# High-Accuracy Clothing and Style Classification via Multi-Feature Fusion
## 4 Experiments
### Result  
With the clothing category classification, we train the ConvNeXt network with dual attention with the DeepFashion data set. Within 25 epochs, our network converges rapidly and tends to continue to decline at the end of the training. The accuracy rate on the validation set is also gradually improved in the process of training, reaching the accuracy rate of about 66.97% at the end of the training. The reason why it does not exceed 0.8 is that the data classification in the DeepFashion data set is not clear enough, and there are often hybridized situations. We can see that each image is predicted correctly, but the confidence level is too low. Part of the reason is that the number of training iterations is too few and the data set is not regular enough. However, on the whole, the clothing classification network designed by us can converge well and has good recognition robustness after a short period of 50 rounds of training, so as to achieve the goal we expect. We saved the model trained on the DeepFashion data set for subsequent transfer learning.  
We trained ConvNeXt-Tiny and ConvNext-Tiny with dual attention in the Style10 data set for 100 epochs. The validation set accuracy for ConvNeXt-Tiny was 65.98% and for ConvNeXt-Tiny with dual attention was 66.17%. The performance is improved with the addition of two attention modules. Then, we used the model trained for 50 rounds on DeepFashion and made ConvNext-Tiny with dual attention network train for 50 rounds on Style10 by means of transfer learning. It can be seen from Table 3 that there is a very obvious improvement. With the help of transfer learning, ConvNeXt-Tiny with dual attention achieved 69.66% accuracy on the Style10 validation set.  
However, our proposed ConvNeXt network with dual attention still achieves a high accuracy in the actual detection. We have tested and verified 10 images of different clothing types. Below each image is the clothing category name predicted by the network and the confidence level of the category.  
With the clothing style classification, we select Style10 data set to train the model and record the change of the validation set accuracy value during the training process. It can be seen that the ConvNeXt network with dual attention using transfer learning is fast and effective in terms of convergence speed and degree; it shows that our assumption of using clothing types to transfer training clothing style is correct, and it can be used to improve performance in different aspects in the future. In the second place is the ConvNeXt network with a dual attention classification network, which combines ConvNeXt network and dual attention. This network does not use transfer learning in the training process, but its effect is better than ConvNeXt network without transfer training.  
From this, we can see that the purpose of using clothing types to transfer training clothing style can be achieved, and the effect is very ideal. It shows that the relationship between the fine-grained attributes of clothing and the whole style is close. As long as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Precision** & **CPU** \\ \hline VGG19 & 59.60\% & 1224 ms \\ GoogleNet & 63.41\% & **185 ms** \\ Resnet50 & 65.67\% & 549 ms \\ DenseNet201 & 65.59\% & 771 ms \\ SWIN-T & 64.75\% & 744 ms \\ \hline ConvNeXt-T(baseline) & 65.98\% & 635 ms \\ \hline
**Ours w/o transfer** & 66.17\% & 752 ms \\
**Ours w/ transfer** & **69.66\%** & 752 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparision of results on the Style10 val set.  
the fine-grained attributes can be reasonably mined, an ideal result can be achieved. The excellent performance of ConvNeXt network with a dual attention structure also confirms the rationality of feature combination. It can be predicted that the effect of transfer learning on the ConvNeXt network with dual attention is better than that of the ConvNeXt network with dual attention that did not use transfer learning.