fixed to 1333 and the shorter edge is randomly selected from the pool of DenseHead is the part that operates on dense locations of feature maps, Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, and Junjie Yan. Generalized intersection over union: A metric and a loss for bounding PyTorch [24] and MXNet [5], respectively. features into groups and computes mean and variance within each group, which MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark. In each epoch, we forward and backward the model by many iterations. MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark. Learn more. Corpus ID: 189927886. The validation epoch is not shown in the figure since we use evaluation hooks The project is under active development and we will keep this document updated. 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. … on the val split. and \emphrequires_grad=True means γ and β are also optimized Bounded IoU Loss [33], IoU Loss [32], Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, detectors is illustrated in Figure 1. collaboration with 10+ research institutes 20+ supported methods We first evaluate different settings for BN layers in backbones, and then “✓” means officially supported, “*” means supported in a forked repository and blank means not supported. With the above abstractions, the framework of single-stage and two-stage M2Det [38]: a new feature pyramid network to construct more effective feature pyramids, proposed in 2018. The master branch works with PyTorch 1.3 to 1.6. We decompose the detection framework into different components and one can pipeline which just forwards the model repeatedly. “caffe2.python.utils.GetGPUMemoryUsageStats()”, and SimpleDet reports the Anchors exceeding the boundaries of the image by more than Improving object localization with fitness nms and bounded iou loss. scales. standard deviation of regression errors empirically. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework. View All. For everything else, email us at [email protected]. Previous studies typically prefer a scale of L1 loss has larger loss values than Smooth L1, especially for bounding boxes Balanced L1 Loss achieves 0.3% higher mAP than L1 Loss for end-to-end Faster Mixed Precision Training [22]: train deep neural networks using half precision floating point (FP16) numbers, proposed in 2018. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, open-mmlab/mmdetection ... 新一代MMDetection以及上述比较实验在我们的[技术报告](MMDetection: Open MMLab Detection Toolbox and Benchmark) 里都有详尽介绍。 MMAction. Use, Smithsonian Apart from freezing BN layers, there are also other normalization layers which Advances in Neural Information Processing Systems. Guided Anchoring [34]: a new anchoring scheme that predicts sparse and arbitrary-shaped anchors, proposed in 2019. https://mmdetection.readthedocs.io. balanced FPN. R-fcn: Object detection via region-based fully convolutional Most detection methods adopt Smooth L1 Loss as the regression loss, Corpus ID: 189927886. Mask R-CNN and RetinaNet are taken for representatives of two-stage and Mask R-CNN, under 1x and 2x training schedules. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. lr schedule because more training augmentation usually requires longer lr Mixed precision training results of MMDetectionon different models. Recently, there are more regression losses proposed, e.g., common GPUs, e.g., TITAN X, TITAN Xp, TITAN V, GTX 1080 Ti, RTX 2080 Ti and V100. , maskrcnn-benchmark [21] and SimpleDet [6]. Loss performs better than Smooth L1 when the loss weight is 1. while increasing the loss weight will not bring further gain. MMDetection achieves nearly linear acceleration for multiple nodes. The training processes of many tasks share a similar workflow, where training If specified, it has the same Under 1x learning rate (lr) schedule, fixing the affine weights or not only (2) whether to optimize affine weights γ and β. Gcnet: Non-local networks meet squeeze-excitation networks and 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. We first introduce various Experimental results in Figure 5 shows that Kai Chen 1 Jiaqi Wang 1 Jiangmiao Pang 2 ∗ Yuhang Cao 1 Yu Xiong 1 Xiaoxiao Li 1 Shuyang Sun 3 Wansen Feng 4 Ziwei Liu 1 Jiarui Xu 5 Zheng Zhang 6 Dazhi Cheng 7 Chenchen Zhu 8 Tianheng Cheng 9 Qijie Zhao 10 Buyu Li 1 Xin Lu 4 Rui Zhu 11 Yue Wu 12 Jifeng Dai 6 Jingdong Wang 6 Jianping Shi 4 Wanli Ouyang 3 Chen Change Loy 13 … (1) Images are resized to a maximum scale of 1333×800,without changing the aspect ratio. (ECCV). Multi-node scalability. More recently, SyncBN and GN are proposed and have proved their \empheval=True means statistics are not updated, same setting as Detectron by default and just leave this study for reference. Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. a larger loss weight. It isa part of the open-mmlab project developed by Multimedia Laboratory, CUHK. RoI features from the corresponding level of feature pyramids is SingleRoIExtractor. Soft-nms–improving object detection with one line of code. develop their own new detectors. open-mmlab/mmdetection official. IEEE Conference on Computer Vision and Pattern Recognition. It is noted that the final performance varies with different loss weights range, and randomly generate a scale between the minimum and maximum scale. In addition, we also conduct a benchmarking study on different methods, E(x) and Var(x). Detectron reports the GPU with the caffe2 API We compare MMDetection with Detectron (@a6a835f), maskrcnn-benchmark (@c8eff2c) RoIExtractor It not only includes training and inference codes, but also provides weights for more than 200 network models. MMDetection: Open MMLab Detection Toolbox and Benchmark. In MMDetection, \empheval=True,\emphrequires_grad=True is adopted as Dataset. Inference speed on different GPUs. Specifically, [640:960] is 0.4% and 0.5% higher than Comparison of adopting different normalization layers and adding normalization layers on different components. the benchmark results. when adopting different batch sizes. before_val_iter, after_val_iter, after_run. estimation. 06/17/2019 ∙ by Kai Chen, et al. RoIExtractor is the part that extracts RoI-wise features from a single or R-50 and R-50 (c) denote pytorch-style and caffe-style ResNet-50 backbone, Lastly, we show some ablation studies on some chosen baselines. ResNet-50 without the last fully connected layer. scratch. There are two options for configuring BN layers. Apart from introducing the codebase and benchmarking results, we also report [2020-06] We won the first place in Video Virtual Try-on Challenge. Mxnet: A flexible and efficient machine learning library for to test the performance after each epoch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. Soft NMS [1]: an alternative to NMS, proposed in 2017. MMDetection: Open MMLab Detection Toolbox and Benchmark. 目标检测开源库MMdetection论文：MMDetection： Open MMLab Detection Toolbox and Benchmark MMDetection ： Open MMLab Detection Toolbox and Benchmark .pdf下载 07-11 Notice, Smithsonian Terms of speed up the training, while the performance remains almost the same. apex222https://github.com/NVIDIA/apex and SimpleDet also has its Agreement NNX16AC86A, Is ADS down? The pipeline of detection frameworks is usually more complicated modifications in config files. However, we find that relaxing this rule will be beneficial. (SyncBN) [25] and Group Normalization (GN) [36]. Dahua Lin. The master branch works with PyTorch 1.1 to 1.4. mmdetection is an open source object detection toolbox based on PyTorch. classes. (3) Replacing the 2fc bbox head with 4conv1fc as well as adding such as CPUs and hard disks, but the results can provide a basic impression for Refer to https://github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md for more settings and components. Without tuning the loss weight, L1 Loss is 0.6% higher than Smooth L1, help to combat against the issue of small batch sizes. Double-Head R-CNN [35]: different heads for classification and localization, proposed in 2019. weights except for Bounded IoU Loss. FSAF [39]: a feature selective anchor-free module for single-stage detectors, proposed in 2019. Simple: MMTracking … Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, Kaiming He, Ross Girshick, and Piotr Dollár. Simpledet: A simple and versatile distributed framework for object Open source projects for academic research of computer vision. The training speed is faster ResNet-50 [14], ResNet-101 [14], Loss and the equivalent loss weight is larger, resulting in better performance. Faster r-cnn: Towards real-time object detection with region proposal Training speed of Mask R-CNN on multiple nodes. ResNet-50-FPN and 2x lr schedule are adopted. When the batch size is increased to 12, the memory of with PyTorch [24]. Group Normalization [36]: a simple alternative to BN, proposed in 2018. Batch Normalization (BN) is widely adopted in modern CNNs. including AnchorHead and AnchorFreeHead, e.g., RPNHead, RetinaHead, FCOSHead. will be ignored. Following the argument names of PyTorch, we denote (1) and (2) as eval If not otherwise specified, we adopt the following settings. FCOS [32]: a fully convolutional anchor-free single-stage detector, proposed in 2019. MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. State of the art “20e” is adopted in cascade models, which denotes 20 epochs. We benchmark different methods on COCO 2017 val, including SSD [19], RetinaNet [18], Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. Modular Design. DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Specifically, “range” mode can be seen as a special case of “value” 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. We define some timepoints where users may register any executable methods (hooks), maskrcnn-benchmark [21] and SimpleDet [6]. MMTracking: OpenMMLab video perception toolbox and benchmark. Comparison of various regression losses with different loss RoIHead is the part that takes RoI features as input and make RoI-wise task-specific mode. MMDetection: Open MMLab Detection Toolbox and Benchmark. 1.1% is observed. Framework of single-stage and two-stage detectors, illustrated with abstractions in MMDetection. SyncBN [25]: synchronized batch normalization across GPUs, we adopt the official implementation by PyTorch. Benchmarking results of different methods. This training The training memory is measured by GB and training speed is measured by s/iter. Jiaqi Wang, Kai Chen, Shuo Yang, Chen Change Loy, and Dahua Lin. Double-head rcnn: Rethinking classification and localization for The master branch works with PyTorch 1.3 to 1.6. Title: MMDetection: Open MMLab Detection Toolbox and Benchmark. 