## 3 The Proposed ReSiNet for Low-Memory Devices
### Construction of ReSTiNet
#### 3.2.2 Fire Module of ReSTiNet  
The introduction of the fire module under ReSTiNet was to decrease the number of parameters as well as escalate the width and depth of the entire network. This was performed in order to ensure the accuracy of detection. This model consists of both expand as well as the squeeze components so that the model's network tends to expand and  
Figure 4: The structure of ReSTiNet. Fire modules are adopted from SqueezeNet, which shrinks the model. Then, residual connections are integrated from ResNet-50 network inside the fire modules to enhance the proposed ReSTiNet’s efficiency.  
compress. The compress or squeeze component utilizes the convolutional layer with a size of \(1\times 1\) introduced by NIN as a substitute for the usual layer with the size \(3\times 3\). In order to decrease the number of parameters, the model that follows the \(1\times 1\) technique was found to be more efficient. Additionally, the accuracy of detection does not reduce significantly as the training parameter is only a single variable that should be learnt. During the expansion, both the models with sizes \(1\times 1\) as well as \(3\times 3\) are typically used. Finally, the arrived outputs from the respective convolutional layers are concatenated at the concatenation layer.  
For a convolutional layer, the parameters are given as \(c_{i}\), the number of channel input variables, \(k\) as the kernel size, and \(c_{o}\) as the number of channel output variables. Using Equation (3), the value of the number of parameters for the convolutional layer is then calculated. The number of channel inputs is \(c_{i}\) for the fire module; \(k_{s_{1}}\) is the kernel size of the squeeze component, and \(s_{1}\) is the number of channel output variables. If the value of \(k_{s_{1}}\) is assigned to 1, a reduction in a large number of model parameters for the squeeze component is possible. The number of channel input variables is \(s_{1}\) followed by the kernel sizes \(k_{e_{1}}\) and \(k_{c_{3}}\) for the expanded component. The total number of channel output variables is the sum of \(e_{1}\) and \(e_{3}\). Using Equation (4), the number of model parameters is calculated. Figure 5 illustrates the structure of the fire modules in ReSTiNet.  
\[P_{conv}=(c_{i}\times k^{2}+1)\times c_{o} \tag{3}\]  
\[P_{fire}=(c_{i}\times k^{2}_{s_{1}}+1)\times s_{1}+(s_{1}\times k^{2}_{e_{1}}+1) \tag{4}\]  
The ability with which the fire can be utilized more effectively depends on the appropriateness of the position of the fire module within the network. ReSTiNet architecture comprises a total of eight fire modules. In the ReSTiNet network, the sixth layer is replaced with the initial four fire modules where the former contains 512 filters followed by down-sampling technique. Layers seventh and eighth containing 1024 filters are replaced with four other fire modules, and this is carried out before the \(1\times 1\) convolutional layer and detection layer. However, the choice of the number of channel inputs \(c_{i}\) is not bounded, while choosing a large number of channel inputs would lead to reduction in parameters.