# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
## 2 Methodology
### EfficientNet  
The EfficientNet model [42], developed by the Google AI team, represents an innovative convolutional neural network architecture that has led a new paradigm in scalable network design. The innovation of this model resides in its application of a systematic method of network scaling known as compound scaling. This method diverges from traditional network scaling practices that typically only enhance the depth or width of the network. EfficientNet employs a fixed scaling coefficient to extend the network's depth, width, and the resolution of the input image in a balanced manner. In this research, the base network model EfficientNet-B0 from the EfficientNets series was selected for analysis. EfficientNet-B0 is an efficient convolutional neural network (CNN) designed by utilizing the compound scaling method. This compound scaling method of EfficientNet enables the generation of a range of models, from EfficientNet-B0 to B7, each providing a set of capabilities that achieve a harmonious balance between computational efficiency and predictive accuracy. EfficientNet-B0 has been optimized under predefined resource constraints through an automated neural architecture search (NAS) process, aimed at finding the optimal balance between efficiency and accuracy. The core structure of EfficientNet-B0 is the Mobile Inverted Bottleneck Convolution (MBConv) module, which incorporates the attention mechanism from the Squeeze-and-Excitation Network (SENet [43]). Upon its introduction, SENet achieved the highest accuracy on the ImageNet dataset, highlighting its effectiveness. The MBConv [44] module, also a product of Neural Architecture Search (NAS), bears similarity to the Depthwise Separable Convolution in structure. These innovations underscore the effectiveness of the EfficientNet architecture in creating models that not only push the boundaries of accuracy in image classification tasks but also do so with considerations for efficiency that make them practical for a wide range of computing environments. Within the MBConv module, as shown in Figure 6, a \(1\times 1\) pointwise convolution is first executed to adjust the output channel dimensions according to the expansion ratio. This is followed by a \(k\times k\) depthwise convolution. If the squeeze-and-excitation operation (SE module) is required, it is implemented subsequent to the depthwise convolution. The final part of the module is another \(1\times 1\) pointwise convolution that restores the original channel dimensions. Additionally, the MBConv module integrates dropout connectivity and input skip connections, which have effectively shortened training times and enhanced the overall performance of the model. The architecture of EfficientNet-B0 comprises 16 Mobile Inverted Bottleneck Convolution (MBConv) modules, 2 convolutional layers, 1 global average pooling layer, and 1 classification layer. In its structural diagram, Figure 7 displays these components, where different colors represent different stages within the network to facilitate the distinction and understanding of the functions and organization of each part. Specifically, black layers represent the initial convolutional layer and the first MBConv layer, responsible for the initial feature extraction and down-sampling of the input image. Blue layers denote the MBConv layers with a kernel size of 3 \(\times\) 3, focusing on efficient feature extraction and processing through depth-wise separable convolutions. Red layers indicate the MBConv layers with a kernel size of 5 \(\times\) 5, providing a larger receptive field to capture more complex patterns and features in the image. The use of different colors helps to visually distinguish between the various types of layers and their specific roles within the network architecture. Across multiple standard image recognition benchmarks, EfficientNet has demonstrated significant performance, particularly achieving state-of-the-art accuracy on the ImageNet dataset while maintaining a lower computational complexity and a lower number of parameters.