Speed/accuracy trade-offs for modern convolutional object detectors

CVPR 2017 Jonathan HuangVivek RathodChen SunMenglong ZhuAnoop KorattikaraAlireza FathiIan FischerZbigniew WojnaYang SongSergio GuadarramaKevin Murphy

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Object Detection COCO test-dev Faster R-CNN box AP 34.7 # 55