# ECGYOLO: Mask Detection Algorithm
## 5 Results  
### Network Model Comparison  
The ECGYOLO model is improved by using GhostConv convolution, which simplifies the traditional convolution of complex operations. This optimization allows ECGYOLO to achieve a faster frame-per-second (FPS) time of 65.3 while maintaining high detection accuracy, which is only 6.2 less than that of the YOLOv7 tiny model, 29 more than YOLOv7 and comparable to SSD. In addition, ECGYOLO is also lightweight. The model requires only 5.06M parameters, which is 94.64M smaller than the SSD model, 103.54M smaller than the highest Faster R-CNN, 31.8M smaller than YOLOv7 and even 1.04M smaller than YOLOv7 tiny. This lightweight design makes ECGYOLO acceptable for most hardware devices and has good application prospects. The improved ECGYOLO model not only reduces the amount of code to achieve light weight but also improves the accuracy of the model. To specify our evaluation method, the authors set 640X640 to the resolution of all model input images, the optimizer used Adam, label smoothing, cosine annealing algorithm and non-extreme suppression. The maximum and minimum learning rates were set to 0.001 and 0.00001, and the momentum was set to 0.9. The detailed evaluation results of each model in precision, recall, F1, FLOPs and mAP parameters are listed in Table 2.  
Table 2 data show that ECGYOLO is 4.4% more accurate than YOLOv7 tiny, overtops YOLOv7 by 3.0%, exceeds SSD by 8.3%, and outperforms FasterRCNN by 6.7%. The increase in precision is 6.6% above the second highest of YOLOv4, recall is 2.6% above the second highest of FasterRCNN, and F1 is also 6.6% higher than the second highest of YOLOv7 tiny. These figures can reduce the number of wrong and missed checks to some extent. FPS is the second highest of that of the above models, only 6.2 below YOLOv7 tiny. Parameter is 1.04M below the second lowest, and FLOPs are 2.5 lower than the second lowest, of YOLOv7 tiny. The above figures are satisfactory for use and operation on most low-computing platforms. The models based on this dataset are shown in Figures 9 and 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AP/\%**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**FLOPs(G)**} & \multirow{2}{*}{**FPS**} & \multicolumn{2}{c}{**Parameter**} \\ \cline{2-2} \cline{5-8}  & **Face** & & & & & & & /MB \\ \hline YOLOv7 tiny & 82.5 & 94.2 & 88.35 & 0.89 & 0.817 & 0.851 & 13.8 & 71.5 & 6.2 \\ YOLOv4 & 83.03 & 93.47 & 88.25 & 0.896 & 0.765 & 0.825 & 61.2 & 12.65 & 64.5 \\ YOLOv7 & 83.7 & 93.8 & 88.75 & 0.86 & 0.823 & 0.841 & 104.3 & 36.3 & 36.9 \\ SSD-Vgg & 76.6 & 92.3 & 84.45 & 0.801 & 0.825 & 0.812 & 272.1 & 64.4 & 99.7 \\ FasterRCNN & 80.6 & 91.5 & 86.05 & 0.784 & 0.85 & 0.815 & 371.7 & 8.5 & 136.69 \\ (Mine) & 89.1 & 96.4 & 92.75 & 0.962 & 0.876 & 0.917 & 11.3 & 65.3 & 5.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network Model Results.  
Figure 9: Original images.  
The results of the pictures from this dataset are shown in Figures 11 and 12.  
Due to the application of learning rate cosine annealing decay, the curve is fluctuating, and after 25 epochs, the curve as a whole has no decreasing trend, at which time, the Loss can be considered to have converged. The loss results and the map curve are shown in Figures 13 and 14.  
Figure 11: Renderings.  
Figure 10: Original images.  
Figure 13: Convergence of ECGYOLO model.