## 4 Results
This table summarizes the data used in the second test, where a small portion of Sample B was included in the training set to enhance the model's generalization. Validation and testing were performed using data from both Sample A and Sample B. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Type** & **Sample Source** & **Sample Content** \\ \hline \multirow{3}{*}{Training Sample} & A Phone & - Male Fish: 133 images \\  & A Phone & - Female Fish: 119 images \\  & B Phone & - Male Fish: 26 images \\  & A Phone & - Female Fish: 36 images \\ \multirow{3}{*}{Validation Sample} & A Phone & - Male Fish: 21 images \\  & A Phone & - Female Fish: 21 images \\  & B Phone & - Male Fish: 6 images \\ \multirow{3}{*}{Test Sample} & A Phone & - Female Fish: 9 images \\  & A Phone & - Male Fish: 20 images \\ \multirow{3}{*}{Validation Sample} & A Phone & - Male Fish: 20 images \\  & B Phone & - Female Fish: 20 images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample data for the second test. Figure 3: Comparison results for Sample A male fish: no fin annotation, angle rotation, and random scaling: (**a**) Sample A male fish test, random 180\({}^{\circ}\) rotation; (**b**) Sample A male fish test, random scaling; (**c**) Sample A male fish test, no rotation; (**d**) Sample A male fish test, fixed size. Although the training sample size for Sample B was relatively small, incorporating Sample B still improved recognition accuracy and reduced the number of recognition errors. This indicates that increasing the sample size would likely result in a significant enhancement of recognition capabilities. Under these conditions, the recognition accuracy for farmed fish reached a high level of 95.76%. Female Fish Recognition Data:  
* Number of Photos: 495;
* Recognition Failures: 0;
* Low-Confidence Recognitions: 6;
* Gender Misidentifications: 11. * Male Fish Recognition Data:
* Number of Photos: 873;
* Recognition Failures: 6;
* Low-Confidence Recognitions: 24;
* Gender Misidentifications: 11. Refer to Figure 5 for a visual representation of these results. In-depth data exploration reveals that the failure rate for female fish recognition is relatively low, with the majority of errors being related to gender misidentification. Figure 4: Test results for Sample B imaging: (**a**) male fish test with fixed image size; (**b**) female fish test with fixed image size; (**c**) male fish test with random image scaling; (**d**) female fish test with random image scaling. Figure 6 illustrates the distribution of recognition rates when using the AI model to identify female fish. Figure 6a shows the distribution of high recognition rates. Across approximately 500 recognition events, the distribution of the AI model's recognition rates for female fish is displayed. The horizontal axis represents the event number, and the vertical axis represents the recognition rate (percentage). It can be observed that most of the model's recognition rates are concentrated in the high range (97.5% to 100%), indicating that the model is very accurate in recognizing female fish in most cases. However, there are some events with lower scores, indicating that the model's performance is inconsistent in certain situations. Figure 6b shows the distribution of low-recognition events. The low recognition rates vary significantly, ranging from 45% to 75%. This suggests that while the model generally performs well, there are specific instances where its accuracy drops considerably. Although the recognition failure rate for male fish is relatively higher, the overall recognition accuracy still exceeds 95%. Male fish are more likely to be misidentified as female due to the lower recognition accuracy. The related distribution can be seen in Figure 7.  
Figure 5: Overall sample recognition accuracy: (**a**) female fish recognition accuracy: 96.57%; (**b**) Male fish recognition accuracy: 95.30%. Figure 6: Female fish recognition rate distribution charts: (**a**) female fish high-recognition-rate distribution; (**b**) female fish low-recognition-rate distribution.