يعرض 3,501 - 3,520 نتائج من 7,147 نتيجة بحث عن 'significantly ((((lower decrease) OR (((mean decrease) OR (nn decrease))))) OR (greater decrease))', وقت الاستعلام: 0.38s تنقيح النتائج
  1. 3501

    Dataset visualization diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  2. 3502

    Dataset sample images. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  3. 3503

    Performance comparison of different models. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  4. 3504

    C2f and BC2f module structure diagrams. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  5. 3505

    YOLOv8n detection results diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  6. 3506

    YOLOv8n-BWG model structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  7. 3507

    BiFormer structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  8. 3508

    YOLOv8n-BWG detection results diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  9. 3509

    GSConv module structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  10. 3510

    Performance comparison of three loss functions. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  11. 3511

    mAP0.5 Curves of various models. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  12. 3512

    Network loss function change diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  13. 3513

    Comparative diagrams of different indicators. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  14. 3514

    YOLOv8n structure diagram. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  15. 3515

    Geometric model of the binocular system. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  16. 3516

    Enhanced dataset sample images. حسب Yaojun Zhang (389482)

    منشور في 2025
    "…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …"
  17. 3517
  18. 3518

    Baseline analysis, nominal factors. حسب Hiroaki Yoshikawa (4615801)

    منشور في 2025
    "…Conversely, the occurrence of myasthenic crisis and current MG-ADL scores were lower in 2018. Regarding treatment, the utilization of tacrolimus, plasma exchange (PE), and intravenous immunoglobulin (IVIg) significantly increased between 2006 and 2018. …"
  19. 3519

    Minimal dataset for the study. حسب Hiroaki Yoshikawa (4615801)

    منشور في 2025
    "…Conversely, the occurrence of myasthenic crisis and current MG-ADL scores were lower in 2018. Regarding treatment, the utilization of tacrolimus, plasma exchange (PE), and intravenous immunoglobulin (IVIg) significantly increased between 2006 and 2018. …"
  20. 3520

    Baseline analysis, continuous factors. حسب Hiroaki Yoshikawa (4615801)

    منشور في 2025
    "…Conversely, the occurrence of myasthenic crisis and current MG-ADL scores were lower in 2018. Regarding treatment, the utilization of tacrolimus, plasma exchange (PE), and intravenous immunoglobulin (IVIg) significantly increased between 2006 and 2018. …"