  We adapted the join-training scheme of Faster RCNN framework from Caffe to
TensorFlow as a baseline implementation for object detection. Our code is made
publicly available. This report documents the simplifications made to the
original pipeline, with justifications from ablation analysis on both PASCAL
VOC 2007 and COCO 2014. We further investigated the role of non-maximal
suppression (NMS) in selecting regions-of-interest (RoIs) for region
classification, and found that a biased sampling toward small regions helps
performance and can achieve on-par mAP to NMS-based sampling when converged
sufficiently.
