  The increase in data collection has made data annotation an interesting and
valuable task in the contemporary world. This paper presents a new methodology
for quickly annotating data using click-supervision and hierarchical object
detection. The proposed work is semi-automatic in nature where the task of
annotations is split between the human and a neural network. We show that our
improved method of annotation reduces the time, cost and mental stress on a
human annotator. The research also highlights how our method performs better
than the current approach in different circumstances such as variation in
number of objects, object size and different datasets. Our approach also
proposes a new method of using object detectors making it suitable for data
annotation task. The experiment conducted on PASCAL VOC dataset revealed that
annotation created from our approach achieves a mAP of 0.995 and a recall of
0.903. The Our Approach has shown an overall improvement by 8.5%, 18.6% in mean
average precision and recall score for KITTI and 69.6%, 36% for CITYSCAPES
dataset. The proposed framework is 3-4 times faster as compared to the standard
annotation method.
