# High-Accuracy Clothing and Style Classification via Multi-Feature Fusion
## 3 Methods and Methodology
### Improved Network Architecture  
Our classification network is improved from ConvNeXt network, which is the best performance feature extraction network available, and no complex structure is improved on the basis of ResNet. The details of the network parameter settings are based on some ideas from Transformer [34] or Swin Transformer [35]. ConvNeXt uses a lot of experimental data to prove which settings work well, and those that work well are retained, and the accuracy of classification by such settings alone exceeds that of Swin Transformer. Because the volume of our data set is small and the classification type is only small, we used ConvNeXt-T to design a network suitable for the proposed clothing category classification. Table 1 shows the details of the proposed network parameters for clothing category classification. The input size of the model is set as a \(224\times 224\times 3\) square image in the design. As shown in Table 1, in the first layer of the model, the images are preprocessed, using \(4\times 4\) convolutional kernels with a step size of 4 and a number of 96, so that the images are scaled down in the scale direction before processing and enriched and expanded with information in the number of channels, and Layer Normalization is performed to accelerate the convergence of training and improve the stability of network fluctuations. Then, the feature map will be fed into the ConvNeXT Block. There are four ConvNeXT Block groups in the whole network structure, where the number of ConvNeXT Blocks in each group is \(3,3,9,\text{ and }3\), respectively. A criss cross attention module is set up after the first ConvNeXT Block group to obtain criss cross attention weight feature maps. The second to fourth ConvNeXT Block groups are preceded by a downsample layer to adjust the size and number of channels of the feature map, and a spatial attention module is connected after the last ConvNeXT Block group to collect the attention weights of the feature map in the spatial dimension.After this, we use the global average pooling operator to pool the entire feature layer, making the spatial information of the model more representative, avoiding overfitting in this layer, reducing computational effort, and enhancing robustness \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Output Size** & **Channel** & **Kernel Size/Stride** \\ \hline Convolution & \(56\times 56\) & 96 & \(4\times 4\) / 4 \\ \hline ConvNeXt Block & \(56\times 56\) & 96 & \(\begin{bmatrix}d7\times 7/1\\ 1\times 1/1\\ 1\times 1/1\end{bmatrix}\times 3\) \\ \hline Criss Cross Attention & \(56\times 56\) & 96 & - \\ \hline Downsample & \(28\times 28\) & 192 & \(2\times 2\) / 2 \\ \hline ConvNeXt Block & \(28\times 28\) & 192 & \(\begin{bmatrix}d7\times 7/1\\ 1\times 1/1\\ 1\times 1/1\end{bmatrix}\times 3\) \\ \hline Downsample & \(14\times 14\) & 384 & \(2\times 2\) / 2 \\ \hline ConvNeXt Block & \(14\times 14\) & 384 & \(\begin{bmatrix}d7\times 7/1\\ 1\times 1/1\\ 1\times 1/1\end{bmatrix}\times 9\) \\ \hline Downsample & \(7\times 7\) & 768 & \(2\times 2\) / 2 \\ \hline ConvNeXt Block & \(7\times 7\) & 768 & \(\begin{bmatrix}d7\times 7/1\\ 1\times 1/1\\ 1\times 1/1\end{bmatrix}\times 3\) \\ \hline Spatial Attention & \(7\times 7\) & 768 & - \\ \hline Global Avgpool & \(7\times 7\) & 1024 & \(7\times 7\) / 1 \\ \hline Full Connection & \(1\times 1\) & 1000 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network structure with input image size \(224\times 224\times 3\). to changes in the input space. The last layer is the full connection layer of the model. The full connection dimension changes with the number of categories. Each ConvNext block uses a \(7\times 7\) convolution kernel with a step size of 1 and the number of input channels for deep separable convolution to learn image features while reducing computational effort. The number of channels is then adjusted using two consecutive \(1\times 1\) convolution kernels of step 1.