EVALUATING THE PERFORMANCE OF NEURAL NETWORKS IN CLASSIFYING THE GENRE OF VIDEO STREAMS.

The purpose of this article is to consider modern video stream classification algorithms and select the optimal option suitable for application in video hosting applications. The study analyzed various neural network architectures, including ResNet18, architectures with skip-connections, as well as architectures with multiple parallel branches, on a specially prepared dataset containing video clips from films of various genres. The main attention was paid to finding a balance between accuracy and the dimensionality of processed data. The research results show that different neural network architectures demonstrate varying effectiveness depending on the size of input images and the specifics of genre classification tasks. The obtained data allow conclusions to be drawn about the potential application of deep learning in video content analysis and the selection of the most suitable methods for classifying film genres, which can contribute to improving the quality of video hosting services. [ABSTRACT FROM AUTHOR]