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

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

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

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

The structure of ASPP+ block. by 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]. by 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. by Mikkel Anbo (15287803)

DSC block and its application network structure. by 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]. by 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]. by 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. by Baoyin Chen (2635399)

Dependent and independent variables (N = 316). by 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. by 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). by Xuxi Cheng (22453447)

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

Dataset visualization diagram. by 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. by 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. by 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. by 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. by 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. by 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. by 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%. …”