  It is well known that contextual and multi-scale representations are
important for accurate visual recognition. In this paper we present the
Inside-Outside Net (ION), an object detector that exploits information both
inside and outside the region of interest. Contextual information outside the
region of interest is integrated using spatial recurrent neural networks.
Inside, we use skip pooling to extract information at multiple scales and
levels of abstraction. Through extensive experiments we evaluate the design
space and provide readers with an overview of what tricks of the trade are
important. ION improves state-of-the-art on PASCAL VOC 2012 object detection
from 73.9% to 76.4% mAP. On the new and more challenging MS COCO dataset, we
improve state-of-art-the from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection
Challenge, our ION model won the Best Student Entry and finished 3rd place
overall. As intuition suggests, our detection results provide strong evidence
that context and multi-scale representations improve small object detection.
