Abstract: Deep learning has grown tremendously in recent years, having a substantial impact on practically every discipline. Transfer learning allows us to transfer the knowledge of a model that has been formerly trained for a particular task to a new model that is attempting to solve a related but not identical problem. Specific layers of a pre-trained model must be retrained while the others must remain unmodified to adapt it to a new task effectively. There are typical issues in selecting the layers to be enabled for training and layers to be frozen, setting hyper-parameter values, and all these concerns have a substantial effect on training capabilities as well as classification performance. The principal aim of this study is to compare the network performance of the selected pre-trained models based on transfer learning to help the selection of a suitable model for image classifica-tion. To accomplish the goal, we examined the performance of five pre-trained networks, such as SqueezeNet, GoogleNet, ShuffleNet, Darknet-53, and Inception-V3 with different Epochs, Learning Rates, and Mini-Batch Sizes to compare and evaluate the network’s performance using confusion matrix. Based on the experimental findings, Inception-V3 has achieved the highest accuracy of 96.98%, as well as other evaluation metrics, including precision, sensitivity, specificity, and f1-score of 92.63%, 92.46%, 98.12%, and 92.49%, respectively.

Jayapalan Senthil Kumar, Syahid Anuar and Noor Hafizah Hassan, “Transfer Learning based Performance Comparison of the Pre-Trained Deep Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130193

@article{Kumar2022,
title = {Transfer Learning based Performance Comparison of the Pre-Trained Deep Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130193},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130193},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Jayapalan Senthil Kumar and Syahid Anuar and Noor Hafizah Hassan}
}