CNN for Computer Vision tasks

Abstract

Object is a neural networks in the field of computer vision and data analysis. The article explores the fundamental principles and aspects underlying the functioning of neural networks. Among the identified limitations are the complexity of tuning hyperparameters and computational costs associated with increasing the network's depth and the size of training data. The aim of the study is to analyze contemporary solutions related to convolutional neural networks (CNN) to select an optimal topology that enhances addressing typical computer vision tasks. The article covers essential methods such as convolutional, pooling, fully connected, and inception layers. Different types of convolutional operations (expanded, partial, strided) and various CNN models (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet) are also examined. After reaching the goal or objectives, it is suggested to employ various methods such as dimensionality reduction, batch normalization, data augmentation, utilization of advanced optimization methods, diverse activation functions, as well as combinations of these approaches. Ref. 10, pic. 4, tabl. 1

Keywords

• artificial intelligence
• computer vision
• classification
• deep learning
• neural networks
• convolutional neural networks