Showing 2,481 - 2,500 results of 4,761 for search 'significantly ((((nn decrease) OR (larger decrease))) OR (((mean decrease) OR (greater decrease))))', query time: 0.53s Refine Results
  1. 2481

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

    Published 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. 2482

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

    Published 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. 2483

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

    Published 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. 2484

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

    Published 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. 2485

    YOLOv8n structure diagram. by Yaojun Zhang (389482)

    Published 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. 2486

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

    Published 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. 2487

    Enhanced dataset sample images. by Yaojun Zhang (389482)

    Published 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. 2488
  9. 2489

    Accuracy on the ERAM task. by Daisung Jang (16781451)

    Published 2024
    “…However, coefficients for estrogen were significant for both emotion recognition tasks. Higher within-person levels of estrogen predicted lower accuracy, whereas higher between-person estrogen levels predicted greater accuracy. …”
  10. 2490

    Experimental design of this study. by Renya Kawakami (20469088)

    Published 2024
    “…For example, several empirical and theoretical studies have demonstrated that older males make greater investment in reproduction compared with younger males. …”
  11. 2491

    All relevant data of this study. by Renya Kawakami (20469088)

    Published 2024
    “…For example, several empirical and theoretical studies have demonstrated that older males make greater investment in reproduction compared with younger males. …”
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