Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
Abstrak
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-toapples 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 [30], R-FCN [6] and SSD [25] 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.
Topik & Kata Kunci
Penulis (11)
Jonathan Huang
V. Rathod
Chen Sun
Menglong Zhu
Anoop Korattikara Balan
A. Fathi
Ian S. Fischer
Z. Wojna
Yang Song
S. Guadarrama
K. Murphy
Akses Cepat
- Tahun Terbit
- 2016
- Bahasa
- en
- Total Sitasi
- 2667×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/CVPR.2017.351
- Akses
- Open Access ✓