## 6 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Dataset** & **Size** & **\#Images**\({}^{1}\) & **\#Annotations**\({}^{1}\) & **mAP\({}_{50}\)** & **mAP\({}_{75}\)** & **IoU**\({}^{2}\) & **Precision**\({}^{2}\) & **Recall**\({}^{2}\) \\ \hline YOLOv4 & OzFish & 608 & 1753 & 43,572 & 68.46\% & 25.94\% & 59.28\% & 79\% & 65\% \\ YOLOv4 & OzFish+NS\({}^{3}\) & 608 & 3506 & 43,572 & 72.93\% & 35.93\% & 66.96\% & 86\% & 66\% \\ \hline YOLOv4 & CCMAR & 608 & 4100 & 13,318 & 88.33\% & 65.78\% & 70.36\% & 85\% & 84\% \\ YOLOv4 & CCMAR & 608 & 12,500 & 67,750 & 92.99\% & 74.49\% & 73.44\% & 87\% & 86\% \\ YOLOv4 & CCMAR & 416 & 35,510 & 113,886 & 91.90\% & 68.96\% & 73.31\% & 88\% & 83\% \\ YOLOv4 & CCMAR & 512 & 35,510 & 113,886 & 92.74\% & 72.67\% & 74.28\% & 88\% & 84\% \\ YOLOv4 & CCMAR & 608 & 35,510 & 113,886 & 93.11\% & 74.70\% & 75.10\% & 89\% & 84\% \\ YOLOv4-tiny & CCMAR & 416 & 35,510 & 113,886 & 85.00\% & 45.33\% & 68.74\% & 87\% & 71\% \\ YOLOv4-tiny-3l & CCMAR & 608 & 35,510 & 113,886 & 89.66\% & 61.02\% & 73.69\% & 90\% & 73\% \\ \hline \hline \end{tabular}  
\({}^{1}\) # means number of. \({}^{2}\) For IoU threshold at 50%. Confidence threshold at 50%. \({}^{3}\)_Negative sample_ images.  
\end{table} 
Table 1: Model training parameters and results using the YOLOv4 methods. Before proceeding to the final stage of the presented method, an EDA on the obtained pseudo-dataset was performed, which revealed different results relative to the underlying environmental conditions. While evaluating the bounding box automatic annotations, which influence the IoU metrics, a recurrent tendency across the fish movement was observed, where the end part and the tail zone were extended from the fins, leaving a blank space proportional to the size of the bounding box, while the head of the fish presented the opposite, with the head presenting itself on the edge or outside the bounding box annotation; some of these results can be reviewed in Figure 6. This effect can be the result of compounded errors of the applied tracking system during the training loop, or derivative of the rapid movement of the fish's tail. To evaluate the automatically generated CCMAR dataset, we trained multiple models, varying the number of images from 4100 to 12,500 and 35,510, and afterward, the network size from 416 \(\times\) 416 to 512 \(\times\) 512 and 608 \(\times\) 608, while training with 35,510 images. It is important to note that the amount of annotated images corresponds to half of the data used for training, and the other half represents the _negative sample_. Another relevant point is the almost proportional rise in the number of annotations versus the number of images, although the distribution of the number of fishes varied between one and more than sixty fishes in one frame. The images without annotations were not considered due to their possibility of containing false-negative detections. Moreover, the obtained metrics are presented in Table 1.  
For the dataset size variation, an expected rise across all the relevant metrics was observed. The mAP\({}_{50}\) rose 4.66% from 88.33% when increasing the automated pseudo-labeled images from 4100, i.e., 2050 annotated images plus the _negative sample_ to 12,500, having increased an insignificant 0.12% when expanding the number of images to 35,510. Similarly, the mAP\({}_{50}\) rose by 8.71% from 65.78%, and then by 0.21%, respectively. The IoU increase also followed similar proportions, rising 3.08% from 70.36%, and afterwards 1.66%. Precision rose from 85% to 87%, and finally to 88%, while recall increased from 84% to 86%, and then regressed to 84%. Considering the obtained results, it was observed what apparently could become a threshold to predict the exact amount of images needed for a dataset, reducing the training requirements needed to obtain the desired results. Furthermore, the first sample increment introduced significant gains, while the latter even reduced the number of targets detected.