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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 12, 2023.
Abstract: Method for hyperparameter tuning of EfficientNetV2-based image classification by deliberately modifying Optuna tuned result is proposed. An example of the proposed method for textile pattern quality evaluation (good or bad textile pattern fluctuation quality classification) is shown. When using the hyperparameters obtained by Optuna without changing them, the accuracy certainly improved. Furthermore, as a result of learning by changing the hyperparameter with the highest degree of importance, the accuracy changed, so it could be said that the degree of importance was certainly high. However, the accuracy also changes when learning is performed by changing the least important hyperparameter, and sometimes the accuracy is improved compared to when learning is performed using the optimal hyperparameter. From this result, it is found that the optimal hyperparameters obtained with Optuna are not necessarily optimal.
Jin Shimazoe, Kohei Arai and Mari Oda, “Method for Hyperparameter Tuning of EfficientNetV2-based Image Classification by Deliberately Modifying Optuna Tuned Result” International Journal of Advanced Computer Science and Applications(IJACSA), 14(12), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141248
@article{Shimazoe2023,
title = {Method for Hyperparameter Tuning of EfficientNetV2-based Image Classification by Deliberately Modifying Optuna Tuned Result},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141248},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141248},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {12},
author = {Jin Shimazoe and Kohei Arai and Mari Oda}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.