Real-Time Traffic Classification Using Weights & Biases for Model Monitoring

In today's transportation landscape, accurate sign prediction of traffic which is essential for enhancing safety on road and optimizing management of traffic. This paper presents an innovative solutions based on cloud which integrates YOLOv8, a cutting-edge object detection model to enhance and fine-tune traffic sign detection. The primary goal is to use YOLOv8 to design a cloud-based traffic sign recognition system while optimizing model development, training, and performance tracking by employing wandb's tools. YOLOv8 is perfect for quickly recognizing and locating traffic signs in intricate visual settings because of its real-time object detection feature. Cloud infrastructure is the foundation of the project. Providing scalable computational resources vital for training sophisticated deep learning models like YOLOv8. Collaboration is another critical aspect of this project. Wandb’s collaborative tools enable seamless interaction between researchers and developers, regardless of location, fostering teamwork and continuous improvement. This paper aims to enhance road safety and improve traffic management by demonstrating the transformative power of cloud-based machine learning solutions.