架构与学习框架的协同设计:通过重新设计 ConvNeXt 架构和自监督学习框架,ConvNeXt V2 在多种视觉任务上表现出色,证明了架构与学习框架协同设计的重要性。 掩码自编码器的有效性:FCMAE 框架使得 ConvNeXt V2 能够从掩码自编码器预训练中受益,显著提升了性能。 GRN层的作用:GRN 层通过增强特征多样性,解决了 ConvNeXt...
nohup python get_FPS.py --weights runs/prune/yolov8n-asf-p2-lamp-exp1-prune/weights/model_c2f_v2.pt --batch 32 --device 0 --warmup 200 --testtime 400 > logs/yolov8n-asf-p2-fps.log 2>&1 & tail -f logs/yolov8n-asf-p2-fps.log --- lamp exp1 --- param_dict = { # o...
_RSCD, v10Detect_LSCD, v10Detect_TADDH, v10Detect_RSCD, v10Detect_LSDECD): args[1] = make_divisible(min(args[1], max_channels) * width, 8) if m in (Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD)...
(Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD): args[2] = make_divisible(min(args[2], max_channels) * width, 8) elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1 args....
(Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD): args[2] = make_divisible(min(args[2], max_channels) * width, 8) elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1 args....
By screening 5000 road traffic waterlogging images on the public dataset RSCD for training, the experimental results show that the average accuracy of the method is 84.4%, which is 3.7% higher than the original YOLOv5 algorithm, and it can more accurately extract and identify the waterlogged ...
_RSCD, v10Detect_LSCD, v10Detect_TADDH, v10Detect_RSCD, v10Detect_LSDECD): args[1] = make_divisible(min(args[1], max_channels) * width, 8) if m in (Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD)...
_RSCD, v10Detect_LSCD, v10Detect_TADDH, v10Detect_RSCD, v10Detect_LSDECD): args[1] = make_divisible(min(args[1], max_channels) * width, 8) if m in (Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD)...
By employing a weight-sharing mechanism, RSCD’s required convolutional layers are decreased, decreasing the parameter count of the detection head. Additionally, the reuse of parameters addresses the limitation of low-parameter utilization in the head network. Figure 2. The structure of LH-YOLO. ...
By employing a weight-sharing mechanism, RSCD’s required convolutional layers are decreased, decreasing the parameter count of the detection head. Additionally, the reuse of parameters addresses the limitation of low-parameter utilization in the head network. Figure 2. The structure of LH-YOLO. ...