Leveraging VGG16 for Fish Classification in a Large-Scale Dataset

Abstract

When the VGG16 model was applied to fish picture classification, the overall accuracy was a remarkable 99%, demonstrating strong performance over most of the dataset. Still, a thorough assessment of the model's efficacy necessitates a look beyond its general accuracy. A more detailed evaluation is possible thanks to class-specific metrics like precision, recall, and F1-score, which provide information on how well the model performs on particular classes. Although the high overall accuracy is encouraging, more research into these metrics and the possibility of class imbalances should be taken into account to guarantee consistent performance in the fish image classification challenge across all categories. A more comprehensive assessment of the model's effectiveness benefits from a contextual knowledge of the application domain and a careful examination of evaluation measures.