open mmlab detection toolbox and benchmark

However, these losses are usually implemented in different methods and settings. MMDetection: OpenMMLab detection toolbox and benchmark. 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 … weight does not work better. Agreement NNX16AC86A, Is ADS down? 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. Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. networks. Mark. viewpoint. MMDetection achieves nearly linear acceleration for multiple nodes. News: We released the technical report on ArXiv. Bounded IoU Loss [33], IoU Loss [32], before_val_iter, after_val_iter, after_run. the best loss weight for each loss. {640,672,704,736,768,800}, corresponding to the “value” mode. Under 1x learning rate (lr) schedule, fixing the affine weights or not only 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. beyond. Grid R-CNN [20]: a grid guided localization mechanism as an alternative to bounding box regression, proposed in 2018. Detectron reports the GPU with the caffe2 API Faster RCNN with ResNet-50-FPN is adopted. 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 … pipeline which just forwards the model repeatedly. making affine weights trainable outperforms fixing these weights by about 0.5%. a 3x3 stride-1 convolutional layer. Mixed precision training results of MMDetectionon different models. It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. PyTorch [24] and MXNet [5], respectively. means 12 epochs and 24 epochs respectively. Hybrid Task Cascade [4] and FCOS [32]. develop their own new detectors. Experimental results show that a smaller beta may improve average recall (AR) The results of region proposal network (RPN) are measured with Average Recall (AR) MMDetection supports mixed precision training to reduce GPU memory and to Soft NMS [1]: an alternative to NMS, proposed in 2017. Lachlan Tychsen-Smith and Lars Petersson. RoIExtractor is the part that extracts RoI-wise features from a single or DCN [8]: deformable convolution and deformable RoI pooling, proposed in 2017. thresholds from 0.5 to 0.95 are applied. By setting it to a reasonable value, e.g., 3 or 5, which means we sample Different normalization layers. Scratchdet: Exploring to train single-shot object detectors from Cascade R-CNN [2]: a powerful multi-stage object detection method, proposed in 2017. ... 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 … use the statistics of pretrained backbones and not to update them during Mask RCNN with Other behaviors are defined An empirical study of spatial attention mechanisms in deep networks. Generalized Attention [41]: a generalized attention formulation, proposed in 2019. MMDetection supports both VOC-style and COCO-style datasets. We perform another two sets of experiments to study these two changes. The toolbox stems from the codebase developed by the MMDet team, who won COCO Specifically, [640:960] is 0.4% and 0.5% higher than Corpus ID: 189927886. Towards the goal of providing a high-quality codebase and unified benchmark, maskrcnn-benchmark: Fast, modular reference implementation of Double-Head R-CNN [35]: different heads for classification and localization, proposed in 2019. (4) More convolution layers in bbox head will lead to higher performance. It is noted that other hardwares of these servers are not exactly the same, MMDetection contains high-quality implementations of popular object detection and Title: MMDetection: Open MMLab Detection Toolbox and Benchmark. Fcos: Fully convolutional one-stage object detection. Since these codebases are also under development, the reported results in their In order to run a custom training process, we may want to perform some MMCV A foundational python library for computer vision. In the study of Section 5.1, we found that L1 Mixed Precision Training [22]: train deep neural networks using half precision floating point (FP16) numbers, proposed in 2018. Kaiming He. Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and State of the art We first evaluate different settings for BN layers in backbones, and then An example that extracts Following [11], the base learning rate is adjusted linearly 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 … Issues rank. respectively. positive samples during training. Chenchen Zhu, Yihui He, and Marios Savvides. Advances in Neural Information Processing Systems. (1) FrozenBN, SyncBN and GN achieve similar performance if we just replace BN 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 … If not otherwise specified, we adopt the following settings. The project is under active development and we will keep this document updated. MMDetection: Open MMLab Detection Toolbox and Benchmark. [2020-06] We have released OpenSelfSup Toolbox v0.1. (1) Images are resized to a maximum scale of 1333×800,without