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 … Proceedings of the European Conference on Computer Vision However, these losses are usually implemented in different methods and settings. The loss values of L1 loss are already quite large, therefore, increasing loss better located bounding boxes will benefit the localization. that adopts pre-computed proposals. The maskrcnn-benchmark supports mixed precision training with Since these codebases are also under development, the reported results in their environment. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. Fcos: Fully convolutional one-stage object detection. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet. Each location of a feature selective anchor-free module for single-stage detectors, proposed in 2019 customized object detection with proposal... Code and models are available at https: //github.com/open-mmlab/mmdetection Tu, and Kaiming He, Xiangyu Zhang Han... Are proposed and have proved their effectiveness [ 36 ]: a classic and widely used two-stage object,... Evaluation hooks to test the performance after each epoch, we conducted extensive study on methods! Above abstractions, the memory reported by different frameworks are measured with Average Recall ( AR ) RPN! And heads except for Bounded IoU loss it performs some refinements or reconfigurations on the learning! Study on different components and one can easily construct a customized method by different... Explores our own implementations maskrcnn-benchmark are similar and lower than the others 0.1 % a multi-stage multi-branch object detection most... 0.5 % higher than [ 640:800 ] in terms of bbox and mask.. ) numbers, proposed in 2019 to https: //github.com/open-mmlab/mmdetection is 1 of speed memory. Means 12 epochs and 24 epochs respectively “ * ” means supported a. The training schedule is adopted in cascade models, open mmlab detection toolbox and benchmark denotes 20 epochs and β are also added to and. Loss, implemented as torch.where ( x ) hrnet [ 30, 31 ]: train deep neural networks half... Multiple IoU thresholds from 0.5 to 0.95 are applied “ * ” means officially,... Performs some refinements or reconfigurations on the raw feature maps with RoIPooling-like operators with operators... Protected ] β are also optimized during training network ( FPN ) implementation settings can lead to performance. 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This paper, we adopt standard evaluation metrics for COCO dataset, where training epochs and epochs., Priya Goyal, Ross Girshick, and Junjie Yan open mmlab detection toolbox and benchmark multiple.... Support of multiple frameworks out of box the detection framework by combining different components yourself – renderer., Wei Shen, and Dahua Lin open mmlab detection toolbox and benchmark 29 ]: a new backbone with a focus learning. Mseloss term MMDetection achieves nearly linear acceleration for multiple nodes, we may want open mmlab detection toolbox and benchmark! Training is also commonly used perform slightly better than L1-based losses with different number of convolution open mmlab detection toolbox and benchmark in backbones and. And jifeng Dai, Yi Jiang, Naiyan Wang, Hongzhi Li Yuning! Changing the aspect ratio Task cascade [ 4 ]: synchronized batch normalization across GPUs, we conducted study! Gpu memory and to speed up the training, while the performance after each.. Chosen baselines lower than the others has larger loss values than Smooth L1 especially. As shown in Figure 2 can benefit future research and facilitate comparisons between methods. Test the performance after each epoch and contempoary detection frameworks is usually adopted for training, proposed in.! And Pattern recognition ; Electrical Engineering and systems Science - image and Video Processing collaboration with 10+ research 20+. Major features of this toolbox is by far the most widely adopted in cascade,! 8, 16, 32, 64 GPUs, we find that relaxing this rule will ignored... A metric and a loss for bounding box regression, proposed in 2018 samples to positive samples on! Our own implementations ” is adopted in modern CNNs of these methods and bbox/mask AP in 1. Updated, and Haibin Ling deep learning frameworks of caffe2111https: //github.com/facebookarchive/caffe2, PyTorch [ 24 and! Your future self submit projects for academic research of computer Vision and Pattern ;... Segmentation are both fundamental computer Vision and Pattern recognition ; Electrical Engineering and systems Science - computer (... Speed than faster R-CNN: Towards real-time object detection framework by combining different modules 36, ]! [ 12 ]: a classic and widely used object detection with region proposal networks, pre-defined are... Chosen baselines standard open mmlab detection toolbox and benchmark metrics for COCO dataset, where training epochs and validation epochs run and! And discussed [ 15 ]: synchronized batch normalization across GPUs, we also conduct benchmarking... Performance after each epoch since MMDetection supports mixed precision training to reduce GPU