## 4 Experiments
### Results
In Table 9, conditions (3) and (4) using Mobilenetv2 show 9.8 G FLOPs, 2.1 M parameters, and 22.3% GPU usage, which are all more efficient than the Xception architecture in terms of computational complexity. This is because the Mobilenetv2 architecture has a smaller number of filters and a shallower network structure than the Xception architecture and requires less computational demand and resource usage by applying the Linear Bottleneck structure. The results in Tables 8 and 9 and Figure 5 show that the HMR framework is suitable for creating 3D models using Mobilenetv2. Accordingly, additional experiments were conducted to secure the general applicability of the DeepLabv3 model with the Mobilenetv2 architecture. That is, the experiments were conducted on combinations of the Atrous rates of the Atrous convolutions in the ASPP (Atrous Spatial Pyramid Pooling) module, which is used to detect information of various scales and integrate the global context in the image in the DeepLabv3 model in order to derive the optimal structure of the ASPP module. An example of each Atrous rate of the Atrous Convolution is shown in Figure 6. The experiment was conducted under the same hardware conditions as the experiment in Table 9, with three conditions for the Atrous rates of the Atrous Convolution in the ASPP module: (1) single Atrous rate of 12, (2) two Atrous rates of 6 and 12, and (3) three Atrous rates of 6, 12, and 18. Semantic segmentation was performed and the IoU (Intersection of Union; %), FLOPs, and #param under each condition were measured. The experimental results are shown in Table 10.  
The experimental results show that applying the ASPP module (ASPP condition (3)) consisting of three-layer Atrous Convolution with Atrous rates of 6, 12, and 8 has the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Computational Metrics** & \
\begin{tabular}{c} **MobilenetV2 Architecture** \\ **(Conditions (1) and (2))** \\ \end{tabular} &
\begin{tabular}{c} **Xception Architecture** \\ **(Conditions (3) and (4))** \\ \end{tabular} \\ \hline FLOPs & 35.5 G & 9.8 G \\ \hline \# param. & 22.8 M & 2.1 M \\ \hline GPU usage (\%) & 65.6\% & 22.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Computational complexity of semantic segmentation with 513 \(\times\) 513 image inputs under MobilenetV2 architecture and Xception architecture. Figure 6: Examples of Atrous Convolution with kernel size 3 \(\times\) 3 under three different rates of 1, 6, and 12.  
highest IoU of 73.1%. However, in terms of computational complexity, ASPP conditions (1) and (2) were shown to have superior performance with 8.8 GFLOPs and 1.9M FLOPs and 9.1 GFLOPs and 2.0M #param, respectively, compared to 9.8 GFLOPs and 2.1M for the ASPP condition (3). Considering these results, it was confirmed that setting the conditions of the ASPP module according to the specifications of the computational hardware device used in the process of segmenting the user's 2D image, rather than specifying one methodology, allows for a wide range of applications to various computational hardware (such as low-cost PCs and mobile devices). As a result, the selected measurement method to process human segmentation based on Mobilenetv2 architecture, before modeling the 3D human model using HMR framework, can produce reasonable results, while solving the limitations of the existing measurement method, such as expensive 3D scanners, long measurement time, and limited clothing conditions for measurement.