Ablation experiments of various block. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

Kappa coefficients for different algorithms. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

The structure of ASPP+ block. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

The structure of attention gate block [31]. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

Relating the biophysical forces that govern bacterial adhesion to experimental data. حسب Mikkel Anbo (15287803)

DSC block and its application network structure. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

The structure of multi-scale residual block [30]. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

The structure of IRAU and Res2Net+ block [22]. حسب Yingying Liu (360782)

"…The actual accuracy and mean latency time of the model were 92.43% and 260ms, respectively. …"

Loss of C1-FDX results in a reduction of the CI and CV activity. حسب Baoyin Chen (2635399)

Dependent and independent variables (N = 316). حسب Priyanka Saluja (20501044)

"…<div><p>Introduction</p><p>Unmet oral health needs remain a significant issue among immigrant adolescents, often exacerbated by experiences of racial discrimination. …"

Blood chemistry test results. حسب Maria A. Colombatti Olivieri (21362615)

Study on the expression of mRNA related to biofilm formation of <i>C. albicans</i> by CSR extract (100 μg/mL). حسب Xuxi Cheng (22453447)

Urinalysis (A-F) and blood chemistry test results related to kidney function (G and H). حسب Maria A. Colombatti Olivieri (21362615)

Dataset visualization diagram. حسب Yaojun Zhang (389482)

"…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%. …"

Dataset sample images. حسب Yaojun Zhang (389482)

"…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%. …"

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

"…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%. …"

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

"…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%. …"

YOLOv8n detection results diagram. حسب Yaojun Zhang (389482)

"…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%. …"

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

"…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%. …"

BiFormer structure diagram. حسب Yaojun Zhang (389482)

"…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%. …"