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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.}
and batch=1 on a single Tesla V100. YOLOv3 baseline Our baseline adopts the architec-to YOLOv3-SPP in some papers [1,7]. We slightly change some training strategies compared to the orig-inal implementation [25], adding EMA weights updat-ing, cosine lr schedule, IoU loss and IoU-aware branch. We use BCE Loss for training cls and obj branch, reg ...
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