dataset, we compare RON against seminal Fast R-CNN [@frcnn], Faster R-CNN [@fasterrcnn], and the most recently proposed SSD [@ssd]. All methods are trained on VOC2007 trainval and VOC2012 trainval, and tested on VOC2007 test dataset. During training phase, we initialize the parameters for all the newly added layers by drawing weights from a zero-mean Gaussian distribution with standard deviation 0.01. All other layers are initialized by standard VGG-16 model [@frcnn]. We use the $10^{-3}$ learning rate for the first 90k iterations, then we decay it to $10^{-4}$ and continue training for next 30k iterations. The batch size is 18 for $320$$\times320$ model according to the GPU capacity. We use a momentum of 0.9 and a weight decay of 0.0005.

Table \[voc07\] shows the result comparisons of the methods[^2]. With 320$\times$320 input size, RON is already better than Faster R-CNN. By increasing the input size to 384$\times$384, RON gets 75.4% mAP, outperforming Faster R-CNN by a margin of 2.2%. RON384 is also better than SSD with input size 500$\times$500. Finally, RON could achieve high mAPs of 76.6% (RON320++) and 77.6% (RON384++) with multi-scale testing, bounding box voting and flipping [@ion].

Small objects are challenging for detectors. As shown in Table \[voc07\], 