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Number of parameters (M)
YOLOX: Exceeding YOLO Series in 2021 Zheng Ge Songtao Liu Feng Wang Zeming Li Jian SunMegvii Technology{gezheng, liusongtao, wangfeng02, lizeming, " " " $ " " # # ! " # 3940414243444546474849505158111417202326 293235384144 COCO AP (%)V100 batch 1 Latency (ms) AP (%) Number of parameters (M)YOLOX-NanoNanoDetYOLOv4-TinyYOLOX-Tin yEfficientDet-Lite0 EfficientDet-Lite3 YOLOX-SPPYOLO-TinyEfficientDet-Lite2 EfficientDet-Lite1 Figure 1: Speed-accuracy trade-off of accurate models (top) and Size-accuracy curve of lite models on mobile devices(bottom) for YOLOX and other state-of-the-art object this report, we present some experienced improve-ments to YOLO series, forming a new high-performancedetector YOLOX.}
accuracy trade-off for real-time applications. They extract the most advanced detection technologies available at the time (e.g., anchors [26] for YOLOv2 [24], Residual Net [9] for YOLOv3 [25]) and optimize the implementation for best practice. Currently, YOLOv5 [7] holds the best trade-off performance with 48.2% AP on COCO at 13.7 ms.1
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