EfficientDet vs RT-DETR

EfficientDet is an object detection model developed by Google Research, released in November 2019. It introduces a compound scaling method that uniformly scales the resolution, depth, and width of the detection network, building on the EfficientNet backbone and a bidirectional feature pyramid network (BiFPN) for multi-scale feature fusion. This design achieves strong accuracy-efficiency tradeoffs across a family of models ranging from EfficientDet-D0 to D7.

EfficientDet-D7 achieves 55.1% AP on COCO while remaining significantly smaller in parameter count than comparable models at the time of release. The model family is well suited for deployment scenarios where compute budget varies, as smaller variants can run on edge hardware while larger variants are competitive with heavier architectures on server-side inference.

RT-DETR (Real-Time Detection Transformer) is an object detection model developed by Baidu, released in April 2023 under the Apache 2.0 license. It is the first transformer-based real-time object detector, addressing the inference speed limitations of earlier DETR models through an efficient hybrid encoder that decouples intra-scale interaction and cross-scale fusion, enabling the model to process multi-scale features without the high computational overhead of standard transformer encoders.

RT-DETR achieves 53.1% AP on COCO at 108 FPS on an NVIDIA T4 GPU for the RT-DETR-L variant, outperforming comparably sized YOLO detectors at similar speeds. It maintains end-to-end inference without non-maximum suppression, simplifying deployment pipelines. RT-DETR established the baseline for real-time transformer detection and has been extended by subsequent works including RF-DETR and RT-DETRv2.