DETRs Beat YOLOs on Real-Time Object Detection
DETRs outperform YOLOs with RT-DETR model, balancing speed and accuracy by adjusting decoder layers. Achieving 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, RT-DETR-R50 surpasses DINO-R50 by 2.2% AP and 21 times in FPS.
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DETRs have surpassed YOLOs in real-time object detection by introducing the Real-Time DEtection TRansformer (RT-DETR) model. RT-DETR focuses on maintaining accuracy while improving speed and vice versa, offering a flexible approach by adjusting the number of decoder layers. It outperforms YOLOs in both speed and accuracy metrics, achieving 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU. Additionally, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. The model leverages an efficient hybrid encoder and uncertainty-minimal query selection to enhance performance. The NMS analysis reveals that adjusting confidence thresholds can impact the execution time of NMS, affecting the overall detection process. The hybrid encoder in RT-DETR processes multi-scale features efficiently, while the uncertainty-minimal query selection improves the quality of initial queries for the decoder. These advancements contribute to RT-DETR's success in real-time object detection, showcasing superior performance compared to existing frameworks.
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4 commentsAlso good that the YOLO moniker is being challenged. After pjreddie went off to do better things, I've always felt a bit sad about random parties co-opting the YOLO name. And then with Ultralyics and their weird approach to monetizing YOLOv8 "yeah uh so it's open source but if you actually train a model plz fork over your money)... not a nice look.
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