# Fashion Product Classification through Deep Learning and Computer Vision
## 6 Experimental Results
### CV Modules  
In logo detection, the type is not the only class to be predicted. The system searches first for large logos, and if nothing is found, the medium and the small logo searches are performed in the same cascade logic. The total execution time for logo detection depends on which logo is recognized first. If a large logo is detected, it takes only the inference time of the large logos neural model (i.e., 1-2 s). Otherwise, the time of inferring a medium logo is spent. The running time of the CV template matching algorithm, instead, is of about 30 s for each iteration. There are three Iterations for each template, i.e., for each type of logo to be detected. There are three logo types (see Section 3), but not all nine iterations are performed for each image, since the algorithm stops after finding one of them. Adidas data experts test this logic on a dataset of 290 images, and then they analyze separately the results of each classifier on logo types. In Table 8, predictions are compared to references in the confusion matrix, and the frequencies for each class is indicated. The type of classification of the 86 over 95 images that depict large logos was shown in the previous section in Table 5. Performance of the template matching approach gains **92.0**% overall \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **\# of Frozen Layers** & **Total \# of Layers** & **Validation Accuracy** \\ \hline
**VGG19** & **15** & **29** & **0.97** \\ DenseNet & 369 & 714 & 0.90 \\ InceptionV3 & 164 & 318 & 0.95 \\ NASNetLarge & 401 & 776 & 0.89 \\ ResNet50 & 94 & 182 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results on medium logo detection, using different architectures. The best choice of network and frozen layers in terms of validation accuracy is highlighted in boldface font. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prediction / Reference** & REGULAR & MEDIUM & LARGE & **Frequency \#** & **Frequency \%** \\ \hline REGULAR & **194** & 4 & 2 & 197 & 67.9 \\ MEDIUM & 0 & **4** & 0 & 9 & 3.1 \\ LARGE & 3 & 1 & **82** & 84 & 29.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results on logo size detection on an unseen validation dataset, performed by the Adidas team. Overall accuracy is **96.6**% on 290 images. The numbers of correct predictions per class (on the main diagonal of the matrix) are highlighted in boldface font. accuracy on small logos on the 200 images that were classified as REGULAR.The confusion matrix is shown in Table 9. It is worth noting that there are no small linear logos. In this particular dataset, there are none, but normally these logos are not recognized by our template matching algorithm. This is because a linear logo template must depict a sub-part of both BOS and trefoil logos (the \"adidas\" writing on the bottom of the logo, as shown in Figure 1), and this fact may generate a huge number of false positives. Regarding the three-stripes module, results were obtained by comparing the output of the classification with a ground truth of 1375 images. Such a dataset contains also 8% of images that does not belong to neither FULL LENGTH nor PARTIAL classes. The overall accuracy of stripes length is **80**% on that dataset. Colors, instead, do not have a precise accuracy value, since both the color names and the coverage percentage are subjective attributes. On the contrary, the color module in combination with the prints and three-stripes classifiers gains measurable results. In the first case, the color module has to recognize the type of \"composition\", choosing among SOLID, COLOR BLOCKS, and SMALL INSERT classes, as outlined in Section 3 and in Figure 5. On the 213 not-printed products recognized using the prints classifier, as in Table 6, the overall accuracy is **70.4**%. The confusion matrix and class frequencies are shown in Table 10.  
The most frequent misclassification, in this case, regards solid color garments. The reason is the presence of small regions, such as logos, of a different color.