# Fashion Product Classification through Deep Learning and Computer Vision
## 6 Experimental Results
### DL Modules
The best choice of parameters in terms of validation accuracy is highlighted in boldface font. An example result for the deep learning approach is shown in Table 5, for large logo types. The network recognize correctly **93**% of the images in a dataset of 86 unseen images, chosen by Adidas data experts (how the 86 number come out is explained later, in Section 6.2). Those images are unseen to the extent that are part of a new dataset provided by Adidas after the training phase. Thus, it does not contain any of the previous images in the 2616 dataset. The classification of these images is acceptable, with only a little misclassification of linear and original (BOS) logos. Table 5 shows the confusion matrix, with prediction classes on the first column and the reference on the first row. The number of images per class and their frequency percentages are also indicated. The inference of the class of an image takes usually 1-2 s of execution time, on a GTX 1070 GPU, for each feature to obtain by means of neural models. Thus, the expected average computational time for processing the whole validation dataset for large logo ranges from 86 s to 172 s. The exact computational times are unavailable, since in fact the validation tests were made by Adidas team on Adidas servers. As another example, the very different problem of prints recognition is analyzed. The validation dataset contains 250 images, and the overall accuracy of the printed classifier is **94.4**%. The 250 images dataset is different from both the previous logo dataset (consisting of 86 images), and the manual classified dataset of 2616 images. The confusion matrix, as well as class frequencies are reported in Table 6. For computational times, the previous mentioned considerations hold. Validation results from Adidas team for neck shape, sleeves, and material are not yet available. However, their models were trained using the datasets and the parameters specified in the following:  
* _Neck shape._ A smaller dataset whose images were extracted from the 2616 images dataset is used. In detail, both test and training set consist of 630 images. All images were cropped in order to depict only the neck and a little portion of the article around the neck (examples are in Figure 3). A batch size of 2 is used for 80 epochs, and the learning rate had a value of 0.001. Data augmentation was used, as specified in the previous section. The final accuracy achieved is **95**% on the test dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction/Reference** & PRINTED & NORMAL & **Frequency \#** & **Frequency \%** \\ \hline PRINTED & **213** & 7 & 220 & 88.0 \\ NORMAL & 7 & **23** & 30 & 12.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results on prints classification on an unseen validation dataset, performed by Adidas team. The numbers of correct predictions per class (on the main diagonal of the matrix) are highlighted in boldface font. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Rate** & **Accuracy** & **\# of Epochs** \\ \hline
0.01 & 0.824 & 14 \\
**0.001** & **0.947** & **8** \\
0.0001 & 0.68 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Learning rate parameter variations. The best choice of parameters in terms of validation accuracy is highlighted in boldface font. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prediction/Reference** & BOS & TREFOIL & LINEAR & **Frequency \#** & **Frequency \%** \\ \hline BOS & **46** & 0 & 0 & 46 & 53.5 \\ TREFOIL & 0 & **24** & 2 & 28 & 32.6 \\ LINEAR & 0 & 4 & **10** & 12 & 13.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results on large logo detection on an unseen validation dataset, performed by Adidas team. The numbers of correct predictions per class (on the main diagonal of the matrix) are highlighted in boldface font. * _Sleeves_. As for neck shape, the dataset used for sleeves is a slightly smaller portion of the original one. It consists of 2400 images, of which 1550 belong to the training set, and 850 to the test set.