# GhostNeXt: Rethinking Module Configurations for Efficient Model Design
## 4 Experiments
### Analysis  
We conducted an experiment to demonstrate the performance under different module configurations. The results of different strategies for selecting channel operations in each part are shown in Table 6. As the same channel operation can be chosen when the channel reduction ratio varies, we report the average accuracy, parameters, and FLOPs in the table. The table shows that dynamic module configuration effectively reduces the number of parameters and FLOPs compared to the existing module configuration. Most of the network configurations constructed by dynamic module configuration outperform or perform similarly to GhostNet. It can be observed from the table that selection in the deep part (close to the classification head) has a key influence on the number of parameters and FLOPs of the network. Moreover, continuous channel expansion can increase the complexity of the network. We also experimented with demonstrating that the dynamic module configuration preserves the key feature of an image. We empirically prove this by providing a visualization of the output features of six different blocks using Grad-CAM [19]. We make notable observations from the results in Figure 3. GhostNet loses significant feature information. It primarily focuses on the background in the early layers and the object in the deep layers. In contrast, the proposed method can preserve meaningful information about object shapes from the early layer. It continuously preserves the meaningful shape information of an object throughout the network and thus gains additional performance improvement. We conducted experiments to evaluate the expressiveness of the GhostNet and GhostNeXt quantitatively. This evaluation is to see how the dynamic module configuration affects the expressiveness of the network. We visualize the expressiveness represented by the nuclear norm with respect to the consumption of FLOPs and parameters. We used the validation set of CIFAR-10 to analyze the expressiveness of final features extracted from the trained networks in Table 6. The result in Figure 4 shows that GhostNeXt's selection of different channel operations for each part are similar to or more expressive than  
Figure 3: Visualization of features extracted from GhostNet and GhostNeXt on STL-10. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**MobileNetV2 (0.6\(\times\))**} & \multicolumn{3}{c}{**ShuffleNetV2 (1.0\(\times\))**} & \multicolumn{3}{c}{**GhostNet (1.0\(\times\))**} & \multicolumn{3}{c}{**Ours (1.3\(\times\))**} \\  & **ACC** & **Params** & **FLOPs** & **ACC** & **Params** & **FLOPs** & **ACC (\%)** & **Params** & **FLOPs** & **ACC** & **Params** & **FLOPs** \\  & **(\%)** & **(M)** & **(M)** & **(\%)** & **(M)** & **(M)** & **(M)** & **(M)** & **(\%)** & **(M)** & **(M)** & **(M)** \\ \hline CUBS [12] & 53.6 & 1.2 & 164.0 & 53.1 & 1.5 & 149.0 & 55.9 & 4.1 & 150.0 & 58.2 & 1.2 & 136.0 \\ \hline Stanford Cars [13] & 76.0 & 1.2 & 164.0 & 75.7 & 1.5 & 149.0 & 79.0 & 4.1 & 150.0 & 80.3 & 1.2 & 136.0 \\ \hline Flowers [14] & 58.2 & 1.1 & 164.0 & 60.5 & 1.4 & 149.0 & 64.8 & 4.0 & 150.0 & 66.3 & 1.1 & 136.0 \\ \hline WikiArt [15] & 64.2 & 1.2 & 164.0 & 59.4 & 1.5 & 149.0 & 62.0 & 4.1 & 150.0 & 65.0 & 1.2 & 136.0 \\ \hline Sketch [16] & 72.8 & 1.3 & 164.0 & 71.7 & 1.5 & 149.0 & 71.6 & 4.2 & 150.0 & 72.3 & 1.3 & 136.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the compared methods on the large-scale datasets. GhostNet. We especially observed that networks with expansion operations selected in the deep part had higher expressiveness than those without the operations. Dynamic module configuration encourages expanding the expressiveness of the network while consuming fewer parameters and FLOPs than GhostNet. Furthermore, we analyzed the expressiveness by block in the network used in the Grad-CAM experiment. As shown in Figure 5, the expressiveness of GhostNeXt is higher than that of GhostNet in all blocks. the block.