يعرض 1,901 - 1,920 نتائج من 3,510 نتيجة بحث عن 'significantly ((((((largest decrease) OR (nn decrease))) OR (teer decrease))) OR (mean decrease))', وقت الاستعلام: 0.48s تنقيح النتائج
  1. 1901

    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%. …"
  2. 1902

    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%. …"
  3. 1903

    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%. …"
  4. 1904

    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%. …"
  5. 1905

    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%. …"
  6. 1906

    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%. …"
  7. 1907

    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%. …"
  8. 1908

    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%. …"
  9. 1909
  10. 1910
  11. 1911
  12. 1912
  13. 1913
  14. 1914
  15. 1915
  16. 1916
  17. 1917
  18. 1918
  19. 1919
  20. 1920