Abstract

Sports image classification is a crucial task in computer vision, facilitating applications such as automated sports analytics and event recognition. This study evaluates the performance of three deep learning models—VGG16, ResNet50, and EfficientNetB0—on a sports image classification dataset.The models were trained and tested using a dataset of sports images, and their performance was assessed based on accuracy, precision, recall, and F1-score. Experimental results indicate that Efficient- NetB0 outperformed the other models, achieving the highest accuracy of 96.6%,precision of 97.35%, recall of 96.6%, and an F1-score of 96.52%. These findings suggest that EfficientNetB0is well-suited for sports image classification, offering a balance of high accuracy and computational efficiency. Its superior performance highlights its potential for real-world applications in sports technology, where fast and accurate classification is essential.

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