The localization and identification of advertisements play a pivotal role in content analysis and information retrieval. Acknowledging this significance, this paper focuses on the critical task of model evaluation and selection for robust and efficient advertisement detection. Employing a comprehensive methodology, we assess various deep learning models based on their accuracy, efficiency, and reliability in detecting and localizing advertisements within diverse print media formats. Our study reveals that certain models significantly outperform others in terms of mean Average Precision (mAP) and F1 scores, while also maintaining low inference latencies. These findings have profound implications for the development of more effective and efficient advertisement detection systems in print media. The conclusions drawn from our research provide valuable insights for future advancements in digital advertising and media analytics.