Comparison of Different Object Detection and Classification Models

Object detection has become an important task in the field of computer vision, where the goal is to recognize and classify objects in images or videos. This paper presents a comparative analysis of different object detection models, focusing on convolutional neural networks (CNN) and transformer-based architectures. CNN-based models (e.g., the YOLO family) have made significant progress in real-time object detection by efficiently extracting local features via convolutional operations. In contrast, transformer-based models, such as the Visual Transformer (ViT), use self-attention mechanisms to capture global dependencies, improving performance on large-scale datasets. This research explores the evolution of these models and examines their foundations, strengths, and weaknesses. Through experimental evaluations, we show that CNNs continue to dominate when data and computational power are limited, while Transformers exhibit superior scalability and accuracy in complex environments. Our results highlight the complementary nature of these approaches and emphasize the need for hybrid models to achieve optimal performance in different object detection tasks.