Heuristic Anchor Box Selection for the Single Shot Detector Algorithm
Summary
For this project, I propose an innovative approach to optimize anchor boxes for the state-of-the-art object detection algorithm Single Shot Detector. Traditionally, anchor boxes are predefined in a simplistic, hand-picked manner. For instance, Wei et al. selected anchors with ratios of 1:1, 1:2, 2:1, 1:3, and 3:1, and their implementation of Single Shot Detector 300x300 achieved a 74.3% mAP on the VOC2007 testing set.
To enhance accuracy and streamline the model, my proposal involves using k-means clustering on the ground truth boxes of the VOC2007+VOC2012 training set. This method is aimed at tailoring the anchor boxes more precisely to the data distribution, thereby identifying the most effective number of anchor boxes and their aspect ratios. Through this approach, it was discovered that the number of anchors per grid cell could be reduced from 6 to 4 while still achieving an increase in mAP by 0.7%. This refinement not only improves performance but also makes the model more efficient and data-adaptive.