# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 4 Experimental Investigation
### The Results of Experiments
The highest accuracy (0.9952) has been obtained when the image resolution is equal to 600, the batch size is equal to 16, and the iteration number is equal to 600. In Table 3, the influence of the training parameters is presented using the entire dataset. All models have been tested using 39 new images, where a total of 309 construction details have been taken. As we can see (Figure 8), the accuracy of model 320_16_300 is equal to 0.9894, but the recognition ratio (0.5242) is the highest of all models. In Figure 9, the box plot of each model's result is presented. The box plot shows the results of the recognition ratio distribution for each class. As we can see, the highest recognition ratio was obtained by Model 320_16_300, where most of the recognition ratio is higher than the median. A similar distribution is in model 320_16_600, but in this case, five construction details have not been recognized (0% accuracy). The recognition ratio of the other models is lower. The majority of the recognition ratio value in model 600_16_300 is lower than the median, and many construction details have not been recognized at all. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Resolution** & **Batch Size** & **Number of Iterations** & **Correctly Detected** & **Recognition Ratio** \\ \hline
320 & 16 & 300 & 162 & 0.5242 \\ \hline
320 & 32 & 300 & 137 & 0.4433 \\ \hline
320 & 16 & 600 & 142 & 0.4595 \\ \hline
640 & 16 & 300 & 155 & 0.5016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Testing results of model trained on 355 color images of 36 classes. Figure 8: Accuracy results of the model trained on 355 color images of 36 classes. The detail recognition ratios of each class are presented in Table 4. The results of the four best models were compared. As we can see, only two details were recognized correctly by all models and two construction details have not been recognized by any of the trained models. The models 320_16_300 and 320_16_600 recognize, respectively, four and five construction details with 100% accuracy. Model 640_16_300 has shown the worst performance, not recognizing a total of eight out of thirty-six construction details. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Label of The**} & \multicolumn{4}{c}{**Recognition Ratio**} \\ \cline{2-5}  & \multicolumn{4}{c}{**of Each Class**} \\ \cline{2-5}  & **320\_16\_300** & **320\_32\_300** & **320\_16\_600** & **640\_16\_300** \\ \hline \(6\times 6\) & 1 & 0.5 & 1 & 1 \\ \hline \(2\times 6\) & 0.75 & 0.88 & 0.75 & 1 \\ \hline \(2\times 3\) & 0.33 & 0.33 & 0.5 & 0.5 \\ \hline \(1\times 4\) & 0.69 & 0.62 & 0.69 & 0.54 \\ \hline \(2\times 2\) & 0.8 & 0.2 & 0.4 & 0.6 \\ \hline \(8\times 16\) & 1 & 1 & 1 & 1 \\ \hline \(4\times 6\) & 1 & 1 & 1 & 0 \\ \hline \(1\times 2\) & 0.39 & 0.28 & 0.39 & 0.33 \\ \hline \(1\times 2\)\_3D & 0.58 & 0.58 & 0.5 & 0.42 \\ \hline \(2\times 3\)\_3D & 0.7 & 0.6 & 0.7 & 0.6 \\ \hline \(2\times 6\)\_3D & 0.3 & 0.5 & 0.6 & 0.6 \\ \hline \(2\times 2\)\_3D & 0.57 & 0.57 & 0.43 & 0.29 \\ \hline \(1\times 4\)\_3D & 0.46 & 0.54 & 0.38 & 0.54 \\ \hline \(1\times 4\)\_trapeze & 0.82 & 0.73 & 0.82 & 0.73 \\ \hline grid & 0.92 & 0.68 & 0.56 & 0.92 \\ \hline \(1\times 1\)\_square & 0.5 & 0.5 & 0.33 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Testing results for each class for all trained models. Figure 9: The box plot of the results of the test dataset for each model.