# EnCaps: Clothing Image Classification Based on Enhanced Capsule Network
## 3 Experiments and Results
### The Comparison with Other Methods
The quantitative evaluation results are shown in Table 4. We use the validation set to validate each network separately, the number of parameters, and the computational effort of MobileNetV2 are minimal, but the corresponding accuracy is also reduced due to the lack of computational effort. The accuracy of EnCaps, 0.842, is the highest of all the networks. The accuracy of the second highest is ResNet, whose value is 0.820, but with a higher number of parameters and operations than EnCaps. The accuracy of original Capsule network is 0.628, which is much lower than all other networks, and the number of parameters and operations is about 0.3 times higher than EnCaps network. In summary, EnCaps is able to obtain very high detection results in the field of classification, especially in clothing image classification, and maintains a lightweight model volume that remedies the shortcomings of traditional capsule network in classification. We use visualization to compare the computation, number of parameters, and validation accuracy among 10 types of model, as shown in Figure 9, where the horizontal axis represents the MFLOPs (Million Floating-Point Operation Per Seconds) and the number of trainable parameters respectively, and the vertical axis represents the validation accuracy. By defining the graph, it can be seen that the graph is closer to the \(y\)-axis and further from the \(x\)-axis, which indicates the higher performance of the model. It can be seen that the EnCaps is at the highest point and relatively close to the \(y\)-axis, which means that our model is at the best performance level with a lightweight module and high accuracy. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Validation Accuracy** & **Validation Loss** \\ \hline \(w\)/\(o\) stem module & 0.682 & 47.42 \\ \(w\)/\(o\) reduced module & 0.778 & 43.10 \\ \(w\)/\(o\) stem \& reduced module & 0.628 & 52.76 \\ EnCaps & 0.842 & 27.58 \\ \hline Real & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiments of EnCaps. From the previous conclusions, it is clear that the performance of ResNet-18 is outstanding among all 10 networks. Therefore, on the validation set, we examine the detection results of 500 pieces of clothing images with EnCaps and ResNet-18, respectively, and draw a confusion matrix that counts the number of detection results per image. As shown in Figure 10, the \(x\)-coordinate represents the predicted label, the \(y\)-coordinate represents the true label, and each square represents a count of the number of predicted results on the true label. It can be seen that seven EnCaps tests have a number of correct detections above 40, with a relatively low false detection rate, while only five of the ResNet-18 tests have a number of correct detections above 40, with a relatively high false detection rate. Some clothing that has different local features of the clothing leads to different types of clothing, and the ability of detected images at a fine granularity becomes a key basis for determining whether the model has high performance. EnCaps has a high recognition accuracy rate for the detection of different types of clothing, and it has a strong ability to screen fine-grained information to realize the highly robust. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **MFLOPs** & **Trainable Params (M)** & **Top-1 Acc.** \\ \hline VGG11 & 257.592715 & 491.36 & 0.792 \\ VGG16 & 353.619785 & 674.52 & 0.75 \\ VGG19 & 364.23662 & 694.78 & 0.762 \\ GoogLeNet & 11.953082 & 22.83 & 0.784 \\ ResNet18 & 22.363325 & 42.65 & 0.82 \\ ResNet50 & 47.057184 & 89.75 & 0.788 \\ ResNet101 & 85.041593 & 162.2 & 0.782 \\ DenseNet121 & 18.396804 & 26.57 & 0.796 \\ MobileNetV2 & 4.468249 & 8.53 & 0.712 \\ Capsule (28 \(\times\) 28) & 13.863176 & 26.11 & 0.628 \\ EnCaps & 10.019512 & 19.13 & 0.842 \\ \hline Real & N/A & N/A & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of calculation, parameter, and accuracy between different networks.