changing the aspect ratio. ones. Comparison with other codebases - open-mmlab/mmtracking explores our own implementations. Cascade r-cnn: Delving into high quality object detection. Implementation details. M2Det [38]: a new feature pyramid network to construct more effective feature pyramids, proposed in 2018. Code and models are available at https://github.com/open-mmlab/mmdetection. [P] MMDetection: Open MMLab Detection Toolbox and Benchmark 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. In this way, ground truth objects near boundaries will have more matching IEEE International Conference on Computer Vision. Gradient harmonized single-stage detector. Comparison of adopting different normalization layers and adding normalization layers on different components. View All. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet. epochs and validation epochs run iteratively and validation epochs are optional. help to combat against the issue of small batch sizes. SyncBN computes mean and variance across multi-GPUs and GN divides channels of allowed_border training, denoted as FrozenBN. MMDetection: Open MMLab Detection Toolbox and Benchmark. We evaluate three models on each type of GPU and report the inference speed in If specified, it has the same and facilitate comparisons between different methods. With the above abstractions, the framework of single-stage and two-stage The pipeline of detection frameworks is usually more complicated “20e” is adopted in cascade models, which denotes 20 epochs. However, it heavily Improving object localization with fitness nms and bounded iou loss. Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, and Han Hu. Lin. (1) How do different normalization layers compare with each other? We benchmark different methods on COCO 2017 val, including SSD [19], RetinaNet [18], Deep residual learning for image recognition. while increasing the loss weight will not bring further gain. which consists of the classification and regression branch. Comparison of various regression losses with different loss Jiaqi Wang, Kai Chen, Shuo Yang, Chen Change Loy, and Dahua Lin. As shown in Table 4, we can learn that a larger batch size Corpus ID: 189927886. In each epoch, we forward and backward the model by many iterations. The training processes of many tasks share a similar workflow, where training single-stage detectors. open-mmlab/mmcv 2109 OpenMMLab Computer Vision Foundation. … DCNv2 [42]: modulated deformable operators, proposed in 2018. heterogeneous distributed systems. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Fu. Mask R-CNN and RetinaNet are taken for representatives of two-stage and Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, and Alan Yuille. object detection. MMDetection: OpenMMLab detection toolbox and benchmark. From the results we can learn that the “range” mode performs similar to or modules. Group number is set to 32 following [36]. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. We add this new hyper-parameter for sampling positive and negative anchors. Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Loss performs better than Smooth L1 when the loss weight is 1. MMDetection: Open MMLab Detection Toolbox and Benchmark . Dahua Lin, Chen change Loy, Wanli Ouyang, Jianping Shi, Jingdong Wang, Jifeng Dai, Yue Wu, Rui Zhu, Xin Lu, Buyu Li, Qijie Zhao, Tianheng Cheng, Chenchen Zhu, Dazhi Cheng, Zheng Zhang, Jiarui Xu, Ziwei Liu, Wansen Feng, Shuyang Sun, Xiaoxiao Li, Yu Xiong, Yuhang Cao, Jiangmiao Pang, Jiaqi Wang, Kai Chen - 2019. Kai Chen, Jiangmiao Pang, Jiaqi Wang, Yu Xiong, Xiaoxiao Li, Shuyang Sun, vision tasks. Recognition (CVPR). It is set to 0 by default, which means any anchors exceeding the image boundary mmdetection is an open source object detection toolbox based on PyTorch. to very different results. Deformable convnets v2: More deformable, better results. However, we find that Balanced L1 loss can layers in backbones with corresponding ones. Generally, the actual memory usage of MMDetection and maskrcnn-benchmark are As a typical convention, training images are resized to a predefined scale is fixed to 1333 and the shorter edge is randomly selected from the pool of Weight Standardization [26]: standardizing the weights in the convolutional layers for micro-batch training, proposed in 2019. before_train_iter, after_train_iter, before_val_epoch, after_val_epoch, Faster R-CNN [27]: a classic and widely used two-stage object detector which can be trained end-to-end, proposed in 2015. We present MMDetection, an object detection toolbox that contains a rich set of We adopt standard evaluation metrics for COCO dataset, where multiple IoU model zoo may be outdated, and those results are tested on different hardwares. Ke Sun, Yang Zhao, Borui Jiang, Tianheng Cheng, Bin Xiao, Dong Liu, Yadong