Transcription of EfficientDet: Scalable and Efficient Object Detection
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EfficientDet: Scalable and Efficient Object Detection
EfficientDet: Scalable and Efficient Object Detection Mingxing Tan Ruoming Pang Quoc V. Le Google Research, Brain Team {tanmingxing, rpang, [ ] 27 Jul 2020. Abstract EfficientDet-D7. D6. D5. model efficiency has become increasingly important in 50. AmoebaNet + NAS-FPN + AA. D4. computer vision. In this paper, we systematically study neu- D3. ral network architecture design choices for Object Detection 45 ResNet + NAS-FPN. and propose several key optimizations to improve efficiency. D2. COCO AP. First, we propose a weighted bi-directional feature pyra- mid network (BiFPN), which allows easy and fast multi- RetinaNet 40 D1. scale feature fusion; Second, we propose a compound scal- Mask R-CNN. ing method that uniformly scales the resolution, depth, and AP FLOPs (ratio). 35. width for all backbone, feature network, and box/class pre- EfficientDet-D0 YOLOv3 [34] 71B (28x).}
Figure 1: Model FLOPs vs. COCO accuracy – All num-bers are for single-model single-scale. Our EfficientDet achieves new state-of-the-art 55.1% COCO AP with much fewer parameters and FLOPs than previous detectors. More studies on different backbones and FPN/NAS-FPN/BiFPN are in Table4and5. Complete results are in Table2.
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