Performance Evaluation of a Multi-Class Classification Model for Marine Objects

For a variety of applications, including environmental monitoring, biodiversity studies, and historical happenings, it is essential to accurately describe maritime items. In this study, a multi-class classification model that aims to discover 10 classes is evaluated. The classification report for a multi-class classification model created to identify distinct marine objects is presented in this research study. Precision, recall, and F1-score measures were used to assess the model’s performance for a variety of classes, including Clams, Coral Reefs, Divers, Fish, Nudibranchs, Sea Rays, Sea Urchins, Submarines, Torpedoes, and Wrecks. The model’s total accuracy was found to be 95%. The findings demonstrate the model’s variable performance across classes, with some getting excellent precision and recall scores (such as torpedoes and submarines) and others still needing work (such as clams and coral reefs). These results help us understand the model’s capabilities and shed light on potential areas for future growth and improvement. The study has significance for marine item recognition, allowing breakthroughs in environmental monitoring, marine research, and related sectors.