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). diction networks at the same time. Based on these optimiza- YOLOv3 EfficientDet-D1 RetinaNet [24] 97B (16x).}
repeated blocks repeated blocks Figure 2: Feature network design – (a) FPN [23] introduces a top-down pathway to fuse multi-scale features from level 3 to 7 (P 3 - P 7); (b) PANet [26] adds an additional bottom-up pathway on top of FPN; (c) NAS-FPN [10] use neural architecture
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