Redifining One-Shot Object Detector For Two Class Problem

Description: One shot object detectors face the anchor problems when the dataset is biased and high variance in the box sizes. To tackle that issue, I developed a hierarchial clustering approach to adjust the anchors height and width viz. Anchor Search algorithm for assigning and learning optimal anchors for two different classes individually. This was developed by taking into consideration the two classes i.e person body and head which somewhat vary a lot in the size and aspect ratio. Also entangled the training of head and body of person together instead of treating them as two different classes 3% mAP gains were observed after final bench-marking the improvements.

Results on the final improvements.

Detector Model	AP Head	AP Body
In-house Vanilla Detector	88.5%	87.5%
In-house Vanilla Detector + Anchor Search	90.02%	90.11%
In-house Vanilla Detector + Anchor Search + Entangle	91%	91.9%