Papers › Speed/accuracy trade-offs for modern convolutional object detectors

Speed/accuracy trade-offs for modern convolutional object detectors

30 Nov 2016CVPR 2017 7arXiv:1611.10012archive 2025-07-28

Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang song, Sergio Guadarrama, Kevin Murphy

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-to-apples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016] and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.

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IBM/MAX-Object-Detector mentioned on GitHubtf report
Qengineering/MobileNetV1_SSD_OpenCV_Caffe mentioned on GitHubBSD-3-Clause report
Qengineering/MobileNet_SSD_OpenCV_TensorFlow mentioned on GitHubtfBSD-3-Clause report
Qengineering/TensorFlow_Lite_SSD_Jetson-Nano mentioned on GitHubtfBSD-3-Clause report
Qengineering/TensorFlow_Lite_SSD_RPi_32-bits mentioned on GitHubtfBSD-3-Clause report
Qengineering/TensorFlow_Lite_SSD_RPi_64-bits mentioned on GitHubtfBSD-3-Clause report
rajatashhpa/1 mentioned on GitHubtf report
sdkchris/Exercise-Sheet-1 mentioned on GitHubtf report
tensorflow/models mentioned on GitHubtf report
tensorflow/models mentioned on GitHubtf report
yuhsijen/Object-Detector mentioned on GitHubtf report

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ObjectObject Detectionobject-detection

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Methods

1x1 ConvolutionConvolutionDense ConnectionsDropoutFaster R-CNNMax PoolingNon Maximum SuppressionPosition-Sensitive RoI PoolingR-FCNRPNReLURoIPoolSPEEDSSDSoftmax

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