# Black-Box Optimization of Object Detector Scales

Source: [Black-Box Optimization of Object Detector Scales](<https://www.sota2.com/research/paper/68fa689f1e209131cea1d2d5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:41:59.280Z

Authors: Mohandass Muthuraja, Octavio Arriaga, Paul Pl\\"oger, Frank Kirchner, Matias Valdenegro-Toro

Published: 2020-10-29T17:55:54.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.15823>)

[PDF](<https://arxiv.org/pdf/2010.15823.pdf>)