The paper presents YOLOv4, an advanced object detection model that aims to achieve both high speed and accuracy. The authors focus on improving the real-time performance of object detectors, making them suitable for various applications beyond recommendation systems. They introduce several new features, including Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), and Mish activation. These features are combined to achieve state-of-the-art results on the MS COCO dataset, with an accuracy of 43.5% AP (65.7% AP50) and a real-time speed of approximately 65 FPS on a Tesla V100 GPU. The paper also discusses the selection of architectural components, such as the backbone network (CSPDarknet53), neck (SPP and PAN), and head (YOLOv3). They explore different training strategies, including data augmentation techniques like Mosaic and Self-Adversarial Training, and regularization methods like DropBlock. The authors conduct extensive experiments to evaluate the impact of these features on both classification and detection tasks, demonstrating that YOLOv4 outperforms other state-of-the-art detectors in terms of speed and accuracy. The results show that YOLOv4 is efficient enough to be trained and used on conventional GPUs, making it accessible to a broader audience. The paper concludes by highlighting the contributions of YOLOv4 and its potential for future research and applications.The paper presents YOLOv4, an advanced object detection model that aims to achieve both high speed and accuracy. The authors focus on improving the real-time performance of object detectors, making them suitable for various applications beyond recommendation systems. They introduce several new features, including Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), and Mish activation. These features are combined to achieve state-of-the-art results on the MS COCO dataset, with an accuracy of 43.5% AP (65.7% AP50) and a real-time speed of approximately 65 FPS on a Tesla V100 GPU. The paper also discusses the selection of architectural components, such as the backbone network (CSPDarknet53), neck (SPP and PAN), and head (YOLOv3). They explore different training strategies, including data augmentation techniques like Mosaic and Self-Adversarial Training, and regularization methods like DropBlock. The authors conduct extensive experiments to evaluate the impact of these features on both classification and detection tasks, demonstrating that YOLOv4 outperforms other state-of-the-art detectors in terms of speed and accuracy. The results show that YOLOv4 is efficient enough to be trained and used on conventional GPUs, making it accessible to a broader audience. The paper concludes by highlighting the contributions of YOLOv4 and its potential for future research and applications.