## 4 Experiment Results and Analysis
### Experimental Results and Analysis  
In this paper, Densenet121 and Resnet101 are selected as comparison networks to verify Dense Pyramidal Net. The three networks are trained and tested on the cifar10 and cifar100 datasets, respectively. Accuracy and loss are mainly used as comparison metrics. In addition, the size of the model structure is also used as a basis for comparison. The test accuracies of the three networks on the cifar10 and cifar100 datasets are shown in Table 2. The parameters of the three models are shown in Table 3.  
It can be seen from Tables 2 and 3 that the parameter amount of Dense Pyramidal Net is slightly larger than that of densenet121 and much smaller than that of resnet101 but the accuracy of Dense Pyramidal Net is ahead of them. The accuracy curves of each model on the cifar10 and cifar100 test datasets are shown in Figures 10 and 11. As can be seen from the figure, the accuracy of the Dense Pyramidal Net network gradually slowed down around 20 rounds and finally reached a peak, and it was always in the lead in terms of accuracy, showing a stronger learning ability than other networks.  
\begin{table}
\begin{tabular}{c c} \hline
**Part** & **Accuracy** \\ \hline DenseNet & 77.10\% \\ DenseNet with improved residual block & 77.86\% \\ Dense Pyramidal Net without DC block & 79.78\% \\ Dense Pyramidal Net & 83.16\% \\ \hline \end{tabular}
\end{table}
Table 1: Comparison results of each part on Cifar10.  
\begin{table}
\begin{tabular}{c c c c} \hline
**NetworkDataset** & **Dense Pyramidal Net** & **Densenet121** & **Resnet101** \\ \hline Cifar10 & 83.98\% & 77.48\% & 73.74\% \\ Cifar100 & 51.19\% & 46.51\% & 34.94\% \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy of the three algorithms on two datasets.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dense Pyramidal Net** & **Densenet121** & **Resnet101** \\ \hline Params(M) & 18 & 8 & 45 \\ \hline \end{tabular}
\end{table}
Table 3: Three model parameters.  
It can be seen from Figures 12 and 13 that the loss curve of Dense Pyramid Net is between the two, gradually approaching flat, and finally reaching a value of 0. Combining the four pictures, Dense Pyramid Net not only has advantages over traditional algorithms in terms of accuracy but also the loss value curve is between the two algorithms. This shows that this model not only exhibits a high learning rate, that is, it can also quickly converge the loss value to get out of the underfitting state earlier and prevent overfitting. In this respect, the algorithm proposed in this paper is superior to the traditional algorithm and has strong robustness.  
At the same time, the new algorithm ResNeSt50 is also compared, and the experimental results are shown in Figure 14. It can be seen that under the same conditions, the algorithm proposed in this paper is better than ResNeSt50 in 100 rounds of training.  
The experimental results show that the optimized model structure, the use of parallel channels and convolution kernels of different sizes make the feature extraction more sufficient. The network training accuracy is also higher, and the anti-overfitting ability is stronger. The proposed Dense Pyramidal Net network has better performance when compared with DenseNet, the classic network resnnet101 and the new ResNeSt50.