# Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning
## 4 Experiments and Results
### Clothing Type Category Classification
In the second experiment, data augmentation was introduced, namely random horizontal flip and random rotation, which normally leads to improvements in the model. The obtained results are described in Table 7.  
Based on the results in Table 7, it can be concluded that amongst data augmentation, the random horizontal flips feature effectively contributed to the improvement in the model's performance. As in the first experiment, GoogleLeNet allowed us to obtain the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Time (ms)** & **Standard Deviation** \\ \hline ResNet 50 & 0.0126 & 0.251 \\ ResNet18 & 0.0081 & 0.162 \\ MobileNetV2 & 0.0117 & 0.235 \\ GoogLeNet & 0.0169 & 0.338 \\ MobileNet V3 Small & 0.0120 & 0.239 \\ EfficientNet-B0 & 0.0197 & 0.394 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Inference time by network architecture. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Train** & **Validation** & **Train Loss** & **Validation Loss** & **Epochs** & **Time (s)** \\ \hline \multicolumn{6}{c}{With Random Horizontal Flip} \\ \hline ResNet-50 & 0.958 & 0.906 & 0.112 & 0.307 & 6 & 262.63 \\ ResNet-18 & 0.950 & 0.907 & 0.146 & 0.275 & 6 & 162.28 \\ MobileNet V2 & 0.927 & 0.910 & 0.201 & 0.271 & 6 & 228.77 \\ GoogLeNet & 0.941 & 0.911 & 0.171 & 0.272 & 10 & 356.57 \\ MobileNet V3 & 0.911 & 0.893 & 0.251 & 0.320 & 7 & 223.88 \\ EfficientNet-B0 & 0.921 & 0.903 & 0.223 & 0.276 & 13 & 542.21 \\ \hline \multicolumn{6}{c}{With Random Rotation (90)} \\ \hline ResNet-50 & 0.934 & 0.887 & 0.187 & 0.323 & 11 & 364.47 \\ ResNet-18 & 0.914 & 0.887 & 0.238 & 0.339 & 10 & 272.93 \\ MobileNet V2 & 0.911 & 0.887 & 0.245 & 0.334 & 12 & 464.70 \\ GoogLeNet & 0.908 & 0.894 & 0.269 & 0.306 & 14 & 509.61 \\ MobileNet V3 & 0.896 & 0.880 & 0.281 & 0.379 & 15 & 481.10 \\ EfficientNet-B0 & 0.896 & 0.880 & 0.295 & 0.321 & 18 & 804.20 \\ \hline \hline \multicolumn{6}{c}{With Random Horizontal Flip and with Random Rotation (90)} \\ \hline ResNet-50 & 0.947 & 0.887 & 0.148 & 0.328 & 15 & 508.69 \\ ResNet-18 & 0.904 & 0.885 & 0.260 & 0.331 & 11 & 306.74 \\ MobileNet V2 & 0.908 & 0.884 & 0.255 & 0.334 & 13 & 539.59 \\ GoogLeNet & 0.903 & 0.883 & 0.259 & 0.325 & 17 & 608.41 \\ MobileNet V3 & & & & & & \\ (small) & 0.899 & 0.880 & 0.271 & 0.369 & 17 & 533.98 \\ EfficientNet-B0 & 0.908 & 0.891 & 0.254 & 0.304 & 25 & 1041.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test performance results with augmented data. best accuracy value (91.1%) and MobileNet V3 (small) resulted in the lowest accuracy value (89.3%). Finally, augmented data with random rotation resulted in poor performance. Following this, the precision, recall and F1 score obtained for the GoogLeNet network are presented in Table 8. Complementarily, Figure 6 depicts the confusion matrix of the model with the best result based on the performance measurements (GoogLeNet). The confusion matrix allows the visualization of the performance of the classification model. Independently of the good performance of the model, it is worth noting that there are some misclassifications between similar clothing articles, such as casual shoes and sport shoes, heels and flats, and t-shirts and tops (Figure 6). The third experiment employed transfer learning via feature extraction, where the weights of all layers are frozen, except the output layer. The obtained results show that the loss of the network is ca. 90 %, and it is therefore not viable. Thus, the results show that using a pre-trained network, the fine-tuned GoogLeNet with augmented data, with proper hyper-parameters, effectively outperforms the work proposed by [PERSON] et al. [40]. Comparatively, the obtained results can be explained by the fine-tuning of all network layers, leveraging the pre-trained weights of all architectures and replacing only the last layer, which differs from [PERSON] et al. [40], where new head layers were added.