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We present region-based, fully convolutional networks for accurate andefficient object detection. In contrast to previous region-based detectors suchas Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds oftimes, our region-based detector is fully convolutional with almost allcomputation shared on the entire image. To achieve this goal, we proposeposition-sensitive score maps to address a dilemma betweentranslation-invariance in image classification and translation-variance inobject detection. Our method can thus naturally adopt fully convolutional imageclassifier backbones, such as the latest Residual Networks (ResNets), forobject 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 isachieved at a test-time speed of 170ms per image, 2.5-20x faster than theFaster R-CNN counterpart. Code is made publicly available at:https://github.com/daijifeng001/r-fcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks