Overall, all optimized models are faster but less accurate than the baseline and original implementations. Furthermore, the original implementations outperform all other models in top-1 accuracy, but have lower top-10 accuracy and percentage of unique predictions. Additionally, the original implementations have higher carbon footprints than the newly trained models. Finally, we additionally investigated the influence of the invalid percentage and the unique percentage on top-n accuracies and observed minimal differences between top-n accuracy with or without valid and uniquely valid predictions (Table ).