# GhostNeXt: Rethinking Module Configurations for Efficient Model Design
## 4 Experiments
### Small-Scale Datasets  
We first compared GhostNeXt with other light-weight models [6; 7; 9] on small-scale datasets. We used GhostNeXt (\(1.0\times\)) as our base model with the module configuration \(E-R-R\) representing the best trade-off between performance and computational complexity, based on experimental results in Section 4.4. Note that the module configuration \(E-R-R\) is constructed by the controller selecting \(E\), \(R\), and \(R\) channel operations for each part. In Table 3, GhostNeXt outperforms other competitors with fewer parameters and FLOPs for most computational complexity levels and tasks. It consumes about three times fewer parameters than GhostNet. We can see that dynamic module configuration not only reduces unnecessary computational costs inside the block but also improves the expressiveness of the network. Figure 2 shows the results on CIFAR-10 under different parameters and FLOPs. Regardless of the number of parameters or FLOPs, GhostNeXt outperforms all other models. While GhostNet cannot maintain the performance for a small number of parameters, GhostNeXt exhibits a better trade-off between the number of parameters and performance than GhostNet. Furthermore, since GhostNet has a high parameter count compared to its FLOPs, when comparing performance with models that have similar FLOPs, it is observed that its performance is comparable to that of ShuffleNetV2. However, when models are compared based on similar parameters, GhostNet performs worse than ShuffleNetV2. In contrast, GhostNeXt shows the best trade-off among all models, indicating its flexibility in handling resource constraints. These results suggest that GhostNeXt is a promising alternative to the compared approaches for various applications that require efficient and accurate image classification.  
We also conducted an experiment based on ResNet-56 to demonstrate the proposed method as a plug-and-play component, whose results are shown in Table 4. Ours-ResNet-56 denotes ResNet-56, in which a Ghost module replaces the convolutional layer with  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **CIFAR-10** & **CIFAR-100** & **STL-10 [17]** & **STL-10 [18]** & **CUBS [12]** & **Stanford Cars [13]** & **Flowers [14]** & **WikiArt [15]** & **Sketch [16]** \\ \hline \# Train & 50,000 & 50,000 & 5000 & 5994 & 8144 & 2040 & 42,129 & 16,000 \\ \# Test & 10,000 & 10,000 & 8000 & 5794 & 8041 & 6149 & 10,628 & 40,000 \\ \# Class & 10 & 100 & 10 & 200 & 196 & 102 & 195 & 250 \\ Image Size & \(32\times 32\) & \(32\times 32\) & \(96\times 96\) & \(224\times 224\) & \(224\times 224\) & \(224\times 224\) & \(224\times 224\) & \(224\times 224\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets used for experiments.  
Figure 2: Performance under different parameter budgets and FLOPs on CIFAR-10.  
a dynamic module configuration. Furthermore, Ghost-ResNet-56 denotes Ours-ResNet-56 without dynamic module configuration. Our proposed method provides comparable performance while reducing the parameter consumption and FLOPs by about 2.5 times and 1.5 times compared to Ghost ResNet-56. It can be employed for other convolutional neural networks stacked by modules or blocks and effectively reduce the computation cost of the network.