Transcription of Number of parameters (M)
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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.}
over, when switching to the advanced YOLOv5 architec-ture that adopts an advanced CSPNet [31] backbone and an additional PAN [19] head, YOLOX-L achieves 50.0% AP on COCO with 640 and IoU Loss for training640 resolution, outperforming the counterpart YOLOv5-L by 1.8% AP. We also test our de-sign strategies on models of small size. YOLOX-Tiny and
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