Deep Learning for Object Detection in Images Development and Evaluation of the YOLOv8 Model Using Ultralytics and Roboflow Libraries

This paper presents a study of the application of the YOLOv8 model for object detection in images using Ultralytics and Roboflow libraries. Deep learning in computer vision is one of the most actively developing areas in modern technologies, allowing to automate the processes of analysis and recognition of objects in images. The YOLO (You Only Look Once) model is one of the most effective and widely used methods for real-time object detection. The study covers the main steps of working with the YOLOv8 model, from installing and configuring the necessary libraries and development environment, to training the model on the provided data and evaluating its performance. We present a detailed analysis of the training process, including selection of optimal hyperparameters, tuning of training parameters and data augmentation.