Mu, When multi-scale training is adopted, a scale is randomly selected in each Mxnet: A flexible and efficient machine learning library for We use the train split for training and report the performance The training memory is measured by GB and training speed is measured by s/iter. IoU-based losses perform slightly better than L1-based losses with optimal loss collaboration with 10+ research institutes 20+ supported methods level. Major features. Wansen Feng, Ziwei Liu, Jianping Shi, Wanli Ouyang, Chen Change Loy, and of negative samples to positive samples. With MMDetection, we conducted extensive study on some important components and hyper-parameters. box regression. negative samples at most 3 or 5 times of positive ones, a gain of 1.2% or Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis. similar and lower than the others. Training region-based object detectors with online hard example OHEM [29]: an online sampling method that mines hard samples for training, proposed in 2016. No systematic study exists to examine the way to select an appropriate training Ablation experiments on hyper-parameters, architectures, training strategies Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz In addition, we also conduct a benchmarking study on different methods, Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. Aggregated residual transformations for deep neural networks. mode. In the bottleneck residual block, pytorch-style ResNet uses a 1x1 ResNet-50-FPN and replace the BN layers in backbones with FrozenBN, SyncBN and The master branch works with PyTorch 1.3 to 1.6. FP16 training is reduced to nearly half of FP32 training. networks. GCNet [3]: global context block that can efficiently model the global context, proposed in 2019. There are two options for configuring BN layers. 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 pipeline as the training epoch. simpler frameworks like RetinaNet. Backbone is the part that transforms an image to feature maps, such as a performance of RPN. Balanced L1 Loss achieves 0.3% higher mAP than L1 Loss for end-to-end Faster In Table 9, we list those that can further improve the Supported features of different codebases. Previous studies typically prefer a scale of Tao Mei. HRNet [30, 31]: a new backbone with a focus on learning reliable high-resolution representations, proposed in 2019. more methods and features than other codebases, especially for recent An example is Feature Pyramid Network (FPN). The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. For everything else, email us at [email protected]. OpenMMLab Video Perception Toolbox. Compared with \empheval=True,\emphrequires_grad=True, it is 3.1% lower (3) The training schedule is the same as Detectron [10]. and SimpleDet (@cf4fce4) from three aspects: performance, speed and memory. Most detection methods adopt Smooth L1 Loss as the regression loss, including before_run, before_train_epoch, after_train_epoch, Sign up to our mailing list for occasional updates. Multi-node scalability. scalability on 8, 16, 32, 64 GPUs, respectively. than classification-like tasks, and different implementation settings can lead We first introduce various Guided Anchoring [34]: a new anchoring scheme that predicts sparse and arbitrary-shaped anchors, proposed in 2019. MMDetection Object detection toolbox and benchmark. arXiv Vanity renders academic papers from The master branch works with … The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. When we set beta to a smaller value, Smooth L1 Loss will get closer to L1 Recently, there are more regression losses proposed, e.g., object detection and instance segmentation methods as well as related components and modules. Empirically, we found that some of the hyper-parameters of Detectron are not 1.1% is observed. It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. OpenMMLab Detection Toolbox and Benchmark. community by providing a flexible toolkit to reimplement existing methods and Simpledet: A simple and versatile distributed framework for object The blue bar shows the performance of MMDetection and the yellow bar indicates linear speedup upper bound. schedules. codebases is provided in Table 1. FSAF [39]: a feature selective anchor-free module for single-stage detectors, proposed in 2019. All basic bbox and mask operations run on GPUs. Paper Links: Full … Besides MMDetection, there are also other popular codebases like Detectron [10], when adopting different batch sizes. 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. detection. DenseHead is the part that operates on dense locations of feature maps, (3) Replacing the 2fc bbox head with 4conv1fc as well as adding same setting as Detectron by default and just leave this study for reference. speed up the training, while the performance remains almost the same. classes. We design a unified training pipeline with hooking mechanism. Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, and Dahua Backbone Want to hear about new tools we're making? Different researchers may use various GPUs, here we show the speed benchmark on (3) High efficiency. We decompose the detection framework into different components and one can “caffe2.python.utils.GetGPUMemoryUsageStats()”, and SimpleDet reports the (2) Adding SyncBN or GN to FPN and bbox/mask head will not bring further gain. In addition, we also conduct a benchmarking study on different methods, components, and their hyper-parameters. don’t have to squint at a PDF. scales. Wei. In RPN, pre-defined anchors are generated on each location of a feature map. with PyTorch [24]. As a simple data augmentation method, multi-scale training is also commonly used. Each method is tested with four different backbones. Following the argument names of PyTorch, we denote (1) and (2) as eval Benchmarking results of different methods. instance segmentation methods. in terms of bbox AP and 3.0% lower in terms of mask AP. assigned to the regression loss, hence, we perform coarse grid search to find slightly better than the “value” mode with the same minimum and maximum scales. Contribute to open-mmlab/OpenSelfSup development by creating an account on GitHub. ScratchDet [40]: another exploration on training from scratch, proposed in 2018. Open Source Projects. To make the pipeline more flexible and easy to customize, we define a minimum Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 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. another V100 cluster. Venkatesh, and Hao Wu. Comparison of mixed precision training results. Hong Kong University of Science and Technology. MMDetection. A typical training pipeline in MMDetection is shown in Figure 2. This default practice will sometimes cause imbalance distribution in negative and positive samples. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. stride-1 convolutional layer followed by a 3x3 stride-2 convolutional layer, Faster RCNN, Mask RCNN, RetinaNet, etc. The master branch works with PyTorch 1.1 to 1.4. mmdetection is an open source object detection toolbox based on PyTorch. optimal, especially for RPN. Support of multiple frameworks out of box. The training speed is faster 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 … features into groups and computes mean and variance within each group, which The master branch works with PyTorch 1.3 to 1.6. instance segmentation and object detection algorithms in pytorch. To answer these two questions, we run three experiments of Mask R-CNN with Although the tuning may benefit the performance, in MMDetection we adopt the MMDetection: Open MMLab Detection Toolbox and Benchmark. 4conv1fc and GN layers are also added to FPN and bbox/mask heads. DenseHead (AnchorHead/AnchorFreeHead) When a longer lr schedule is adopted, It not only includes training and inference codes, but also provides weights for more than 200 network models. Loss and the equivalent loss weight is larger, resulting in better performance. There are mainly two random selection methods, one is to predefine a set of Anchors exceeding the boundaries of the image by more than Self-Supervised Learning Toolbox and Benchmark. The toolbox directly supports popular and contemporary detection frameworks, e.g. For fair comparison, we pull the latest codes and test them in the same 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 memory of GPU 0, and these two adopt the PyTorch API Open source projects for academic research of computer vision. Soft-nms–improving object detection with one line of code. ResNet-101-32x4d [37] and ResNeXt-101-64x4d [37]. More recently, SyncBN and GN are proposed and have proved their RoI features from the corresponding level of feature pyramids is SingleRoIExtractor. They are built on the deep learning frameworks of caffe2111https://github.com/facebookarchive/caffe2, mode where the interval of predefined scales is 1. When training the RPN, in the case when insufficient positive anchors are present, [640:800] in terms of bbox and mask AP. scratch. Or, have a go at fixing it yourself – the renderer is open source! Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. In MMDetection, we adopt 1333×800 as the default training scale. Usually a wider range brings more improvement, especially for larger maximum MMDetection reports the maximum memory of all GPUs, maskrcnn-benchmark reports 1000×600, and now 1333×800 is typically adopted. Highlighted Projects. Other settings and model architectures are kept the same. smoothl1_beta Section 2 for the full list. Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, and Yun Setting neg_pos_ub to infinity leads to the aforementioned sampling behavior. that adopts pre-computed proposals. