# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 1 Introduction
The selected techniques include self-supervised learning (SLE) and transfer learning (TLE). The authors provide guidelines for selecting the optimal technique for specific deep learning applications, aiming to solve the problem of data scarcity. According to the authors, the biggest advantage of transfer learning (TL) is the ability to train models based on simulations, which provides a controlled and safe environment for teaching the model to perform specific actions. In turn, the biggest advantage of self-supervised learning (SSL) is saving time on data labeling. The next paper [13] focuses on the comparison of three architectures that are models of convolutional neural networks. The article aimed to show which of the three architectures mentioned in the title will guarantee the best results in terms of handwriting recognition accuracy and will provide the shortest time needed to classify data from the test set. The authors took care of the proper implementation of the models and training it on real data, which gave the work a research character. The last work [14] is devoted to the development of a new Adaptive Hyperparameter Tuning (AHT) algorithm to automate the selection of optimal hyperparameters for convolutional neural network (CNN) models used in medical image classification. The authors focus on optimizing such hyperparameters as learning rate, epoch size, batch size, activation functions, and kernel/filter size. The Adaptive Hyperparameter Tuning algorithm uses the Gaussian process methodology and Parzen tree estimator to evaluate different combinations of hyperparameters and selects the ones that provide the highest model accuracy. The hyperparameter optimization was performed on a medical dataset that was used to validate the classification of malignant and benign tumors. The presented project includes the implementation, which is a number of different approaches to constructing an artificial intelligence model using deep neural networks to recognize selected types of objects in digital images. The research also includes a comparison of the quality indicators of matches of the network created from scratch with the results of object detection using a pre-trained YOLO (You Only Look Once) model. The aim of the work is to analyze the obtained results and draw appropriate conclusions regarding the impact of the selection of a given parameter or approach on the obtained results. The planned experiments also include an evaluation of the performance of various deep neural network architectures to identify optimal approaches to recognizing objects in images. In addition to analyzing the effectiveness of object detection, the project also involves examining the performance of the created models in terms of processing time and system resource consumption, which is crucial for their practical implementation. The genesis of the research presented in this article can be indicated as the lack in the literature, according to the best knowledge of the authors, of a comparison of three neural network models that belong to convolutional neural networks: LeNet-5, AlexNet, and GoogLeNet. Although these models are not the newest, they are still the subject of research [15; 16; 17; 18]. This fact is also the reason for conducting the research presented in this article. In addition, the LeNet-5 model is mainly recommended for handwriting recognition, and in our approach, it was used for animal recognition in digital images. Due to its low complexity, it can also be considered for use in embedded systems with lower computing power than computing clusters. The article is of a typical application nature. The results included in it can be used by a potential reader for other applications related to object recognition in digital images. In our opinion, the literature lacks a comparison of these three convolutional neural network models, and in the currently very rapid development of neural network applications, it can be a very useful source of knowledge.