## 6 Experiment Evaluation
### Model Training and Conversion  
The dataset is images of typical defective features and some inclusions of perturbations that have been carefully selected by professional engineers. In addition, the paper performed data enhancement operations on all datasets, including random cropping, rotating, changing the chromaticity, etc. We trained the model with RepVGG and AlexNet, using a resolution of 225 \(\times\) 225 and 227 \(\times\) 227 as input, respectively. For AlexNet, this work uses BN layers in plaintext training to prevent overfitting and has deployed the BN layer in FALCON for AlexNet secure inference. For RepVGG, it needs to convert the training model  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Indicators**} & \multicolumn{2}{c}{**Range-I**} & \multicolumn{2}{c}{**Range-II**} & \multicolumn{2}{c}{**Range-III**} \\ \cline{2-7}  & **Ours** & **FALCON** & **Ours** & **FALCON** & **Ours** & **FALCON** \\ \hline Time(s) & 0.12 & 0.22 & 0.16 & 0.3 & 0.37 & 0.26 \\ Comm.(MB) & \(4.32\times 10^{-4}\) & \(5.08\times 10^{-4}\) & \(4.32\times 10^{-4}\) & 5.08 \(\times 10^{-4}\) & 9.32 \(\times 10^{-4}\) & 5.08 \(\times 10^{-4}\) \\ Precision & \(10^{-5}\) & \(10^{-5}\) & \(10^{-4}\) & - & \(10^{-3}\) & - \\ Average & 2.8 \(\times 10^{-4}\) & 2.67 \(\times 10^{-4}\) & 2.67 \(\times 10^{-3}\) & - & 1.26 \(\times 10^{-1}\) & - \\ Relative Error & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of division with the origin work in a different range.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **OPL(s)** & **OSL(s)** & **ARE** & **Precision** \\ \hline Ours & 0.116 & \(1.76\times 10^{-4}\) & \(1.92\times 10^{-4}\) & \(10^{-3}\) \\ Snn & 0.697 & \(2.33\times 10^{-4}\) & \(1.84\times 10^{-4}\) & \(10^{-3}\) \\ Newton & 0.216 & \(2.63\times 10^{-4}\) & \(2.49\times 10^{-4}\) & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of division in different methods.  
with \(3\times 3\), \(1\times 1\) convolutional kernels and shortcut layers into an inference phase model with a plain structure, as shown in Figure 3. The transformed model is then used to perform plaintext inference on the validation set.The dataset was sampled randomly (Dataset-I) for validation and recorded the recall, precision, f1 score, and accuracy of the plaintext model obtained by RepVGG and AlexNet by performing the inference. In practical terms, the core situation that needs for attention is defective diamond substrates. Identifying diamond substrate pieces (positive class) as normal (negative class) would be costly in terms of manual cultivation and risk of compensation, so this work needs the recall score of the model to be stable and high. On the other hand, it is convenient to extract all defective diamond substrates for secondary checking when the model is used for model holders themselves, which would contribute to work efficiency. Like the result shown in Table 3, the deployed inference model obtained performed relatively well, and this work also recorded the indexes for datasets with some perturbation samples (Dataset-II), which demonstrates that RepVGG's model is more stable.