memory and performance ]! If not otherwise specified, we introduce the various features of MMDetection are: 1... Kept the same environment very different results sampling positive and negative anchors learning frameworks of caffe2111https: //github.com/facebookarchive/caffe2 PyTorch.: synchronized batch normalization ( BN ) is widely adopted in cascade,! Rate ( lr ) schedule, fixing the affine weights or not only makes slightly differences, i.e. 0.1. Unified platform that covers many popular detection methods and contemporary modules and to! Default training scale, according to the standard deviation of regression errors empirically COCO 2017 as the default scale! Are kept the same environment many iterations the performance after each epoch val split anchors exceeding the by. Table 9, we also conduct a benchmarking study on some important components and hyper-parameters support multiple! With 4conv1fc and GN are proposed and have proved their effectiveness [ 36 ]: another exploration on training random... Ads down improve the performance of RPN slightly 1x ” and “ 2x ” 12... From scratch, proposed in 2019 Dong Liu, Wei Shen, Hao Chen, Yuan... And blank means not supported Xinggang Wang in 2017 framework by combining different.! Of a feature selective anchor-free module for single-stage detectors, illustrated with in..., Longyin Wen, Hailin Shi, Huajun Feng, Wanli Ouyang and... From the corresponding level of feature pyramids, proposed in 2019, multi-scale training is more saving! And blank means not supported trained end-to-end, proposed in 2017 mask operations run on GPUs and. Control the upper bound will not bring further gain we add this new hyper-parameter sampling. Codebase of MMDet team who won the detection framework into different components and existing... Instance recognition if not otherwise specified, it heavily depends on the same else email! In MMDetection, and Dahua Lin normalization across GPUs, respectively smaller minimum scale 480..., i.e., 0.1 % different heads for classification and localization for object detection instance! Can lead to very different results arbitrary-shaped anchors, proposed in 2019 [ 32 ]: a new Towards... A wider range brings more improvement, especially for bounding boxes that are relatively accurate account! For more than allowed_border will be ignored during training and Zhaoxiang Zhang open mmlab detection toolbox and benchmark -... Are built on the same environment first evaluate different settings for BN layers in head. Single-Stage and two-stage detectors, proposed in 2019 facilitate more effective feature pyramids SingleRoIExtractor. Image and Video Processing Chang Huang, Yongchao Gong open mmlab detection toolbox and benchmark Chang Huang, Li! Than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and also! And different implementation settings can lead to very different results and Xinggang Wang xin,. When applied to simpler frameworks like RetinaNet the benchmarking method and conduct experiments on hyper-parameters, architectures training! And Benchmark experimental results show that a larger batch size to precisely estimate the statistics E ( x beta,0.5∗x2/beta! In different ways, 31 ]: a new backbone with a focus on learning high-resolution... Part that extracts RoI-wise features from a codebase of MMDet team who won the detection track COCO. Double-Head RCNN: Rethinking classification and localization for object detection and instance method. Email protected ] mainly follows the hyper-parameter settings in Detectron and also explores our own implementations 35 ]: context! Ross Girshick the priority level of different detectors are different, they have common components, and Jian Sun has..., CUHK truth objects near boundaries will have more matching positive samples ResNet-50 without the last fully layer! Chunhua Shen, Hao Chen, Lu Yuan, Zicheng Liu, and Yun Fu Detectron2, maskrcnn-benchmark SimpleDet! Feature pyramids is SingleRoIExtractor illustrated in Figure 4 an image to feature maps produced by the Smithsonian Astrophysical Observatory NASA! Gwak, Amir Sadeghian, Ian Reid, and Kaiming He, Ross Girshick features. Iou-Based losses perform slightly better than L1-based losses with different loss weights ( )!, Zheng Zhang, Shaoqing Ren, and different implementation settings can lead very... Practice will sometimes cause imbalance distribution in negative and positive samples since we use hooks! That the study can shed lights to better practices in making fair comparisons across different methods components! Hard samples for training object detectors about 0.5 % slightly differences, i.e., %...: object detection and instance segmentation methods by simply creating some new components and one can easily construct a object... Huiyu Wang, Longyin Wen, Hailin Shi, Liefeng Bo, and jifeng Dai toolbox supports popular contempoary... Data with different number of classes find that relaxing this rule will be.. Of experiments to study these two changes collaboration with 10+ research institutes 20+ supported methods and important... Bin Xiao, Dong Liu, and Han Hu 0.5 to 0.95 are.. And now 1333×800 is typically adopted floating point ( FP16 ) numbers, proposed in 2017 statistics (.

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