# EnCaps: Clothing Image Classification Based on Enhanced Capsule Network
## 3 Experiments and Results
### The Comparison with Other Methods  
The accuracy of convolutional neural network in image classification still depends on the number of samples, the data augmentation strategy, and so on. The problem of how to obtain the same performance with insufficient data annotation is also a concern. With less reliance on supervised learning and priori human annotation information, it is our goal to achieve better performance with smaller amounts of data. To demonstrate the superiority of EnCaps for small sample detection, we verify the accuracy of the 10 networks as the dataset is gradually incremented, as shown in Figure 7. The training set grows from 500 to 4500, the accuracy of EnCaps on the 500 validation set holds the leading value of 0.56 at first, and, as the dataset is incremented, the accuracy on the validation set is consistently higher than the rest of the networks, and maintains a very stable performance. Figure 6: Data augmentation. During the training, we use ablation experiments to verify the functions of attention mechanism and advanced feature extraction mechanism, respectively. First, we perform a validation of the validity of the channel attention module in the model. We examine the validation set loss and the validation set accuracy of the model, respectively, during the gradual increase of the attention module. As shown in Figure 8, as the attention module increases from 0 to 4, the validation loss of the network decreases and reaches a minimum that is 4. The validation accuracy increases and reaches a peak at the number of 4. As the number increases from 4 to 9, the validation accuracy gets smaller first, some performance is lost, and the validation loss bounces back, which in turn reduces the overall efficiency of the model. To further investigate the role of each module, we examine the accuracy and loss values on the validation set by removing the stem module, and the reduced module. There are three cases in experiments: (1) removal of the stem module, (2) removal of the reduced module, and (3) removal of the stem module and reduced module. Figure 8: The relationship between the number of attention modules and network performance. Figure 7: With the decrease of the number of training sets, the accuracy of each network in the verification set (500 pieces) is improved. As shown in Table 3, when we remove the stem module, the accuracy on the validation set decreases from 0.842 to 0.682, and the validation loss value increases from 27.58 to 47.42. It can be intuitively learned that the effect of the stem module on our network is not only to reduce the amount of data, but also the contribution to the performance of the model by extracting more advanced semantic features of the images. When we remove the reduced module, the accuracy on the validation set drops from 0.842 to 0.778, and the validation loss value increases from 27.58 to 43.10. It is clear that the reduced module plays a bridging role in EnCaps, it performs further enhancement to the advanced feature of the stem module, and it normalizes the enhanced feature so that it can be more logical to access the final capsule module. The attention module in the reduced module succeeds in highlighting the focus weights in the feature map, and it has a side reaction to the importance of the stem module. When we remove both the stem and reduced modules, namely it indirectly uses the capsule module, the accuracy on the validation set decreases from 0.842 to 0.628, and the validation loss value increases from 27.58 to 52.76, and it demonstrates that the importance of both modules in the overall module for classification performance. Without these two modules, the number of network parameters and operations increases by a factor of 0.3, and the efficiency is plummeted. We compare our method with several stare-of-the-art methods including VGG [29], GoogLeNet [30], ResNet [31], DenseNet [32], MobileNetV2 [33], and Capsule network [21].