Abstract
In recent years, the use of neural networks has expanded significantly through the development of models such as Generative Pre-trained Transformer (GPT) and various variations of convolutional neural networks (CNN) for various machine vision tasks. One of the key tasks in this area is the detection of objects in images. This article presents a comparative analysis of GPT-based models, pre-trained models and created artificial neural networks in the context of object detection. Object detection is a key task in computer vision, and applications cover various fields such as autonomous driving, surveillance, and medical imaging. The study begins by outlining the basics of object detection and the importance of choosing the right model for effective implementation. The advantages of their extensive pre-training are juxtaposed with the challenges associated with high computing requirements and limited customization.