YOLOv4 - Ten Tactics to Build a Better Model

The blog post by Jacob Solawetz, published on November 13, 2020, provides a comprehensive guide on implementing ten advanced tactics to enhance the YOLO v4 model for custom object detection tasks. It emphasizes the importance of gathering more representative data and employing image preprocessing and augmentation to improve model accuracy. The post also discusses the significance of choosing the right input resolution size, leveraging pretrained weights, and selecting the appropriate model size and architecture, such as YOLOv4 or YOLOv4-tiny, based on specific needs like inference speed. It underlines the necessity of saving model progress to resume training efficiently, selecting the best model post-training, and keeping track of model evaluations using metrics like mean average precision. Additionally, it covers exporting trained models for deployment in various formats and optimizing inference times through model size selection and hardware choices. The post aims to equip readers with strategies to maximize the performance of YOLO v4 in custom object detection scenarios.