all methods have inferior performance on ‘boat’ and ‘bottle’. However, RON improves performance of these categories by significant margins: 4.0 points improvement for ‘boat’ and 7.1 points improvement for ‘bottle’. In summary, performance of 17 out of 20 categories has been improved by RON.

To understand the performance of RON in more detail, we use the detection analysis tool from [@detann]. Figure \[det\_error\] shows that our model can detect various object categories with high quality. The recall is higher than 85%, and is much higher with the ‘weak’ (0.1 jaccard overlap) criteria.

PASCAL VOC 2012
---------------

We compare RON against top methods on the comp4 (outside data) track from the public leaderboard on PASCAL VOC 2012. The training data is the union set of all VOC 2007, VOC 2012 train and validation datasets, following [@fasterrcnn][@frcnn][@ssd]. We see the same performance trend as we observed on VOC 2007 test. The results, as shown in Table \[voc12\], demonstrate that our model performs the best on this dataset. Compared with Faster R-CNN and other variants [@ohem][@hypernet], the proposed network is significantly better, mainly due to the reverse connection and the use of boxes from multiple feature maps.

MS COCO
-------

To further validate 