SSD300[@ssd]                 trainval35k     38.0       20.5       20.8
  SSD500[@ssd]                 trainval35k     43.7       24.7       24.4
  RON320                        trainval       44.7       22.7       23.6
  RON384                        trainval       46.5       25.0       25.4
  RON320++                      trainval       47.5       25.9       26.2
  RON384++                      trainval     **49.5**   **27.1**   **27.4**
  --------------------------- ------------- ---------- ---------- ----------

  : MS COCO test-dev2015 detection results.[]{data-label="time_comp"}

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With the standard COCO evaluation metric, Faster R-CNN scores 21.9% AP, and RON improves it to 27.4% AP. Using the VOC overlap metric of IoU $\geq$0.5, RON384++ gives a 5.8 points boost compared with SSD500. It is also interesting to note that with 320$\times$320 input size, RON gets 26.2% AP, improving the SSD with 500$\times$500 input size by 1.8 points on the strict COCO AP evaluation metric.

We also compare our method against Fast R-CNN with online hard example mining (OHEM) [@ohem], which gives a considerable improvement on Fast R-CNN. The OHEM method also adopts recent bells and whistles to further improve the detection performance. The best result of OHEM is 25.5% AP (OHEM++). RON gets 27.4% AP, which demonstrates that the proposed network is more competitive on large dataset.

From MS COCO to PASCAL VOC
--------------------------

Large-scale dataset is important for improving deep neural networks. In this experiment, we investigate how the MS COCO 