## 4 Proposed Lightweight AI Model Development Procedures
### AI Model Simplification  
Simplified ResNet: ResNet is a series of deep convolutional neural network architectures that introduced the concept of residual learning. The key idea in ResNet is the use of skip connections or shortcuts that bypass one or more layers, allowing information from previous layers to directly flow to subsequent layers. The ResNet family includes several variants, each distinguished by the number of layers. The most common variants are ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. The numbers indicate the total number of layers in the network, including both convolutional layers and fully connected layers. Table 2 lists the number of layers, parameters, and the memory size of the family of ResNet models. As the depth of the model increases, there is a substantial rise in the number of parameters and memory size. Consequently, I had the option to modify the number of layers in ResNet models to achieve simplification. Simplified DenseNet: DenseNet is a widely used convolutional neural network architecture known for its effectiveness in object classification tasks. One of the key components that distinguishes DenseNet from other AI model architectures is its dense connectivity pattern, where each layer is connected to every other layer in a feed-forward manner. This connectivity promotes feature reuse, enables efficient information flow, and contributes to better parameter efficiency. As shown in Table 3, a critical parameter in DenseNet is the growth rate, which determines the number of additional feature maps each layer contributes. For example, in the original DenseNet121 model, the growth rate is set to 32. This means that each dense block in the architecture adds 32 feature maps to the output of the previous layer, significantly increasing the number of parameters and computation. By adjusting the growth rate to a number that is less than 32, the new DenseNet121 model will have a simpler architecture with fewer feature maps per layer. Such simplified DenseNet121 models are especially attractive when working with limited computational resources or aiming for a more lightweight architecture. For example, by reducing the growth rate to 8, the model becomes less complex, as each dense block will only add eight feature maps per layer. This leads to a reduction in the total number of parameters, making the model more memory-efficient and faster to train and infer. On the other hand, such simplified DenseNet121 models still retain the benefits of dense connectivity and multi-level feature fusion. This simplification allowed us to strike a balance between model complexity and performance, making DenseNet121 with a lower growth rate a viable option for hardware-limited robot vacuum cleaner applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ResNet Model** & \
\begin{tabular}{c} **The Number** \\ **of Layers** \\ \end{tabular} &
\begin{tabular}{c} **The Number** \\ **of Parameters** \\ \end{tabular} & **Memory Size** \\ \hline ResNet18 & 18 & 11,194,312 & 131.4 MB \\ \hline ResNet34 & 34 & 21,318,728 & 250.3 MB \\ \hline ResNet50 & 50 & 23,604,104 & 277.1 MB \\ \hline ResNet101 & 101 & 42,674,568 & 501.0 MB \\ \hline ResNet152 & 152 & 179,521,992 & 2104.8 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of layers, parameters, and memory size of the family of ResNet models. Simplified EfficientNet: EfficientNetB0 to EfficientNetB7 is a family of convolutional neural network architectures, which uniformly scale the depth, width, and resolution of the network to find a balance between model size and classification performance. While it achieves a state-of-the-art accuracy with relatively fewer parameters compared to other models, it can still be computationally expensive for hardware-constrained applications. The \"B\" in EfficientNetB0 to EfficientNetB7 represents its baseline architecture, which starts with a relatively small depth, width, and image resolution.