## 3 Experiment and Results
### Transfer Learning Experiment
\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}  & \(ACC\)**0.5**(\%) & \(ACC\)**1**(\%) & \(MAE\) & \(ACC\)**0.5**(\%) & \(ACC\)**1**(\%) & \(MAE\) \\ \hline ResNet-18 [8] & 62.5 & 87.4 & 0.183 & 54.9 & 77.6 & 0.425 \\ ResNet-18-ImageNet1k [8] & 64.5 & 87.7 & 0.218 & 54.9 & 78.5 & 0.361 \\ ResNet-18-pre [8] & 63.0 & 88.6 & 0.205 & 55.3 & 78.9 & 0.321 \\ ResNet-50 [8] & 60.8 & 87.4 & 0.244 & 52.3 & 75.9 & 0.353 \\ ResNet-50-ImageNet1k [8] & 61.7 & 87.4 & 0.231 & 53.2 & 77.6 & 0.294 \\ ResNet-50-pre [8] & 61.7 & 88.2 & 0.194 & 55.7 & 79.7 & 0.301 \\ Vit [31] & 63.0 & 88.1 & 0.522 & 46.8 & 72.6 & 0.858 \\ Vit-ImageNet1k [31] & 63.2 & 88.1 & 0.516 & 52.7 & 78.5 & 0.758 \\ Vit-pre [31] & 64.4 & 88.6 & 0.503 & 55.3 & 79.7 & 0.752 \\ Swin Transformer [49] & 63.1 & 88.2 & 0.517 & 52.3 & 78.9 & 0.820 \\ Swin Transformer-ImageNet1k [49] & 63.6 & 88.4 & 0.516 & 54.4 & 81.0 & 0.716 \\ Swin Transformer-pre [49] & 64.5 & 88.5 & 0.504 & 58.2 & 81.4 & 0.702 \\ ResNet-50-6 & 68.5 & 91.6 & 0.145 & 57.0 & 81.9 & 0.300 \\ ResNet-50-6-pre & 68.9 & 91.8 & 0.132 & 61.6 & 82.3 & 0.265 \\ CFGN & 70.1 & 92.0 & 0.123 & 62.0 & 83.5 & 0.249 \\ CFGN-pre & 71.6 \(\dagger\) & 92.1 \(\dagger\) & 0.108 \(\dagger\) & 63.7 \(\dagger\) & 84.8 \(\dagger\) & 0.247 \(\dagger\) \\ \hline \hline \end{tabular}  
* represents the best experimental result. ‘-ImageNet1k’ represents the use of the ImageNet1k pretrained model. ‘-pre’ represents the use of the model-eye pretrained model.  
\end{table} 
Table 6: Results of transfer learning 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}  & \(ACC\)**0.5**(\%) & \(ACC\)**1**(\%) & \(MAE\) & \(ACC\)**0.5**(\%) & \(ACC\)**1**(\%) & \(MAE\) \\ \hline ResNet-18 [8] & 61.6 & 88.6 & 0.274 & 55.9 & 76.8 & 0.390 \\ ResNet-18-ImageNet1k [8] & 62.5 & 89.3 & 0.242 & 57.1 & 77.4 & 0.333 \\ ResNet-18-pre [8] & 63.3 & 89.4 & 0.236 & 59.9 & 77.4 & 0.259 \\ ResNet-50 [8] & 63.1 & 88.8 & 0.255 & 54.8 & 75.7 & 0.380 \\ ResNet-50-ImageNet1k [8] & 64.2 & 89.2 & 0.243 & 56.5 & 77.4 & 0.354 \\ ResNet-50-pre [8] & 63.4 & 89.6 & 0.249 & 58.2 & 78.0 & 0.334 \\ Vit [31] & 64.9 & 88.8 & 0.504 & \(\times\) & \(\times\) & \(\times\) \\ Vit-ImageNet1k [31] & 64.1 & 89.7 & 0.495 & 53.1 & 77.4 & 0.819 \\ Vit-pre [31] & 65.0 & 89.2 & 0.497 & 57.1 & 77.4 & 0.807 \\ Swin Transformer [49] & 64.6 & 89.0 & 0.505 & 50.3 & 74.0 & 0.958 \\ Swin Transformer-ImageNet1k [49] & 64.8 & 89.4 & 0.489 & 56.5 & 75.7 & 0.812 \\ Swin Transformer-pre [49] & 65.6 & 89.6 & 0.493 & 57.1 & 77.4 & 0.758 \\ ResNet-50-6 & 66.1 & 91.1 & 0.236 & 57.1 & 79.1 & 0.321 \\ ResNet-50-6-pre & 67.9 & 91.6 & 0.244 & 57.6 & 79.7 & 0.293 \\ CFGN & 69.6 & 92.5 & 0.230 & 61.6 & 81.9 & 0.285 \\ CFGN-pre & 69.5 & 92.7 \(\dagger\) & 0.168 \(\dagger\) & 62.7 \(\dagger\) & 84.2 \(\dagger\) & 0.270 \(\dagger\) \\ \hline \hline \end{tabular}  
* represents the best experimental result. ‘-ImageNet1k’ represents the use of the ImageNet1k pretrained model. ‘-pre’ represents the use of the model-eye pretrained model.  
\end{table} 
Table 5: Results of transfer learning experiments on the Jinzhou dataset. sets contain only a small amount of high-diopter hyperopia data (+2 D to +6 D), and there are no high-diopter hyperopia data in the training sets. This indicates that introducing the model-eye pretrained model alleviated the problem of insufficient high-diopter hyperopia data. Furthermore, when fine-tuning the pretrained model on Dataset2, ResNet-50-6 achieved an increase of 0.5% to 0.6% in _ACC_0.5 and _ACC_1. For the CFGN model, _ACC_0.5 increased by 1.1%, while _ACC_1 improved by 2.3%. These results demonstrate that the CFGN model is more effective than ResNet-50 in extracting common features between model eyes and real human eyes.