Black-Box Optimization of Object Detector Scales

• URL: http://arxiv.org/abs/2010.15823v1
• Date: Thu, 29 Oct 2020 17:55:54 GMT
• Title: Black-Box Optimization of Object Detector Scales
• Abstract summary: We propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD. We show that by tuning the input image size and prior box anchor scale on Faster R-CNN mAP increases by 2% on PASCAL VOC 2007. On the COCO dataset with SSD there are mAP improvement in the medium and large objects, but mAP decreases by 1% in small objects.
• Score: 7.168750704705386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detectors have improved considerably in the last years by using advanced CNN architectures. However, many detector hyper-parameters are generally manually tuned, or they are used with values set by the detector authors. Automatic Hyper-parameter optimization has not been explored in improving CNN-based object detectors hyper-parameters. In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES. We show that by tuning the input image size and prior box anchor scale on Faster R-CNN mAP increases by 2% on PASCAL VOC 2007, and by 3% with SSD. On the COCO dataset with SSD there are mAP improvement in the medium and large objects, but mAP decreases by 1% in small objects. We also perform a regression analysis to find the significant hyper-parameters to tune.

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