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.}
comparison on COCO dataset [25]. Under similar accu-racy constraint, our EfficientDet uses 28x fewer FLOPs than YOLOv3 [34], 30x fewer FLOPs than RetinaNet [24], and 19x fewer FLOPs than the recent ResNet based NAS-FPN [10]. In particular, with single-model and single test-time scale, our EfficientDet-D7 achieves state-of-the-art 55.1 AP
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