## 4 Experiment Results
### EfficientNet Optimal Parameter Search  
EfficientNet's compound scaling optimizes model performance through the balanced scaling of three main dimensions: network depth, width, and image resolution. Rather than simply enlarging the network, this approach aims to maximize both efficiency and accuracy by carefully considering all three dimensions simultaneously. The expansion of each dimension affects model performance differently, contributing to higher accuracy and efficiency overall. Accordingly, the model was trained and validated using the ImageNet dataset, where a grid-search method was used to evaluate various parameter combinations. This method systematically explores possible values for each parameter, thus identifying the optimal combination of network depth, width, and image resolution. It plays a crucial role in effectively exploring the parameter spaces of complex models. In this study, we applied the EfficientNet approach to a contact lens dataset. Because this dataset differs from ImageNet in its characteristics, the grid-search method is essential for determining optimal parameters. Through this process, we experimented with various combinations of key parameters--such as depth, width, and resolution--to identify the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Labeled Images** \\ \hline b\_edge & 483 \\ burr & 411 \\ broken & 431 \\ b\_bubble & 427 \\ etc\_abnormal & 423 \\ no\_lens & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of labeled images for each class. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Total Number of Images** & **Original Image Size** & **Input Image Size** & **Data Split** \\  & & **(Train: Validation: Test)** \\ \hline
2800 & 1500 \(\times\) 1500 (pixel) & 640 \(\times\) 640 (pixel) & 6:2:2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment dataset. most suitable configuration for our dataset. This step is crucial for customizing EfficientNet to fit the dataset's unique features. We specifically conducted experiments to discover an optimized network structure for our contact lens dataset based on the EfficientNetB0 model. By default, EfficientNet uses a width of 1.0, a depth of 1.0, and a resolution of 224 \(\times\) 224 pixels. During our exploration, we focused on adjusting three key parameters: resolution, width, and depth. We set the resolution to 640 pixels, considering the dataset's characteristics, computational efficiency, and practical usage. This resolution choice was made to balance model performance and efficiency. Width is the core focus of this study: by carefully adjusting width, we aimed to maximize the model's ability to detect various abnormal contact lens types as shown in Table 3. Increasing the width expands the variety of features captured by the model, making it critical to identify the most appropriate width value for this dataset. After determining the optimal width, the depth was gradually adjusted. Increasing the depth allowed the model to learn more complex features; however, given the characteristics of this dataset with a smaller number of classes, it was crucial to minimize the depth to avoid overfitting. Therefore, as shown in Table 4, the exploration process involved gradually increasing the depth while aligning it with the optimal width and assessing the accuracy and efficiency of the model at each step. Figure 12 illustrates the exploration of width and depth. Through this exploration, the study identified the optimal structure for the EfficientNet model tailored to the contact lens dataset, discovering optimal parameters: a width of 2.2, a depth of 1.0, and a resolution of 640 \(\times\) 640 pixels.