## 3 Experiment and Results
### Transfer Learning Experiment  
In this section, we employed transfer learning by loading the weights of pretrained models and fine-tuning them with real-human-eye data. For common deep learning models such as ResNet-18, ResNet-50, Vit, and Swin Transformer, we used models pretrained on ImageNet1k, removed the classifier part, and fine-tuned them layer by layer. As shown in the table, the training results with transfer learning outperformed those obtained by training directly on eccentric infrared pupil images. Additionally, Vit successfully converged on Dataset2, validating the effectiveness of transfer learning. However, for models with six-channel image inputs, such as CFGN and ResNet-50-6, pretrained models are not available. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dataset3**} & \multicolumn{3}{c}{**Dataset4**} \\ \cline{2-7}  & _ACC0.5_(\%) & _ACC1_(\%) & _MAE_ & _ACC0.5_(\%) & _ACC1_(\%) & _MAE_ \\ \hline DenseNet-121 [45] & 58.6 & 86.4 & 0.200 & 49.8 & 74.7 & 0.395 \\ VGG-16 [44] & 62.0 & 88.2 & 0.201 & 49.4 & 78.5 & 0.346 \\ AlexNet [46] & 63.5 & 88.1 & 0.146 & 53.2 & 77.6 & 0.290 \\ ResNet-18 [8] & 62.5 & 87.4 & 0.183 & 54.9 & 77.6 & 0.425 \\ ResNet-50 [8] & 60.8 & 87.4 & 0.244 & 52.3 & 75.9 & 0.353 \\ ResNeXt-50 [47] & 60.5 & 87.2 & 0.226 & 50.6 & 75.5 & 0.335 \\ Se-ResNet-50 [48] & 62.1 & 87.1 & 0.213 & 52.7 & 78.1 & 0.342 \\ Vit [31] & 63.0 & 88.1 & 0.522 & 46.8 & 72.6 & 0.858 \\ Swin Transformer [49] & 63.1 & 88.2 & 0.517 & 52.3 & 78.9 & 0.820 \\ REDNet [16] & 64.1 & 88.6 & 0.198 & 54.9 & 77.6 & 0.310 \\ ResNet-50-6 & 68.5 & 91.6 & 0.145 & 57.0 & 81.9 & 0.300 \\ CFGN & 70.1 + & 92.0 + & 0.123 + & 62.0 + & 83.5 + & 0.249 + \\ \hline \hline \end{tabular} \(\dagger\) represents the best experimental result.  
\end{table} 
Table 4: Results of comparative experiments on the Tacheng dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dataset1**} & \multicolumn{3}{c}{**Dataset2**} \\ \cline{2-7}  & _ACC0.5_(\%) & _ACC1_(\%) & _MAE_ & _ACC0.5_(\%) & _ACC1_(\%) & _MAE_ \\ \hline DenseNet-121 [45] & 61.7 & 87.8 & 0.274 & 52.0 & 76.3 & 0.385 \\ VGG-16 [44] & 63.9 & 89.2 & 0.265 & 50.8 & 74.0 & 0.343 \\ AlexNet [46] & 64.6 & 89.7 & 0.231 & 55.4 & 76.8 & 0.274 \\ ResNet-18 [8] & 61.6 & 88.6 & 0.274 & 55.9 & 76.8 & 0.390 \\ ResNet-50 [8] & 63.1 & 88.8 & 0.255 & 54.8 & 75.7 & 0.380 \\ ResNeXt-50 [47] & 61.2 & 88.2 & 0.265 & 52.0 & 74.6 & 0.366 \\ Se-ResNet-50 [48] & 65.1 & 88.1 & 0.249 & 53.1 & 76.8 & 0.364 \\ Vit [31] & 64.9 & 88.8 & 0.504 & \(\times\) & \(\times\) & \(\times\) \\ Swin Transformer [49] & 64.6 & 89.0 & 0.505 & 50.3 & 74.0 & 0.958 \\ REDNet [16] & 63.1 & 88.9 & 0.250 & 52.0 & 75.7 & 0.399 \\ ResNet-50-6 & 66.1 & 91.1 & 0.236 & 57.1 & 79.1 & 0.321 \\ CFGN & 69.6 + & 92.5 + & 0.230 + & 61.6 + & 81.9 + & 0.285 + \\ \hline \hline \end{tabular} \(\dagger\) represents the best experimental result.  
\end{table} 
Table 3: Results of comparative experiments on the Jinzhou dataset. Therefore, we trained refractive error screening pretrained models on CFGN and ResNet-50-6 using a large amount of objective model-eye data for six-channel eccentric infrared pupil images and fine-tuned them on the real-human-eye datasets, Dataset1 and Dataset2. The CFGN-pre and ResNet-50-6-pre in Tables 5 and 6 represent the training results after applying transfer learning. Experimental results show that on Dataset1 and Dataset3, CFGN-pre achieved the highest \(ACC1\) accuracy and the lowest \(MAE\). On Dataset2, compared to the original CFGN, CFGN-pre significantly improved \(ACC1\), reaching 84.2%. In Dataset2 and Dataset4, the test