The CreateML-based compared to the AutoML-based approach to animal image classification

Tianyun Wang; Yanzhang Zhu

doi:10.1117/12.2640908

13 October 2022 The CreateML-based compared to the AutoML-based approach to animal image classification

Tianyun Wang, Yanzhang Zhu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871M (2022) https://doi.org/10.1117/12.2640908
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Many platforms based on auto machine learning approaches have emerged as a result of the rapid development of machine learning. However, the performance differences between platforms have rarely been investigated. In this study, Google Cloud AutoML and Apple CreateML platforms are compared to find the difference in terms of the performance. To be more specific, we trained the model with the same dataset from Kaggle Animal 10. Three experiments are conducted between Google Cloud AutoML and Apple CreateML to achieve this goal. Finally, we arrived at the conclusion by examining the training accuracy, validation accuracy, and average precision across these two platforms. According to each pair of comparative tests, Google Cloud AutoML appears to perform better in every experiment when compared to Apple CreateML.

Citation Download Citation

Tianyun Wang and Yanzhang Zhu "The CreateML-based compared to the AutoML-based approach to animal image classification", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871M (13 October 2022); https://doi.org/10.1117/12.2640908

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