## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ID** & \begin{tabular}{c} **Left Back** \\ **Shooting (Pixel)** \\ \end{tabular} & \begin{tabular}{c} **Right Side** \\ **Shooting (Pixel)** \\ \end{tabular} & \begin{tabular}{c} **Side Pixel** \\ **Difference (Pixel)** \\ \end{tabular} & **Real Weight (kg)** & \
\begin{tabular}{c} **Back** \\ **Difference (Pixel)** \\ \end{tabular} & **Real Weight (kg)** &
\begin{tabular}{c} **Estimated** \\ **Weight (kg)** \\ \end{tabular} & **Difference (kg)** \\ \hline
9 & 60,146 & 59,403 & 34 & 1.88 & 9819 & 10,676 & 24 & 2.67 & 291 & 281.71 & 9.29 \\
21 & 14,031 & 14,031 & 7 & 9.16 & 8818 & 9227 & 11 & 5.83 & 376 & 389.12 & \(-\)13.12 \\
36 & 72,247 & 71,788 & 30 & 2.13 & 45,864 & 45,521 & 59 & 1.08 & 423 & 566.15 & \(-\)143.15 \\
44 & 44,624 & 44,198 & 20 & 3.20 & 10,000 & 10,126 & 11 & 5.83 & 418 & 389.92 & 28.08 \\
47 & 43,968 & 44,156 & 20 & 3.20 & 28,421 & 29,072 & 25 & 2.56 & 447 & 438.57 & 8.43 \\
64 & 20,079 & 19,978 & 8 & 8.01 & 5726 & 5492 & 6 & 10.68 & 458 & 456.09 & 1.91 \\
67 & 18,592 & 18,507 & 8 & 8.01 & 6002 & 6056 & 8 & 8.01 & 409 & 430.47 & \(-\)21.47 \\
70 & 13,850 & 13,713 & 22 & 2.91 & 6753 & 6239 & 18 & 3.56 & 131 & 130.8 & 0.2 \\
81 & 40,195 & 40,141 & 22 & 2.91 & 3831 & 3852 & 10 & 6.41 & 294 & 272.02 & 21.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction values on the test dataset of ANN-3 network. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ID** & \begin{tabular}{c} **Left Side** \\ **Shooting (Pixel)** \\ \end{tabular}  & \begin{tabular}{c} **Right Side** \\ **Shooting (Pixel)** \\ \end{tabular}  & \ \begin{tabular}{c} **Side Pixel** \\ **Difference (Pixel)** \\ \end{tabular}  & **Real Weight (kg)** & \begin{tabular}{c} **Estimated** \\ **Weight (kg)** \\ \end{tabular}  & **Difference (kg)** \\ \hline
9 & 60,146 & 59,403 & 34 & 1.88 & 9819 & 10,676 & 24 & 2.67 & 291 & 281.71 & 9.29 \\
21 & 14,031 & 14,031 & 7 & 9.16 & 8818 & 9227 & 11 & 5.83 & 376 & 389.12 & \(-\)13.12 \\
36 & 72,247 & 71,788 & 30 & 2.13 & 45,864 & 45,521 & 59 & 1.08 & 423 & 566.15 & \(-\)143.15 \\
44 & 44,624 & 44,198 & 20 & 3.20 & 10,000 & 10,126 & 11 & 5.83 & 418 & 389.92 & 28.08 \\
47 & 43,968 & 44,156 & 20 & 3.20 & 28,421 & 29,072 & 25 & 2.56 & 447 & 438.57 & 8.43 \\
64 & 20,079 & 19,97 In this study, the K-fold cross-validation technique was used to test the validity of the proposed method and the accuracy of the results obtained. K-fold cross-validation is one of the methods of splitting the dataset for evaluation of classification models and training of the model [34; 35]. This method is used to generate random layers. Each layer represents a combination of training data subset and test data subset sections for training and validating machine learning models. For each layer, a certain accuracy value is obtained for the model. For example, in the case of 10-fold cross-validation, the overall accuracy is estimated by averaging the accuracy values produced by all 10 folds. For any dataset with a given number of samples, there are many possible combinations of training and test datasets that can be generated. Some of these datasets are used to train the model and some are used to test the success of the model. Therefore, it allows each divided part to be used separately for both training and testing. The representation of the K-fold cross-validation method for K = 10 is given in Figure 12.  
Training and testing the model up to K can take a long time and can be costly in terms of computation and time for large datasets. On the other hand, it provides a reliable result. In this study, the K value was accepted as 10, and validity tests were carried out. Here, the test and training images at each step are meaningfully segmented. A similar situation was repeated at each K step and validity tests were performed on different images. In this study, the validity of the ANN-3 architecture, which was trained using both side and rear images, was tested with K-fold.