## 3 Classification Model  
Although some of the jellyfish images extracted from the \"Spot the Jellyfish\" campaign database were captured by SCUBA divers, and thus show the entire jellyfish against a consistently blue background, the majority of database photos were captured from a boat or from the shore. Therefore, even though the images were all of high resolution, in most cases the jellyfish individuals only occupied a small region of the frame. As a result, highly reliable detection and classification schemes were required to correctly identify the jellyfish in the images. Initially, conventional convolution neural networks (CNNs) were trained to classify the raw and unprocessed images into five categories for the identification of _Pelagia noctiluca_, _Cotylrolriza tuberculata_, _Carybdea marsupialis_, _Velella velella_ and salps jellyfish. While good results were generally obtained, \begin{table}
\begin{tabular}{l c c} \hline \hline
**Species** & **Number of Training Images** & **Number of Testing Images** \\ \hline _Carybdea marsupialis_ & 89 & 40 \\ _Cotylrhizae tuberculata_ & 201 & 40 \\ _Pelagia noctiluca_ & 170 & 40 \\ Salps & 52 & 40 \\ _Velella velella_ & 90 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of training and testing images per species. Figure 1: Images of _Carybdea marsupialis_, _Cotylrhizae tuberculata_, _Pelagia noctiluca_, _Salp_ and _Velella velella_. Figure 2: Original (**left**) and cropped (**right**) versions of a _Pelagia noctiluca_ training image. the applicability of region-based convolution neural networks (R-CNNs) was subsequently tested and found to further improve the classification method's accuracy. Such deep learning techniques improve on the CNN models by following a two-stage approach. Initially, sub-regions within the image that might contain a jellyfish are identified. Features are then extracted from the convolved areas and adopted as an input for the classifier. In this study, five classification models were implemented to distinguish between five different jellyfish species. In all cases, transfer learning was used to improve the accuracy. Such an approach involves the use of a pre-trained CNN that can extract features from thousands of natural images coming from various categories. The developed faster R-CNN models were then optimised to extract features that are specific to jellyfish images. The five models were based on ResNet18, ResNet50 and GoogLeNet. The ResNet18 and ResNet50 networks were 18 and 50 layers deep, respectively. These have been trained on more than a million images [25]. On the other hand, GoogLeNet is 22 layers deep [26]. Since these networks are based on \(224\times 224\) images, all jellyfish frames had to be resized in order to ensure consistent dimensions. The resizing of images was carried out through a bicubic interpolation scheme. Table 2 provides a summary of the salient differences between the five deployed classification models. The time required to classify the 200 test images is also listed. Data augmentation was also applied to increase the number of training examples. Deep learning techniques such as the Faster R-CNN method often rely on big data. However, in this case, only a few examples were available. In such circumstances, the model might learn the function very well by overfitting. Although very accurate results will be noted during the training process, the performance on unseen images will not be equivalent. In this study, as only 602 training images were available, overfitting was avoided through augmentation. As discussed by [PERSON] and [PERSON] [27], such a process will help to create synthetic instances to increase the size of the training set. In this case, the jellyfish images were vertically and horizontally flipped and in each case, a different feature layer was defined. For the majority of cases, the number of anchors was set to three. However, in classification Model 5, this was increased to ten. The test images were not part of the training set.