# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 2 Results of Research
### Description of Selected Convolutional Network Structures
#### 2.1.2 AlexNet  
The AlexNet architecture was designed by [PERSON], [PERSON], and [PERSON] in 2012. It is one of the first deep neural networks that gained wide recognition in the field of computer vision. AlexNet consists of eight layers, which include convolutional layers, max-pooling layers, and fully connected layers [29]. The specific structure shown in Figure 2 is as follows:  
* Five convolutional layers: the first convolutional layer processes the input image of 227 \(\times\) 227 pixels (some sources use 224 \(\times\) 224 pixels), using 96 filters of 11 \(\times\) 11 pixels. The next convolutional layers apply smaller filters, 5 \(\times\) 5 and 3 \(\times\) 3, respectively, with different numbers of filters (256, 384, 384, 256). * Three max-pooling layers: these layers reduce the resolution of the feature maps using max-pooling operations, which reduces the amount of data to be processed and introduces some robustness to image shifts. * Three fully-connected layers: the first two fully-connected layers have 4096 neurons each and function similarly to classical multilayer perceptron layers, and the third fully-connected layer contains 1000 neurons and is used for final classification using the softmax function [29]. Figure 1: LeNet-5 network architecture diagram [28]. The choice of AlexNet for this study is due to its relatively simpler architecture compared to more complex models. AlexNet is a less complex architecture that falls between the simpler LeNet-5 and the more advanced GoogLeNet. AlexNet potentially combines the advantages of the less complex LeNet-5 architecture, such as lower computational requirements and faster training time, with the advantages of the more complex GoogLeNet, such as the ability to process and classify complex patterns in images. Additionally, it requires images of 227 \(\times\) 227 pixels. For the images used in the study, which are in RGB format and have a size of approximately 300 \(\times\) 200 pixels, only minimal rescaling is necessary. This avoids losing important information from the images. **GoogLeNet**  
GoogLeNet is an advanced convolutional neural network architecture developed by Google's research team. This architecture has been successfully applied to many image processing tasks [31]. GoogLeNet consists of 22 layers, which include convolutional layers, reduction layers, and special modules called inception modules. The inception module is a key element of this architecture, which allows for efficient combination of information from different spatial scales [31]. The specific structure is shown in Figure 3.  
* Initial layers: The architecture starts with convolutional layers, which are used to extract features from the input image. Given a 224 \(\times\) 224 \(\times\) 3 input, the following operations are applied:
* \(7\times 7\) convolutional layer with 64 filters, stride 2, ReLU activation function;
* MaxPooling2D layer with 3 \(\times\) 3 filter size and stride 2;
* \(1\times 1\) convolutional layer with 64 filters, ReLU activation function;
* \(3\times 3\) convolutional layer with 192 filters, ReLU activation function;
* MaxPooling2D layer with 3 \(\times\) 3 filter size and stride 2. * Nine inception modules (red boxes): Inception modules are the key element of the architecture, using multiple convolution branches with different filter sizes to efficiently extract features from images. * Two softmax auxiliary layers (green boxes): These layers play a key role in the network training and regularization process. Their presence is intended to speed up the learning process by guiding the network towards the goal and ensuring that the intermediate functions generated by the network are good enough. * Global Average Pooling: Instead of a fully connected layer, global averaging is used, which makes it easier to adjust and improve the network. Figure 2: AlexNet network architecture diagram [30]. Figure 3: GoogleNet network architecture diagram [32].