  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.
