## 4 Results
Therefore, during the first testing phase, fish fin annotation was used for positioning, along with other parameter adjustments for image training, to compare differences in recognition capabilities. The preliminary test results are provided below:  
The model was trained using Sample A, which consists of images captured by mobile device A, and tested on Sample B, which consists of images captured by mobile device B. The use of different mobile devices allows us to evaluate the model's robustness across varying image qualities and conditions, ensuring that the model can generalize well to different capture environments. In the first test, only Sample A was trained, with different parameters tested:  
The error rate in identifying male fish was significantly higher than that for female fish. By using fin annotation for positioning, the impact of other contaminants was reduced, effectively decreasing the problem of gender misidentification. However, issues persisted, such as low confidence levels or failure to identify the gender at all. It can be inferred that even after positioning, male fish samples remained more challenging to identify. By \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Type** & **Sample Source** & **Content** \\ \hline Training Sample & A Phone & - Male Fish: 133 images - Female Fish: 119 images - Male Fish: 21 images - Female Fish: 21 images - Female Fish: 21 images - Male Fish: 20 images - Female Fish: 20 images - Female Fish: 20 images \\ \hline \hline \end{tabular}
\end{table} 
Table 1: First test sample data. annotating the fish fins, the recognition rate for male fish can be significantly improved, which is the most critical aspect of the model. This is illustrated in Figure 2.  
The validation results for male fish, as shown in the table below, indicate that under non-rotating conditions, there is a higher error rate, leading to recognition failures. Similarly, the test results for female fish exhibited low confidence levels. This suggests that applying angle rotation can enhance recognition accuracy. Likewise, enabling random scaling, as opposed to using fixed sizes, can improve recognition capability and reduce the likelihood of recognition failures. The data from the rotation and scaling experiments for female fish showed positive results; however, to avoid redundancy, the visualization of female fish data is not included. Refer to Figure 3 for further details. Different orientations and settings from various mobile devices result in varying levels of testing accuracy. When the model trained on Sample A was tested using Sample B, the recognition accuracy failed to meet the standard threshold, highlighting the need to improve the model's flexibility and generalization. For Sample A, the results from tests with a smaller sample size outperformed those with a larger sample size. Conversely, for Sample B, the tests with a larger sample size outperformed those with a smaller sample size. This indicates that different photographic environment parameters affect the results depending on the sample size, and various shooting methods and camera settings can lead to variable test outcomes. The imaging results from Sample A were more favorable for recognition. For Sample B, the fixed-size-mode test results were superior to those from random scaling, which is in stark contrast to the results observed in Sample A. This demonstrates a completely different recognition outcome, as illustrated in Figure 4.  
Figure 2: Results for Sample A with and without fish fin annotation: (**a**) Sample A male fish test, no fin annotation; (**b**) Sample A female fish test, no fin annotation; (**c**) Sample A male fish test, with fin annotation; (**d**) Sample A female fish test, with fin annotation. In the second test, a small amount of Sample B data was added as training material to enhance the model's generalization. Please refer to Table 2 for details. After incorporating these additional samples, the model underwent separate validation and testing.