## 5 Performance Analysis of ReSTiNet  
Tiny-YOLO is used as the backbone architecture for the proposed ReSTiNet model. As Tiny-YOLO has several layers with 512 and 1024 filters, it has a large number of parameters, its speed is slow, and its model size is large. A replacement is carried out, thereby using the fire module instead of the sixth, seventh, and eighth layer present in the Tiny-YOLO method as the fire module contains far lower numbers of parameters as opposed to its counterpart filter of size \(3\times 3\). The input channels also decreased to the filters with a size of \(3\times 3\). Then, by multiplying the number of filters as well as the input channel values, the net parameters present in the fire module can be calculated. By reducing the input channel and filter count, a deep CNN network can be designed containing only fewer number of parameters. Table 6 shows that the parameters that are reduced in the layer containing 256 filters are numerically lower relative to the layer with 512 filters. If the goal is to further decrease the number of model parameters, a replacement with a larger number of channel inputs in the convolutional layer is required while distributing such layers in the middle and the end components of the ReSTiNet module. It was found that the accuracy of detection becomes poor if fire modules substitute the total convolutional layers since fire modules replace certain convolutional layers with a limited number of filters. If the convolutional layers (first five) with a fewer number (less than 256) of convolution filters are retained instead of being substituted by fire modules, the rate of accuracy can improve by 6.2 percent and the size of model can increase by 1.6 megabyte. Thus, ReSTiNet retains the frontal (first five) convolutional layers while replacing the convolutional layers (three) with eight fire modules at the end of Tiny-YOLO. A simplistic method to compress the network is to decrease the number of layers in the network, network scaling factor, and to utilize networks that are considered shallow. However, the degree of freedom to efficiently compress such networks is limited and more distant from existing DNN models [58]. [PERSON] et al. [59] suggested a training procedure of shallow neural networks that best simulates the deep models, but there has been an increase in the number of parameters. In study [60], the authors had shown that the degree of expansion possesses the capability to be exponentially grown as a function of increasing depth. However, networks that are too shallow do not play the role of substitution for deeper networks. As illustrated in Figure 4, there are five pooling layers after the five convolutional layers. It contains a total of eight fire modules with a depth value of 2 followed by convolutional layer with a kernel size \(1\times 1\) in the ReSTiNets architecture. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conv.** & **Input** & **Output** & **Kernel** & **Conv. Layer** & **Fire Module** \\
**Layer** & **Channel** & **Channel** & **Size** & **(Parameters)** & **(Parameters)** \\ \hline
1 & 3 & 16 & 3 & 448 & 184 \\
2 & 16 & 32 & 3 & 4680 & 740 \\
3 & 32 & 64 & 3 & 18,496 & 2888 \\
4 & 64 & 128 & 3 & 73,856 & 11,408 \\
5 & 128 & 256 & 3 & 295,168 & 45,344 \\
6 & 256 & 512 & 3 & 1,180,160 & 180,800 \\
7 & 512 & 1024 & 3 & 4,719,616 & 722,048 \\
8 & 1024 & 512 & 3 & 4,719,616 & 722,048 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter numbers comparison between fire modules and convolutional layers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **mAP (\%)** & **Model Size (MB)** \\ \hline MobileNet & 47.12 & 13.5 \\ SqueezeNet & 41.51 & 3.0 \\ Tiny-YOLO & 42.21 & 60.50 \\ ReSTiNet & **63.79** & **10.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ReSTiNet vs. other lightweight models. The above mentioned eight fire modules in ReSTiNets replaced the three convolutional layers from the last layer in Tiny-YOLO.