当硬件平台为Jetson Xavier NX时,YOLO-ReT-M1.4@320x320具有比YOLO-Fastest-XL高0.92mAP@VOC、3.34mAP@COCO的性能,同时推理速度快4.02FPS; 当平键平台为AGX Xavier时,YOLO-ReT-EB3@416x416取得了最佳性能,同时仍具有实时推理速度; 需要注意的是:在具有相似FPS时,基于MobileNetV2的检测器性能要优于骨干为Efficien...
当硬件平台为Jetson Xavier NX时,YOLO-ReT-M1.4@320x320具有比YOLO-Fastest-XL高0.92mAP@VOC、3.34mAP@COCO的性能,同时推理速度快4.02FPS; 当平键平台为AGX Xavier时,YOLO-ReT-EB3@416x416取得了最佳性能,同时仍具有实时推理速度; 需要注意的是:在具有相似FPS时,基于MobileNetV2的检测器性能要优于骨干为Efficien...
MobileNetv2 [23] introduces linear bottlenecks and inverse residuals based on the previous generation, enhancing the network’s representation ability. MobileNetv3 [24] incorporates the SE module, updates the activation function, and achieves better performance. MBConv [25] is an inverse residual ...
The Fused-MBConv was employed for shallow convolution, and MBConv was used for deep convolution [18]. This resulted in a faster training speed for the EfficientNetV2 model under the same parameters and hardware conditions [16]. Regarding computational efficiency, EfficientNetV2 was more advantageous ...