This thesis investigates the application of YOLO (You Only Look Once) models for object detection tasks, with a particular focus on the quantization of such models to enable efficient deployment on edge devices and resource-constrained hardware platforms. Model quantization plays a critical role in reducing memory footprint and computational cost while aiming to preserve the accuracy and robustness of the original floating-point networks. The work focuses on integrating a complete training and evaluation pipeline, including data pre-processing compliant with widely adopted standards (e.g. YOLO) and the integration of automated tools for ground truth visualization and validation. Various training strategies were explored to enhance model performance, includ- ing hyperparameter tuning, architectural modifications, and data augmentation techniques.