DETR vs RT-DETR

DETR (Detection Transformer) is an end-to-end object detection model developed by Facebook Research (Meta), released in May 2020. It is one of the first models to eliminate hand-crafted components such as anchor generation and non-maximum suppression by framing object detection as a direct set prediction problem, solved with a transformer encoder-decoder architecture built on top of a CNN backbone.

DETR achieves 42.0% AP on the COCO benchmark with a ResNet-50 backbone, performing comparably to a well-tuned Faster R-CNN at the time of release. Its attention-based design allows it to reason about global context and long-range dependencies within an image. DETR is primarily used as a research baseline and architectural reference, with subsequent works such as Deformable DETR and DINO building on its foundations to address its slower training convergence and limited small-object detection capability.

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.