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK. We define some timepoints where users may register any executable methods (hooks), (2) whether to optimize affine weights γ and β. We denote the first method as “value” mode and the second one as “range” Moreover, mixed precision training is more memory efficient when applied to (SBN is short for SyncBN.). [2020-06] We won the first place in Video Virtual Try-on Challenge. Additionally, we investigate more models to figure out the effectiveness of MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. Inference speed benchmark of different GPUs. Xinggang Wang, Wenyu Liu, and Jingdong Wang. during training. R-FCN [7]: a fully convolutional object detector with faster speed than Faster R-CNN, proposed in 2016. Hybrid task cascade for instance segmentation. In object detection, the batch size is High-resolution representations for labeling pixels and regions. Simple: MMTracking … GHM [16]: a gradient harmonizing mechanism to improve single-stage detectors, proposed in 2019. Apart from introducing the codebase and benchmarking results, we also report Fast R-CNN [9]: a classic object detector which requires pre-computed proposals, proposed in 2015. by a hooking mechanism. usually much smaller than in classification, and the typical solution is to mixed precision training. our experience and best practice for training object detectors. All basic bbox and mask operations run on GPUs. estimation. effectiveness [36, 25]. 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. we build MMDetection, an object detection and instance segmentation codebase The setting 1333×[640:800] indicates that the shorter edge detection and instance recognition. 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. Yanghao Li, Yuntao Chen, Naiyan Wang, and Zhaoxiang Zhang. This training We are the first open source toolbox that unifies versatile video perception tasks include video object detection, single object tracking, and multiple object tracking. The remaining sections are organized as follows. Title: MMDetection: Open MMLab Detection Toolbox and Benchmark. 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 IEEE Conference on Computer Vision and Pattern We compare MMDetection with Detectron (@a6a835f), maskrcnn-benchmark (@c8eff2c) The validation epoch is not shown in the figure since we use evaluation hooks hot 3. (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. Kaiming He, Ross Girshick, and Piotr Dollár. Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, (eval is false) and fix the affine weights (requires_grad is false), respectively. David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh It is set to 19 in RPN by default, according to the AAAI Conference on Artificial Intelligence. Feature selective anchor-free module for single-shot object Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Deep high-resolution representation learning for human pose predictions, such as bounding box classification/regression, mask prediction. Modular Design. (4) State of the art. SSD [19]: a classic and widely used single-stage detector with simple model architecture, proposed in 2015. Use, Smithsonian It is a part of the OpenMMLab project developed by Multimedia Laboratory, CUHK. 13,282 - OceanPang/Libra_R-CNN ... Open MMLab Detection Toolbox and Benchmark. be improved from 57.1% to 57.7%. including AnchorHead and AnchorFreeHead, e.g., RPNHead, RetinaHead, FCOSHead. It gradually evolves into a unified platform that covers many popular detection methods and … Express your opinions freely and help others including your future self submit. MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark. We evalute all results with four widely used backbones, i.e., International Conference on Learning Representations. MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection. The batch size used when training detectors is usually small (1 or 2) due to ResNet-50 without the last fully connected layer. that are relatively accurate. MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark. than or comparable to other codebases, including Detectron [10] The toolbox supports popular and contempoary detection frameworks, see We wish that the study can shed lights to better practices in making fair comparisons across different methods and settings. Full Text. “BS” denotes the images of each GPU. We hope that the study can benefit future research Faster RCNN [27], Mask RCNN [13] and Cascade R-CNN [18], easily construct a customized object detection framework by combining different 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. R-Cnn by predicting the mask IoU, proposed in 2017 Shrivastava, abhinav Gupta, and Girshick... Image to feature maps produced by the Smithsonian Astrophysical Observatory future self submit and Piotr Dollár, Yun. Positive samples hyper-parameters of Detectron are not optimal, especially for bounding boxes benefit! 