As one of the core applications in computer vision, object detection has become increasingly important in scenarios that demand high accuracy, but have limited computational resources, such asroboticsanddriverless cars. Unfortunately, many currenthigh-accuracy detectorsdo not fit these constraints. More importantly, real-world applications of object detection are run on a variety of platforms, which often demand different resources. A natural question, then, is how to design accurate and efficient object detectors that can also adapt to a wide range of resource constraints?
In “EfficientDet: Scalable and Efficient Object Detection”, accepted atCVPR 2020, we introduce a new family of scalable and efficient object detectors. Building upon our previous work on scaling neural networks (EfficientNet), and incorporating a novel bi-directional feature network (BiFPN) and new scaling rules,EfficientDetachieves state-of-the-art accuracy while being up to 9x smaller and using significantly less computation compared to prior state-of-the-art detectors. The following figure shows the overall network architecture of our models.