## 4 Proposed Lightweight AI Model Development Procedures
### AI Model Simplification
The number after \"B\" indicates the level of compound scaling applied to the baseline architecture. Higher numbers represent larger and more computationally intensive models, which generally achieve a better performance but require more resources. Therefore, as I move from EfficientNetB0 to EfficientNetB7, the models become larger and more powerful, capable of handling more complex object classification tasks. However, they also require more memory and computational resources for training and inference. Table 4 lists the number of parameters, memory size, and scaling factor for each EfficientNetB model. By fine-tuning the scaling factor, I had the ability to customize the model size and achieve simplification. Simplified ShuffleNet: ShuffleNet is a family of AI architectures designed to strike a balance between model accuracy and computational efficiency. ShuffleNet utilizes the channel shuffle operation to enable information exchange across groups of feature channels, enhancing the representational capacity. It employs depth-wise separable convolutions to reduce the computation and model size. ShuffleNetV2 introduces the concept of residual connections within ShuffleNet units to further improve information flow. ShuffleNetV3 introduces a new ShuffleNet unit design called \"Ghost Unit,\" which includes a \"ghost\" branch to enhance information flow without significantly increasing computations. Table 5 lists the number of parameters and memory size of each ShuffleNet model. Each ShuffleNet \begin{table}
\begin{tabular}{c c c c} \hline \hline
**EfficientNet Model** & **The Number of Parameters** & **Memory Size** & **Scaling Factor** \\ \hline EfficientNetB0 & 4,059,819 & 48.3 MB & minimum \\ \hline EfficientNetB1 & 6,585,487 & 78.2 MB & 1.0 \\ \hline EfficientNetB2 & 7,779,841 & 92.2 MB & 1.1 \\ \hline EfficientNetB3 & 10,795,831 & 127.5 MB & 1.2 \\ \hline EfficientNetB4 & 17,688,167 & 208.4 MB & 1.4 \\ \hline EfficientNetB5 & 28,529,919 & 335.6 MB & 1.6 \\ \hline EfficientNetB6 & 40,978,583 & 481.4 MB & 1.8 \\ \hline EfficientNetB7 & 64,118,175 & 752.5 MB & 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The number of parameters, memory size, and scaling factor of EfficientNet models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**DenseNet121 Model** & **The Number of Parameters** & **Memory Size** \\ \hline Growth Rate 4 & 169,086 & 3.9 MB \\ \hline Growth Rate 8 & 527,976 & 8.0 MB \\ \hline Growth Rate 10 & 784,833 & 11.0 MB \\ \hline Growth Rate 12 & 1,094,694 & 14.6 MB \\ \hline Growth Rate 16 & 1,869,240 & 23.6 MB \\ \hline Growth Rate 32 & 7,045,704 & 83.9 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of parameters and memory size of DenseNet121 model. model provides different trade-offs between model size, computational efficiency, and accuracy, making them suitable for various applications with varying resource constraints. Simplified MobileNet: MobileNet is a family of neural network architectures that provide efficient and lightweight solutions on resource-constrained devices. One of the key design principles of MobileNet is the use of depth-wise separable convolutions, which significantly reduce the computation and model size while preserving the accuracy. The architecture of MobileNet is governed by a parameter called \"alpha\", which controls the width of the network. The width of the network determines the number of channels in each layer and, consequently, the number of parameters and computation involved in the model. As shown in Table 6, by adjusting the alpha parameter, different versions of MobileNet can be created, each optimized for a specific balance between model size and accuracy. Simplified Xception: Xception is a neural network architecture that supports lightweight solutions on resource-constrained devices. The original Xception model consists of a 9-layer structure repeated eight times for linear stacking. The authors in ref. [22] suggest removing seven repeated structures to build a L-w Xception 1 model, containing only 9.5 million parameters.