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EfficientDet

swMATH ID: 40818
Software Authors: Mingxing Tan, Ruoming Pang, Quoc V. Le
Description: EfficientDet: Scalable and Efficient Object Detection. Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multiscale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations and better backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In particular, with single model and single-scale, our EfficientDet-D7 achieves state-of-the-art 55.1 AP on COCO test-dev with 77M parameters and 410B FLOPs, being 4x - 9x smaller and using 13x - 42x fewer FLOPs than previous detectors. Code is available at https://github.com/google/automl/tree/master/efficientdet
Homepage: https://arxiv.org/abs/1911.09070
Source Code:  https://github.com/google/automl/tree/master/efficientdet
Related Software: MS-COCO; SqueezeNet; EfficientNet; YOLO; SSD; YOLOv4; MobileNets; ShuffleNet; MobileNetV2; Faster R-CNN; ImageNet; CIDEr; BLEU; Adam; Rouge; LVIS; SMOTE; AMC; CCNet; AlexNet
Cited in: 2 Documents

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