Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors on ShortScience.org

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Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Huang, Jonathan and Rathod, Vivek and Sun, Chen and Zhu, Menglong and Korattikara, Anoop and Fathi, Alireza and Fischer, Ian and Wojna, Zbigniew and Song, Yang and Guadarrama, Sergio and Murphy, Kevin
Conference and Computer Vision and Pattern Recognition - 2017 via Local Bibsonomy
Keywords: dblp

Summaries/Notes 1

[link] Summary by Léo Paillier 6 years ago

_Objective:_ Compare several meta-architectures and hyper-parameters in the same framework for easy comparison.

## Architectures:

Four meta architectures:

1.  R-CNN
2.  Faster R-CNN
3.  SSD
4.  YOLO Architecture (not evaluated in the paper)

[![screen shot 2017-05-05 at 3 12 57 pm](https://cloud.githubusercontent.com/assets/17261080/25746807/5a294360-31a5-11e7-808e-d48497a16cd5.png)](https://cloud.githubusercontent.com/assets/17261080/25746807/5a294360-31a5-11e7-808e-d48497a16cd5.png)

## Results:

Very interesting to know which framework to implement or not at first glance.

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