# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 1 Introduction  
Camels are remarkable animals for their ability to adapt to harsh environmental conditions. They can survive for long periods without drinking water thanks to their specialized physiology and behavior. They can also tolerate high temperatures and solar radiation as well as cold and windy climates. Therefore, camels are a vital resource for desert dwellers who have developed rich knowledge regarding the breeds and characteristics of camels. However, most people lack the expertise to recognize the different breeds of camels, especially Arabian camels, which may appear similar but have distinct features and colors. Camels are large, hump-backed and iso-toed ungulates belonging to the mammalian genus Camelus of the family Camelidae. There are two species of camels: the Arabian camel (Camelus dromedarius) and the Bactrian camel (Camelus bactrianus). This work focuses on the Arabian camel, which has several breeds that mostly vary in color. One of the most widely accepted classifications of Arabian camel breeds is based on the King AbdulAziz Camel Beauty Competition, also known as Mezayen, which divides the participating Arabian camels into six categories: Majaheem (black camels), Waddeh (white camels), Shageh (off-white camels with some redness), Sofor (yellow camels with black humps), Homor (red-brown camels), and Shaele (pure yellow camels) [1, 2]. Figure 1 shows an example of each breed. In terms of color, certain categories of Arabian camel breeds, such as Majaheem and Waddeh, are easily distinguishable. However, other categories, including Shageh, Sofor, Homor, and Shaele, exhibit a similar appearance and coloration, which can lead to misidentification. Image-based Arabian camel breed classification is a challenging problem that has not been adequately addressed in the literature. Unlike other animal breeds such as birds and dogs, which have been extensively studied using image classification techniques, Arabian camel breeds have distinctive and subtle features that require more sophisticated methods to recognize and distinguish them. In this paper, we propose a novel approach for image-based Arabian camel breed classification using transfer learning on convolutional neural networks (CNNs). Transfer learning is a powerful technique that enables the reuse of knowledge learned from one domain to another domain with different data distributions. CNNs are state-of-the-art deep learning models that can automatically learn hierarchical representations from raw image data and achieve high performance on various computer vision tasks, such as image classification, object detection, face recognition, and more [3]. Although there are several publicly available image-based datasets for animal breed classification, such as the Stanford Dogs dataset [4], Oxford IIIT-Pet Dataset [5], and Tsinghua Dogs Dataset [6], there is currently no dataset specifically designed for the classification of Arabian camel breeds. This presents an opportunity for researchers to create a new dataset to address this gap in the literature and advance the field of animal breed classification. In our work, we adapt pre-trained models originally trained on the ImageNet dataset to a new and smaller dataset of Arabian camel images. By doing so, we aim to learn features specific to camels and use them to classify the images into different camel breeds. This novel dataset of Arabian camel images is collected from various sources and annotated with their corresponding breeds by subject matter experts. We evaluate several pre-trained well-known CNN architectures with diverse characteristics on our dataset, including InceptionV3 [7], NASNetLarge [8], PNASNet-5-Large [9], MobileNetV3-Large [10], and EfficientNetV2 (small, medium, and large variants) [11]. Our proposed approach to image-based Arabian camel breed classification brings several benefits. Firstly, by using transfer learning, we can leverage the knowledge learned from pre-trained CNNs on another large datasets such as ImageNet to improve the classification accuracy of our model.