EfficientDet

EfficientDet is a family of object detection models developed by Google Research that significantly improves efficiency without sacrificing accuracy. Built upon the EfficientNet backbone, it introduces a weighted bi-directional feature pyramid network (BiFPN) and a novel compound scaling method. This architecture allows the model to uniformly scale the resolution, depth, and width of the backbone, feature network, and prediction heads simultaneously. In the 2026 market landscape, EfficientDet remains a critical benchmark for production-grade computer vision, particularly in environments with limited computational resources like edge devices and IoT sensors. Its design addresses the fundamental challenge of balancing model performance (mAP) with computational cost (FLOPs). By optimizing the feature fusion process through BiFPN, it enables more effective multi-scale feature representation. The model family ranges from D0 (lightweight/mobile) to D7 (state-of-the-art accuracy), providing developers with a granular spectrum of deployment options for tasks ranging from autonomous navigation to high-precision industrial inspection.