  We present region-based, fully convolutional networks for accurate and
efficient object detection. In contrast to previous region-based detectors such
as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of
times, our region-based detector is fully convolutional with almost all
computation shared on the entire image. To achieve this goal, we propose
position-sensitive score maps to address a dilemma between
translation-invariance in image classification and translation-variance in
object detection. Our method can thus naturally adopt fully convolutional image
classifier backbones, such as the latest Residual Networks (ResNets), for
object detection. We show competitive results on the PASCAL VOC datasets (e.g.,
83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is
achieved at a test-time speed of 170ms per image, 2.5-20x faster than the
Faster R-CNN counterpart. Code is made publicly available at:
https://github.com/daijifeng001/r-fcn
