object detection framework, which could associate the merits of region-based and region-free approaches. Furthermore, recent complementary advances such as hard example mining [@ohem], bounding box regression [@girshick2014rich] and multi-layer representation [@hypernet][@ion] could be naturally employed.

**Contributions**. We make the following contributions:

1.  We propose RON, a fully convolutional framework for end-to-end object detection. Firstly, the reverse connection assists the former layers of CNNs with more semantic information. Second, the objectness prior gives an explicit guide to the searching of objects. Finally, the multi-task loss function enables us to optimize the whole network end-to-end on detection performance.

2.  In order to achieve high detection accuracy, effective training strategies like negative example mining and data augmentation have been employed. With low resolution 384$\times$384 input size, RON achieves state-of-the-art results on PASCAL VOC 2007, with a 81.3% mAP, VOC 2012, with a 80.7% mAP, and MS COCO, with a 27.4% mAP.

3.  RON is time and resource efficient. With 1.5G GPU memory, the total feed-forward speed is 15 FPS, 3$\times$ faster than the seminal Faster R-CNN. Moreover, we conduct extensive design choices like the layers combination, with/without objectness prior, and other variations.

Related Work
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Object detection is a fundamental and heavily-researched task 