## 4 Experiments and Results Discussion
### Improving the Classification Base Line  
The authors of the Grocery Store Dataset [3] trained a DenseNet-169, connecting the feature vector to an SVM classifier. They obtained 85% accuracy on test by using a model on which they performed a fine tuning. In the same manner, they tested a DenseNet-169 classification network without using SVM. In this case, they obtained 84% accuracy. In our experiment, two recent models have been evaluated: a ResNeXt-101 with cardinality 32 and a bottleneck width of eight [4] and a ResNet-152 [33]. The ResNeXt-101 model has been modified by readating the input shape to 600 \(\times\) 600 \(\times\) 3. We have divided the training set, composed of 2640 images, into 1820 images for training (68.94%) and 820 for validation (31.06%). The reason for this distribution is that we have balanced the distribution, to try to distribute 30% of the images of each category to validation in a random manner. The test set consists of 2485 images, and it has been pre-defined by the authors of the Grocery Store Dataset [3]. The training of the ResNeXt-101 has been performed by using an Adam optimizer with a learning rate of 0.00005. The feature vector was connected to an output _dense_ layer of 81 categories with a _softmax_ activation function. The initial weights used were those of ImageNet [31]. A batch size of eight was used, and the model was trained for 40 epochs, reaching the highest validation accuracy at epoch 30. The training accuracy was 99.78%, while the validation accuracy was 99.76% in epoch 30. The evaluation of the model was performed by using the test set. The test accuracy was 90.80%, with a precision of 92.50%, recall of 92.10%, balanced accuracy of 92.09% and F1-Score of 92.30%. The same experiment was carried out with a ResNet-152. However, despite the depth of this network, the results were slightly worse. The test accuracy was 89.90%, with a precision of 91.60%, recall of 91.10%, balanced accuracy of 91.09% and F1-Score of 91.30%. Figure 6: Third approach: Siamese net with ResNeXt-101-32x8d and LOMO descriptor. The next experiment consisted in adding a dropout layer with a random deactivation of 20% to the input of the last classification layer of the ResNeXt-101. This modification of the model improved the result up to 91.60% test accuracy, 93.60% precision, 92.20% recall, 92.18% balanced accuracy and 92.18% F1-Score. Figure 6(a) shows the training and validation accuracy, while Figure 6(b) shows the loss of this model. A Precision-Recall curve with the average precision score micro-averaged over all classes is displayed in Figure 8. In addition, a Multi-Class Precision-Recall curve with all classes is displayed in Figure 9. The evaluation of the model shows a good performance, resulting in the 96% AUC (Area Under the Curve). The confusion matrix of this model is shown in Table 1. For the sake of space, instead of showing the 81 fine classes, we show the 43 coarse categories where the results have been grouped. Although the overall results have improved, the confusion matrix shows two categories that the model is not able to detect properly: oranges and satsumas. In order to avoid confusion between oranges and satsumas, another model has been trained to distinguish between oranges and satsumas, again with a ResNeXt-101 with a 20% dropout layer. When the result of the first model is oranges or satsumas, the image is sent to the second classifier. The training/validation and test sets are the same as those used for the first model but only filtering these two classes. The training result of this second model has been 98.86% training accuracy and 94.87% validation accuracy. The test  
Figure 8: Precision-Recall curve with the average precision score micro-averaged over all classes. Figure 7: ResNeXt-101 training with 20% dropout. accuracy in the detection of oranges/satsumas has been 79%, 79% precision, 78.5% recall, 78.5% balanced accuracy and 78.8% F1-Score. By staggering the two models, the test accuracy has increased to 92%. Table 2 shows the summary of the results.