# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 3 Experiments and Discussion
### A Comparison with Other CNNs on the Testing Set  
To evaluate the performance of the MIPS-EfficientNet algorithm, we conducted a series of experiments on the testing set, comparing it with other well-known convolutional neural networks (CNNs) including EfficientNet, ResNet-18, ResNet-50, and ResNeXt50. The comparison focused on several key metrics: test accuracy (Test-Acc), recall, precision, and F1 score.  
As shown in Table 3, Multi-Step Image Pre-processing Strategy (MIPS) optimization demonstrably enhanced the performance metrics of both EfficientNet and the ResNet series of models. Specifically, the MIPS-enhanced EfficientNet model showed significant improvements in precision, recall, and F1 score, with the F1 score markedly increasing from 80.68% to 93.78%. The F1 score, being the harmonic mean of precision and recall, more comprehensively reflects the model's performance in handling positive classes, such as target categories of interest. This substantial increase suggests that the original design of EfficientNet, while prioritizing efficiency, may have compromised accuracy to some extent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer** & **Loss Function** & **Train Acc (\%)** & **Val Acc (\%)** \\ \hline \multirow{3}{*}{AdamW} & FL (Focal Loss) & 79.85\% & 79.10\% \\ \cline{2-4}  & CE (Cross Entropy) & 99.21\% & 91.04\% \\ \hline \multirow{3}{*}{SGD} & FL & 99.74\% & 90.30\% \\ \cline{2-4}  & CE & 100.00\% & 97.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: MIPS-EfficentNet training strategies.  
From Table 3, it can be seen that there is no significant difference in the training time between MIPS-EfficientNet and EfficientNet, with both being 1.595 h. This is because MIPS is an image pre-enhancement strategy, where the preprocessing steps are completed before model training, thus having no impact on the training time. However, compared to other models, MIPS-EfficientNet has a significant advantage in training time. By comparing the training times of different models, it is evident that the training time of MIPS-EfficientNet is the same as that of the original EfficientNet, indicating that the MIPS optimization strategy does not increase the training time while significantly enhancing the model's performance. Specifically, MIPS-EfficientNet shows a marked improvement in test accuracy, recall, precision, and F1 score compared to other models, while maintaining a relatively low training time.  
After the enhancement with MIPS technology, the EfficientNet model not only improved significantly in performance, but in certain cases, its F1 score even surpassed that of some of the ResNet models. This not only validates the effectiveness of the MIPS optimization technique but also highlights the potential of the EfficientNet model when augmented with appropriate pre-processing strategies. Additionally, the ResNet models also exhibited performance improvements following MIPS pre-processing, further verifying the effectiveness of the MIPS pre-enhancement strategy and underscoring the general applicability and practicality of MIPS technology across multiple deep learning frameworks. These findings provide valuable insights for model selection in practical applications, especially when there is a need to balance limited computational resources with high performance, positioning MIPS pre-processing as a key strategy for balancing these demands.