## 3 Results and Discussion  
To perform the training and the testing of the model, two datasets were created:  
* The first one was used for training and validation purposes. To create it, 6400 images of 2.5 cm radius circular laboratory plates were taken, each containing seawater captured in a mussel sea farm. In the laboratory of the fishing farm, mussels are kept in separate seawater tanks according to their size and gathered using sleeve filters for each specific size of each class (40, 100, 180); with this procedure, it can be guaranteed that each sample just contains individuals of one class, what helps labeling the images. Table 1 displays the samples obtained for the first dataset:  
Table 2 shows the characteristics of the optics used to take the images of the samples; each sample was photographed twice (in two different microscope configurations) to handle the different larvae sizes:  
* The second dataset was formed by mixing individuals of the three different classes in a tank of seawater so that it would contain mussels at three different stages in their growth. This dataset was used for validation purposes, to detect, classify, and count the individuals. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Size (\(\mu\)m)** & **Samples** \\ \hline
0 & 40 & 2400 \\
1 & 100 & 1800 \\
2 & 180 & 1200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset description. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Zoom** & **Video Magnification** & **Objective** & **Aperture** \\ \hline
1 & 4.99\(\times\) & 1.6\(\times\) & 1\(\times\)/0.09 & 0.09302 \\
2 & 3.97\(\times\) & 1.3\(\times\) & 1\(\times\)/0.08 & 0.07673 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Properties of the image capturing process in their two configurations. Once we had a relatively large dataset, it was labeled to inform the algorithm about the different classes present in the samples. For this process, an open-source third-party tool, labelImg, was used. The bounding boxes around each mussel were manually identified by a team of biology experts, resulting in a text file in which the class is identified with an id (in this case 0 = 40 \(\upmu\)m, 1 = 100 \(\upmu\)m and 2 = 180 \(\upmu\)m) along with the 4 points that make up the vertices of the boxes within the images. Figure 5 showcases the process of manually labeling the samples in the pictures taken:  
The next step was to perform the data augmentation described in the previous section and to save 20% of the images taken for the testing phase of our model and keep 80% of them for training purposes. A Python script was created to perform the CutMix algorithm and to distribute the files randomly in a _train_ and _test folder_ according to the use they would have. Special care was taken to make sure that a similar number of images of all classes were available in order to realistically evaluate the model under random circumstances. Figure 6 displays a picture of a sample of seawater with mussel larvae to test the capability of the algorithm:  
The algorithm was implemented under the Python language. Table 3 displays the average number of individuals accounted in different pictures of different mussel classes,  
Figure 5: Manual labeling of the samples. Figure 6: Counting mussel larvae in a sample plate under microscope. and the average manually accounted larvae present, as well as the time spent in both types of processes. It is shown that the average time the algorithm takes to evaluate the presence of the larvae remains practically constant, whilst the average time a scientist needs to account for the individuals is much longer; it is necessary to remark, also, that these samples just contain larvae of the same class, making it much easier for a human to make the accounting. Another test performed to check the validity of the algorithm was to use pictures of other species, very similar in their larva state, to mussel: clam.