  We present R-FCN-3000, a large-scale real-time object detector in which
objectness detection and classification are decoupled. To obtain the detection
score for an RoI, we multiply the objectness score with the fine-grained
classification score. Our approach is a modification of the R-FCN architecture
in which position-sensitive filters are shared across different object classes
for performing localization. For fine-grained classification, these
position-sensitive filters are not needed. R-FCN-3000 obtains an mAP of 34.9%
on the ImageNet detection dataset and outperforms YOLO-9000 by 18% while
processing 30 images per second. We also show that the objectness learned by
R-FCN-3000 generalizes to novel classes and the performance increases with the
number of training object classes - supporting the hypothesis that it is
possible to learn a universal objectness detector. Code will be made available.
