## 4 Results  
The confusion matrices obtained from testing all five classification models with the same set of images are presented in Table 3. Here, the columns represent the true (actual) class of the jellyfish shown in the test images, while the rows correspond to the category predicted by the classifier. A \"not classified\" category was added to the list of categories within the \"predicted\" section, to denote the images that registered a jellyfish which was not characterised by the classifier. The test set contained 40 images for each jellyfish species. For a perfect classifier, the values within the diagonal would be 40, implying that the correct species was identified every time. As demonstrated below, this was not always the case. Confusion matrices help to identify whether a classifier is behaving randomly or whether for instance, it cannot distinguish between two classes. The precision, recall and \(f_{1}\) score for every class by every model were computed by Equations (1)-(3). Here, \(TP\) represents the number of true positive cases and corresponds to the number of instances for which the correct class was predicted. On the other hand, \(FP\) corresponds to the false positives and indicates the number of times other jellyfish were incorrectly classified with the group being tested. The number of instance that belong to the class under test but were labelled otherwise are treated as false negatives (\(FN\)). The results obtained for the trained models are presented in Table 4. The precision score gives an estimate of the fraction of the predicted jellyfish species that actually belong to the same species. The recall score indicates the percentage of correctly classified instances. Obviously, classifiers with high precision and recall scores are preferred. The \(f_{1}\) score combines these two metrics by taking the harmonic mean. The [PERSON]'s \(\kappa\) score that measures the convergence between the actual classes and the predicted ones, was computed as shown in Equation (4). For this metric, \(p_{o}\) represents the actual observed agreement and is equivalent to the accuracy of the model while \(p_{e}\) is the probability expected by a random classifier. In this case, a value of 1 denotes perfect agreement, while a value of zero indicates that the classifier performance is similar to that of a random draw. \[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
Figure 3: Adopted framework for the training of each classifier. \[f_{1}=\frac{2(\text{ }Recall\times Precision)}{Recall+Precision} \tag{3}\] \[\kappa=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{4}\]  
Clearly, the implementation of transfer learning has allowed very accurate jellyfish species classifications/assignments to be made. The models that performed best where those that used the GoogLeNet feature extraction network. While an increase in the number of anchors resulted in a slower classification process, a higher accuracy was also concurrently obtained through this measure. As expected, ResNet50 gave better results than ResNet18 and took less than 4 more seconds to classify the 200 test images. Figures 4-8 present a sample of the results obtained through the implementation of classification Model 5.