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Speed/accuracy trade-offs for modern convolutional object detectors (CVPR2017) #503

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hurutoriya opened this Issue Nov 13, 2017 · 0 comments

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@hurutoriya
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hurutoriya commented Nov 13, 2017

一言でいうと

  • 特定の目的に沿った、速度、メモリ、精度のバランスを実現するために物体検出アーキテクチャはどれを選択すべきか?という研究
  • 基本的に速度と精度はトレードオフであり、最速はSSDs, MobileNet, 最高精度はFaster R-CNN w/Inception Resnet at stride 8

論文リンク

http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_SpeedAccuracy_Trade-Offs_for_CVPR_2017_paper.pdf

著者/所属機関

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

投稿日付(yyyy/MM/dd)

概要

  • メタアーキテクチャ: Faster RCNN,RFCN,SSD間の比較
  • 特徴抽出器: Inception, ResNet, VGG, MobileNet間の比較
    image

新規性・差分

手法

結果

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