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In contrast to previous region-based detectors such\nas Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of\ntimes, our region-based detector is fully convolutional with almost all\ncomputation shared on the entire image. To achieve this goal, we propose\nposition-sensitive score maps to address a dilemma between\ntranslation-invariance in image classification and translation-variance in\nobject detection. Our method can thus naturally adopt fully convolutional image\nclassifier backbones, such as the latest Residual Networks (ResNets), for\nobject detection. We show competitive results on the PASCAL VOC datasets (e.g.,\n83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is\nachieved at a test-time speed of 170ms per image, 2.5-20x faster than the\nFaster R-CNN counterpart. 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