## 2 Overview of Related Work  
To our knowledge, no other paper has been published regarding the problem of shabti identification. However, computer vision techniques have been widely used for different related archaeological problems, such as the classification of ancient coins or pottery sherds. In [17], the authors classify ancient Roman Republican coins, matching a dense set of SIFT features extracted from a picture against a database. Another work for Roman Republican coins using SIFT classification is found in [18; 19], where the authors classify the coins in 60 categories. The SIFT method has been evaluated in our work, giving worse results than YOLOv3 for our problem. [PERSON] et al. [20] have also studied the problem of ancient Roman Republican coins, but in their case, the authors use a bag-of-visual-words (BoW) model [21] adapted for that problem. The authors classify the coins into 28 different motifs. More recently, [PERSON] et al. [22] have created a large dataset for coin classification and they have developed a CNN, named CoinNet. Beyond the problem of Roman coins, CNNs have been used in problems concerning the classification of architectural heritage images, such as the one proposed by [PERSON] et al. [23]. Regarding the classification or pottery, [PERSON] and [PERSON] [24] also use BoW to classify archaeological pottery sherd in 8 categories, extracting local features based on color and texture information to be transformed into a global vector that describes the whole sherd image. In our problem, shabtis are not only classified into a small group of classes. They are identified and classified into a concrete dynasty or period. DNNs have been used for very different problems, such as recently the material modeling [25; 26]. However, CNNs are a concrete type of DNNs that have represented a major advance in image detection and classification over the last few years. Previous methods based on feature extraction and matching, such as SIFT [27], SURF [28] or ORB [29] do not offer such good results, something that we have been able to verify directly in this project. In addition, they are slower than a neural network during normal operation because matching has to be done with all possible images. Neural networks require a costly training, but offer better results and are much faster during operation, since the network itself obtains the category without having to compare it with other images. Among the different existing CNN, a YOLO v3 [30] network has been chosen to support our research. The You Only Look Once (YOLO) algorithm is a state-of-the-art, open source, real-time object detection system that uses a single CNN to detect objects in images. There have been different versions with successive improvements: YOLO v1 and Fast YOLO (2015) [31], YOLOv2, known as YOLO9000 (2016) [32] and YOLOv3 (2018) [30]. YOLO is not the only CNN, but it has achieved some of the best results against ImageNet, in a reasonable time. ImageNet [33] is a dataset used for the \"Large Scale Visual Recognition Challenge\", in which several algorithms compete to see which offers the highest accuracy in object detection in a dataset composed of 1000 different classes. The ImageNet training set had 1.2 million images and the validation/test set had 150,000 photographs. Because YOLO is a detector and not a classifier, it is used to detect objects. A classifier always returns outputs for all the possible classes and shows the probabilities of an object being an element of each of them. The speed of YOLO is improved with the use of clusters of GPUs. YOLOv3 was able to detect correctly in 78.5% of cases the class to which an element belonged (TOP-1), such as a dog or a car, and detected the element 94.7% of times from one of the possible 5 detected objects with greater certitude (TOP-5). In addition to the detection problem, it is useful for a user of our system to know similar shabtis that have been discovered.