# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 3 Experiments and Discussion
### Accuracy across Different Categories
Therefore, when selecting a model for practical application, based on these indicators, MIPS-EfficientNet surpasses the original EfficientNet model in reducing misjudgments, enhancing correct identification rates, and maintaining a good balance between these aspects. In the process of delving deeper into the aforementioned phenomena, this study further employed t-distributed Stochastic Neighbor Embedding (t-SNE [45]) to visualize the output of the model's final convolutional layer. As depicted in Figure 9, the impact of different loss functions on the distribution of the feature space is clearly observable. Figure 8: A comparison of confusion matrices. The horizontal axis represents the predicted classification labels, while the vertical axis corresponds to the actual classification labels. (**a**) presents the classification results of fish feeding behavior images obtained using the EfficientNet network model, visualized in the form of a confusion matrix. (**b**) displays the classification results for fish feeding behavior images obtained using the MIPS-EfficientNet network model, also visualized through a confusion matrix. Figure 9 provides a t-SNE comparative visualization of feature vectors extracted by the EfficientNet and MIPS-EfficientNet models. In these representations, samples from various categories are encoded in different colors to denote their class: none (yellow), weak (blue), medium (green), and strong (red). This color-coding facilitates the observation of the models' capabilities to cluster similar features within a three-dimensional feature space. Notably, MIPS-EfficientNet demonstrates a more distinct clustering of points compared to EfficientNet, indicating enhanced capability in distinguishing between categories. Within the feature space, the distances between categories, represented by unique colors, are pronounced, indicating the model's effective discriminative ability to differentiate between sample categories. The spatial separation between categories, particularly the discernible division between the strong and medium classes, signifies the superior discriminative power of the MIPS-EfficientNet model. Such a clear demarcation of classes is crucial for precise classification tasks, especially in datasets characterized by complexity or a high degree of overlap. Furthermore, it can be inferred that the MIPS-EfficientNet model places greater emphasis on the internal consistency of categories and the distinctions between them during the learning process. This is evidenced by the cohesiveness of the category clusters and their separation. Similarly, it can be anticipated that this attribute may lead to a lower classification error rate, as the model has understood the differences between categories on a more granular level. Taking these observations into account, it can be posited that the MIPS-EfficientNet model may surpass traditional EfficientNet models in specific applications such as the recognition of fish behaviors. The t-SNE visualization in Figure 9 illustrates the potential of MIPS-EfficientNet in handling complex datasets for refined classification tasks, and its efficiency in feature extraction warrants further exploration and application.