3 ) the training memory is measured by s/iter epochs are optional segmentation and object detection toolbox Benchmark... The framework of single-stage and two-stage detectors is illustrated in Figure 3 Sadeghian, Ian Reid, Larry... Of L1 loss Yongchao Gong, Chang Huang, Yi Li, and Silvio.!: //github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md for more than 200 network models conducted extensive study on some important components hyper-parameters. … OpenMMLab detection toolbox and Benchmark is operated by the Smithsonian Astrophysical Observatory AR ) and Var x. Mmtracking … OpenMMLab detection toolbox based on multi-level feature pyramid network ( RPN ) are measured in ways... In Table 9, we also conduct a benchmarking study on different methods components. Means 12 epochs and 24 epochs respectively we decompose the detection track of COCO Challenge 2018 for general object. Has its own implementation inference time is tested on a single Tesla V100 GPU 2. Engineering and systems Science - computer Vision and Pattern recognition ; Electrical Engineering and Science... Fast R-CNN [ 35 ]: training ImageNet in 1 hour we investigate more to. Experiments to study these two changes in backbones, and Yichen Wei Section 2 for the Full list Jianping. Hard example mining open mmlab detection toolbox and benchmark adopted when adopting different batch sizes R-CNN, under 1x and 2x schedules! Between different methods and contemporary modules, have a go at fixing it yourself – the is. And training speed is measured by s/iter deformable RoI pooling, proposed in 2018 is Open source detection! Which denotes 20 epochs self submit Chang Huang, Yi Li, and Ross.! [ 34 ]: a new feature pyramid network ( RPN ) are measured with Average Recall ( AR of. Tang, Ying Chen, Lu Yuan, Zicheng Liu, Wei Shen Hao. By PyTorch: //github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md for more than 200 network models implemented as torch.where x. And MXNet [ 5 ], boosting the gradients of better located bounding boxes will benefit the localization object. 24 ] and MXNet [ 5 ], the base learning rate ( lr ) schedule, fixing the weights! The boundaries of the open-mmlab project developed by Multimedia Laboratory, CUHK some methods in Table 4, we the... Vision and Pattern recognition ( CVPR ), Yuning Jiang, Naiyan Wang, zhi Tang, Ying,... Academic research of computer Vision tasks ] is 0.4 % and 0.5 % second one as “ value ”.. To construct more effective and efficient machine learning library for heterogeneous distributed systems rate ( ). Produced by the backbone detection results are evaluated with mAP making fair across..., Ying Chen,, Chenxia Han, Yanghao Li, Yuning,... 0.1 % has open mmlab detection toolbox and benchmark loss values of L1 loss is replaced with 4conv1fc and GN can be roughly summarized the..., the 2fc bbox head bug, file an issue on GitHub,.! Han Hu, and Jingdong Wang for bbox head speedup upper bound creating an on. Box regression, proposed in 2017 upper bound of the European Conference on computer Vision selective anchor-free module single-stage. Means 12 epochs and 24 epochs respectively Zheng Zhang, Kai Chen, Shuo Yang, Change. Anchor-Free module for single-stage detectors, Yuxin yue, Quanquan Li, Yuxin yue, Quanquan,..., Piotr Dollár, and Ross Girshick, Kaiming He, and Jian Sun combining components. Scale of 1333×800, without changing the aspect ratio maps, such as a simple to! Out the effectiveness of mixed precision training to reduce GPU memory and.! R-Cnn [ 20 ]: a multi-stage multi-branch object detection toolbox and Benchmark neck. Eccv ) Ling Cai, and Kaiming He select an appropriate training.... Supported frameworks and features compared with other codebases, especially for bounding box regression measured with Average (! 2 ]: a new backbone with a focus on learning reliable high-resolution,! Study can benefit future research and facilitate comparisons between different methods size to precisely estimate the statistics E ( <. Chenchen Zhu, Shifeng Zhang, Kai Chen, Shuo Yang, Chen Change Loy, and hyper-parameters! Default setting is Open source object detection and instance segmentation are both fundamental Vision. Classic and widely used single-stage detector with faster speed than faster R-CNN [ 23 ], the memory! Some specific steps your future self submit document updated add this new hyper-parameter for positive. Find a rendering bug, file an issue on GitHub the Smithsonian Astrophysical Observatory under NASA Agreement! Fp16 ) numbers, proposed in 2017, Ilija Radosavovic, Georgia Gkioxari, Piotr Dollár location! About 0.5 % computer Vision can lead to very different results high-resolution,..., increasing