# GhostNeXt: Rethinking Module Configurations for Efficient Model Design
## 4 Experiments
### Large-Scale Datasets  
We also compared with other light-weight models [6; 7; 9] to demonstrate the superiority of GhostNeXt for recognizing large-scale images, as described in Table 2. From CUBS to Sketch, each is a fine-grained classification dataset consisting of birds, cars, flowers, paintings from artists, and sketches. Since these require better preservation of the fine-grained features of an image than a general coarse-grained classification, we considered that these are appropriate for determining whether detailed feature information is well preserved. Furthermore, we set it the same way, except that the batch size was increased to 64 in the setting of small-scale datasets. We resized all images to 256 \(\times\) 256 pixels and random cropping to 224 \(\times\) 224 pixels.  
Table 5 shows the experimental results on the large-scale datasets. GhostNeXt outperforms other lightweight models, including GhostNet, with fewer parameters and FLOPs. In particular, it consumes about three times fewer parameters than the competitor and has the same number of parameters as MobileNetV2 but fewer FLOPs. The dynamic module configuration for each block effectively reduces the parameter consumption of GhostNet and preserves detailed feature information, resulting in performance improvements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **ACC (\%)** & **Params (M)** & **FLOPs (M)** \\ \hline ResNet-56 [4] & 93.0 & 0.8 & 125.0 \\ Ghost-ResNet-56 & 92.6 & 0.5 & 53.5 \\ Ours-ResNet-56 & 92.5 & 0.2 & 38.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the compared methods on CIFAR-10.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} } & \multicolumn{2}{c}{**CIFAR-100**} & \multicolumn{2}{c}{**STL-10**} & \multicolumn{2}{c}{**CIFAR-10**} \\  & & **Params (M)** & **FLOPs (M)** & **ACC (\%)** & **Params (M)** & **FLOPs (M)** & **ACC (\%)** & **Params (M)** & **FLOPs (M)** \\ \hline VGG-16 & 74.4 & 14.8 & 314.0 & 75.4 & 14.7 & - & 93.6 & 14.7 & 313.0 \\ ResNet-56 & 72.5 & 0.8 & 127.3 & 77.7 & 0.8 & 290.0 & 93.0 & 0.8 & 125.0 \\ ResNeX-29 (2 \(\times\) 64d) & 79.8 & 7.6 & 263.6 & 80.6 & 7.5 & 606.6 & 96.0 & 7.5 & 263.5 \\ ResNeX-29 (4 \(\times\) 64d) & 81.0 & 14.7 & 511.4 & 81.5 & 14.6 & 1160.0 & 96.2 & 14.6 & 511.3 \\ \hline MobileNetV2 (0.5\(\times\)) & 68.1 & 0.8 & 19.6 & 71.3 & 0.7 & 49.6 & 90.1 & 0.7 & 19.5 \\ ShuffleNetV2 (0.5\(\times\)) & 69.2 & 0.4 & 11.9 & 71.4 & 0.3 & 26.6 & 90.9 & 0.3 & 11.8 \\ GhostNet (0.5\(\times\)) & 71.6 & 1.4 & 11.8 & 70.1 & 1.3 & 25.2 & 91.6 & 1.3 & 11.7 \\ Ours (0.7\(\times\)) & 71.5 & 0.5 & 10.3 & 71.2 & 0.4 & 22.5 & 91.8 & 0.4 & 10.1 \\ \hline MobileNetV2 (1.0\(\times\)) & 72.9 & 2.3 & 68.9 & 73.2 & 2.2 & 161.0 & 92.2 & 2.2 & 68.8 \\ ShuffleNet2 (1.0\(\times\)) & 73.7 & 1.3 & 45.5 & 74.7 & 1.2 & 102.3 & 93.3 & 1.2 & 45.4 \\ GhostNet (1.0\(\times\)) & 75.3 & 4.0 & 40.6 & 71.6 & 3.9 & 87.7 & 93.0 & 3.9 & 40.5 \\ Ours(1.5\(\times\)) & 75.4 & 1.4 & 39.1 & 75.7 & 1.3 & 87.0 & 94.1 & 1.3 & 38.9 \\ \hline MobileNetV2 (1.5\(\times\)) & 72.8 & 5.1 & 144.7 & 74.4 & 4.9 & 355.0 & 92.3 & 4.9 & 144.0 \\ ShuffleNet2 (1.5\(\times\)) & 75.7 & 2.5 & 93.9 & 75.5 & 2.4 & 211.0 & 94.0 & 2.4 & 93.8 \\ GhostNet (1.5\(\times\)) & 77.2 & 7.9 & 86.4 & 74.4 & 7.7 & 187.7 & 94.3 & 7.7 & 86.3 \\ Ours (2.2\(\times\)) & 76.2 & 2.6 & 79.8 & 77.1 & 2.5 & 178.1 & 94.2 & 2.5 & 79.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance on the CIFAR-100, STL-10, and CIFAR-10 datasets.