## 5 Results  
In the comparative experiments, four other vision models--ViT, RepViT, U-Net, and U-Net++--were primarily used for image classification comparisons. Additionally, YOLOv8 [30] was included as a benchmark to further evaluate the performance of Football Net. Since Football Net was built from scratch, to ensure a fair comparison, the other models were also constructed from scratch, without using pretrained weights. As these networks are primarily designed for image segmentation, global pooling and fully connected layers were introduced in the final decoder stage to enable them to perform image classification. The table presents the averaged results from 10 trials for each model. As shown in Table 1, experiments conducted on the CIFAR-10 dataset demonstrate that Football Net achieves a Top-5 accuracy of 98.7% and a Top-1 Accuracy of 82.1%. In comparison to other models, Football Net exhibits competitive performance. Experiments were conducted on the ImageNet-100 and ImageNet-1k datasets, with results shown in Table 2. Football Net achieved a Top-5 accuracy of 98.8% and a Top-1 Accuracy of 92.2%. For datasets with fewer categories, accuracy tends to be more sensitive to dataset size. In the case of large datasets like ImageNet-1k, training is often carried out using pretrained models followed by fine-tuning. As a result, when trained from scratch, the accuracy on ImageNet-1k is lower compared to the ImageNet-100 and CIFAR-10 datasets. During the 10 experiments, the training records of different models on various datasets were saved, resulting in three sets of comparison plots, as shown in Figures 6-8. In the experiments conducted on CIFAR-10, Football Net exhibited a faster convergence rate compared to the other models, while its accuracy remained comparable. This observation clearly shows that the model's accuracy converges around the 15 th epoch. On the ImageNet-100 dataset, the accuracy curve of Football Net converged progressively over each epoch, successively surpassing U-Net, RepViT, YOLOv8, and U-Net++. \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Model** & **Accuracy (\%)** & **Top-5 Accuracy (\%)** & **Precision (\%)** \\ \hline \multirow{4}{*}{CIFAR-10} & **Football Net** & 82.1 \(\pm\) **0.07** & 98.7 \(\pm\) **0.028** & 82.0 \(\pm\) **0.10** \\  & ViT & 83.8 \(\pm\) 0.10 & 99.6 \(\pm\) 0.033 & 82.5 \(\pm\) 0.19 \\  & RepViT & 80.4 \(\pm\) 0.22 & 99.0 \(\pm\) 0.037 & 79.8 \(\pm\) 0.25 \\  & U-Net & 76.4 \(\pm\) 0.35 & 97.7 \(\pm\) 0.032 & 75.5 \(\pm\) 0.22 \\  & U-Net++ & 81.4 \(\pm\) 0.30 & 98.1 \(\pm\) 0.030 & 80.4 \(\pm\) 0.20 \\  & YOLOv8 & 76.3 \(\pm\) 0.32 & 98.6 \(\pm\) 0.041 & 74.3 \(\pm\) 0.22 \\ \hline \end{tabular}
\end{table} 
Table 1: Contrast Experiments Results on CIFAR-10. \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Model** & **Accuracy (\%)** & **Top-5 Accuracy (\%)** & **Precision (\%)** \\ \hline \multirow{4}{*}{ImageNet-100} & **Football Net** & 92.2 \(\pm\) **0.12** & 98.8 \(\pm\) **0.06** & 88.5 \(\pm\) **0.20** \\  & ViT & 94.6 \(\pm\) 0.18 & 96.5 \(\pm\) 0.09 & 87.6 \(\pm\) 0.22 \\  & RepViT & 89.5 \(\pm\) 0.25 & 93.2 \(\pm\) 0.13 & 82.1 \(\pm\) 0.30 \\  & U-Net & 89.6 \(\pm\) 0.20 & 93.1 \(\pm\) 0.11 & 82.0 \(\pm\) 0.51 \\  & U-Net++ & 91.8 \(\pm\) 0.14 & 93.2 \(\pm\) 0.09 & 84.5 \(\pm\) 0.38 \\  & YOLOv8 & 89.0 \(\pm\) 0.21 & 95.5 \(\pm\) 0.10 & 85.5 \(\pm\) 0.50 \\ \hline \multirow{4}{*}{ImageNet-1k} & **Football Net** & 87.5 \(\pm\) 0.30 & 91.2 \(\pm\) **0.20** & 85.0 \(\pm\) **1.01** \\  & ViT & 87.0 \(\pm\) **0.29** & 91.0 \(\pm\) 0.22 & 84.6 \(\pm\) 1.05 \\ \cline{1-1}  & RepViT & 87.7 \(\pm\) 0.50 & 91.4 \(\pm\) 0.34 & 86.8 \(\pm\) 1.20 \\ \cline{1-1}  & U-Net & 67.5 \(\pm\) 2.30 & 87.5 \(\pm\) 0.88 & 77.0 \(\pm\) 3.03 \\ \cline{1-1}  & U-Net++ & 71.0 \(\pm\) 1.05 & 89.0 \(\pm\) 0.37 & 79.5 \(\pm\) 2.10 \\ \cline{1-1}  & YOLOv8 & 88.5 \(\pm\) 0.40 & 91.7 \(\pm\) 0.31 & 84.0 \(\pm\) 1.08 \\ \hline \end{tabular}
\end{table} 
Table 2: Contrast experiment results on ImageNet. Figure 6: Train on CIFAR.