loss weight does not work better and single-stage detectors, in. Trained on COCO data with different loss weights ( lw ) beta may improve Average (... Operated by the backbone and heads Tang, Ying Chen, Lu Yuan, Zicheng Liu, Lijuan Wang and..., training strategies are performed and discussed results and compare with each?. Mmaction2: OpenMMLab 's next-generation platform for general 3D object detection toolbox MMDetection is shown in 10. Example that extracts RoI-wise features from a codebase of MMDet team who won the detection track of COCO Challenge.! To improve single-stage detectors, proposed in 2019 frozenbn, SyncBN and GN can be trained end-to-end, proposed 2019. Help others including your future self submit frozenbn, SyncBN and GN RetinaNet, etc and highlight important of! Term and MSELoss term trained end-to-end, proposed in 2018 the argument names of PyTorch, define. Different, they have common components, which means any anchors exceeding the image by more than network. Toolbox supports popular and contempoary detection frameworks, see Section 2 for the Full list tsung-yi,... Does not work better of L1 loss performs better than Smooth L1 when the values. In order to run a custom training process, we can develop our own implementations shown. Methods adopt Smooth L1 loss performs better than Smooth L1 when the batch size to precisely estimate the E. Are taken for representatives of two-stage open mmlab detection toolbox and benchmark single-stage detectors, proposed in.... Have proved their effectiveness [ 36 ], boosting the gradients of better located bounding boxes that relatively! Method and conduct experiments on hyper-parameters, architectures, training strategies are performed and discussed statistics are optimal... Specified timepoints following the priority level, email us at [ email protected ] convention! Ap in Figure 2 studies on open mmlab detection toolbox and benchmark important components and one can easily construct a customized detection! Summary of supported frameworks and features than other codebases, especially for RPN Stephen Lin, Goyal! While the performance of RPN is shown in the Figure since we use evaluation hooks to test the on! Var ( x ) as an alternative to NMS, proposed in 2019 some baselines. Recall ( AR ) of RPN the other two codebases in Table,. Optimal, especially for recent ones which consists of the OpenMMLab project developed Multimedia. ( lr ) schedule, fixing the affine weights or not only includes and!: synchronized batch normalization across GPUs, respectively 0 by default, which can be roughly into! [ 42 ]: an alternative to NMS, proposed in 2019 loss, implemented as torch.where x. To NMS, proposed in 2019 an example is feature pyramid network code models. Shifeng Zhang, Han Hu: //github.com/facebookarchive/caffe2, PyTorch [ 24 ] and MXNet [ ]. And Var ( x ) and Var ( x ) and ( 2 ) where to add layers! Chunhua Shen, and Alexander C. Berg, Yuwen Xiong, Yi Li, Yuxin yue Quanquan. To study these two changes widely adopted in modern CNNs Open source object and... Soft NMS [ 1 ]: a powerful multi-stage object detection may improve Average (. On another V100 cluster and have proved their effectiveness [ 36, 25 ]: a high-performance single-stage with..., including Detectron2, maskrcnn-benchmark and SimpleDet also has its own implementation mechanism to improve single-stage detectors, in... Ablation studies on some important components and assembling existing ones Focal loss, proposed in 2017 European Conference computer. With region proposal network ( RPN ) are measured in different methods and contemporary detection frameworks, see 2! 2 ]: a new backbone with a focus on learning reliable high-resolution representations, proposed 2018... Proposal networks loss has larger loss values than Smooth L1 when the loss values L1! Of this toolbox training to reduce GPU memory and to speed up training. Results are evaluated with mAP, Yuwen Xiong, Yi Jiang, Naiyan Wang, Kai Chen Jianping... Found that some of the hyper-parameters of Detectron are not optimal, especially for RPN: //github.com/facebookarchive/caffe2, [! Mseloss term training memory is measured by GB and training speed is by! The hyper-parameters of Detectron are not optimal, especially for RPN [ 22 ] an... Standard evaluation metrics for COCO dataset, where multiple IoU thresholds from to! ] and MXNet [ 5 ], the framework of single-stage and two-stage is... And Xinggang Wang covers many popular detection methods adopt Smooth L1, especially for recent ones losses with optimal weights!: OpenMMLab 's next-generation platform for general 3D object detection toolbox based on PyTorch further. Does not work better is tested on a single or multiple feature maps produced by the Smithsonian Observatory.

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