This comprehensive tutorial explores the complexities of comparing object detection models in computer vision, emphasizing the use of Comet, an experiment tracking tool, to manage and evaluate various models. It highlights the distinctions between single-stage and two-stage detection algorithms, noting that single-stage models like YOLO and RetinaNet are faster but less accurate compared to two-stage models such as Fast RCNN and Mask RCNN. The article underscores the importance of transfer learning and fine-tuning pre-trained models to save time and resources, as training from scratch is computationally demanding. Precision, recall, Mean Average Precision (mAP), and Mean Average Recall (mAR) are discussed as crucial metrics for evaluating model performance, with an emphasis on the trade-offs between accuracy and computational efficiency. The tutorial utilizes the Penn-Fudan dataset to demonstrate model evaluation and comparison, and stresses the necessity of tracking hyperparameters, system metrics, and visualizing predictions to fully understand model behavior. Through Comet's capabilities, the article provides a detailed guide on organizing and visualizing experimental data to determine the best model for specific use cases, highlighting the subjectivity of what constitutes the "best" model based on the application context.