Transcription of EfficientDet: Scalable and Efficient Object Detection
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EfficientDet: Scalable and Efficient Object Detection
EfficientDet: Scalable and Efficient Object DetectionMingxing Tan Ruoming Pang Quoc V. LeGoogle Research, Brain Team{tanmingxing, rpang, efficiency has become increasingly important incomputer vision. In this paper, we systematically study neu-ral network architecture design choices for Object detec-tion and propose several key optimizations to improve ef-ficiency. First, we propose a weighted bi-directional fea-ture pyramid network (BiFPN), which allows easy and fastmulti-scale feature fusion; Second, we propose a compoundscaling method that uniformly scales the resolution, depth,and width for all backbone, feature network, and box/classprediction networks at the same time. Based on these op-timizations and EfficientNet backbones, we have developeda new family of Object detectors, called EfficientDet, whichconsistently achieve much better efficiency than prior artacross a wide spectrum of resource constraints.}
towards more accurate object detection; meanwhile, state-of-the-art object detectors also become increasingly more expensive. For example, the latest AmoebaNet-based NAS-FPN detector [42] requires 167M parameters and 3045B FLOPs (30x more than RetinaNet [21]) to achieve state-of-the-art accuracy. The large model sizes and expensive com-
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