## 4 Experiments and Results Discussion
The combination of FN and HN improves the success of both networks, achieving 70.06% of correct detections in the complete test dataset (354 images), against FN (66.10% of 354 images) and HN (66.38%, considering that in this case the test set was composed of 200 images, which is smaller because the number of classes was lower). However, some shabtis were not detected as positive or negative. Considering only positive and negative detections the results are: 82.10% of success for the FN model that has detected 285 shabtis (positive + negative), 78.07% of success for the HN model that has detected 153 shabtis (positive + negative) and 82.39% of success for the FN + HN model that has detected 301 shabtis (positive + negative). These results clearly show that the combination of both models significantly improves the positive detections. Figure 11: Loss function for the first iterations of the FN/HN training. A Precision-Recall (PR) curve has been obtained to evaluate the FN, HN and FN + HN models. The Average Precision (AP) has been computed by integrating the curve, therefore computing the Area Under the Curve (AUC). Figure 13a shows the PR curve for the FN network with an \(AP~{}=~{}0.73\). Figure 13b shows the PR curve for the HN network with an \(AP~{}=~{}0.61\). Figure 13c shows the PR curve for the FN + HN model with an \(AP~{}=~{}0.79\). As previously explained, the combination of both models, FN and HN, improves the model, since the system is able to detect the shabtis either by their shape or by their name. Regardless of whether the system returns the proper owner or not, another important issue is giving the right dynasty and period. Although a shabti could be wrong, it is important to return a similar shabti of the same dynasty or period. The FN + HN model returns the right dynasty in 74.86% of cases and the right period in 81.36% of cases. Considering only the 301 detected shabtis of the FN + HN model, the right dynasty is given in 88.04% of cases and the right period in 95.68% of cases. A confusion matrix of the dynasty and period detection is shown in Tables 4 and 5 respectively. Figure 12: Success of the FN/HN/FN+HN models against the test dataset. Figure 13: Precision-Recall curves for the FN, HN and FN + HN models. With regard to the confusion matrix of the dynasties, most of the errors are situated close to the diagonal. This means that our approach is able to identify that the shabti was produced near that dynasty or period of time. For example, some shabtis of the XXI dynasty were detected between the XXth and the XXII dynasties, but these dynasties happen in a short slot of time according to Egyptian history. In some cases, shabtis of different dynasties, far distant in time, were wrongly detected. For example, shabtis of the XVIII dynasty are detected in the XXVI-XXX dynasty. This happens because some examples of the previous dynasty were reproduced in a very similar style in the next dynasties. Concerning the confusion matrix of the periods, it is important to note that there were shabtis detected on the limit between periods. As an example, the XX dynasty corresponds to the New Kingdom while the XXI dynasty corresponds to the Third Intermediate Period. In these two dynasties, which are very close together, the style of some shabtis were very similar. However, in most cases, the success was 95.68% of correct period identification, as presented before. Although our problem is different from others published, a comparison with similar archaeological problems, such as the ancient coin or pottery sherds classification, can be established. [PERSON] et al. [18] obtained a classification accuracy of 73.6% detecting 60 types of Roman coins; [PERSON] and [PERSON] [17] obtained a classification accuracy of 71.4% also detecting 60 types of Roman coins; and [PERSON] et al. [22] showed an accuracy depending on the class (i.e., 68.15% for coins with a quadriga or 79.28% with a curule) in CoinNet.