Analysis of YOLOv8 Model Architecture and Trainable Parameters

Abstract

With the rapid development of artificial intelligence technology, the complexity and sophistication of neural network models are increasing at an unprecedented rate. Convolutional Neural Networks (CNNs), as one of the key technologies in the field of deep learning, have demonstrated excellent performance in tasks such as image recognition and object detection. Given the significant characteristics of CNNs in terms of depth, breadth, and module diversity, this paper focuses on the cutting-edge open-source object detection project YOLOv8 of ultralytics, deeply analyzes the core components and working principles of its network architecture, and elaborates on the functions of key layers (such as convolutional layers, pooling layers, residual connection layers, upsampling layers, connection layers) and the calculation methods of their trainable parameters in combination with the project source code.