Fine Tuned of DenseNET121 to Classify NTT Weaving Motifs on Mobile Application

Yohanes Eudes Hugo Maur, Albertus Joko Santoso, - Pranowo


The problem of classifying Woven Fabric Motifs through pattern recognition can be addressed using Convolutional Neural Networks (CNNs). Existing CNN architectures like VGG, ResNet, MobileNet, and DenseNet offer diverse propagation methods. These architectures, trained on datasets like imagenet, have demonstrated competence in solving large-scale classification tasks. The CNN model trained on the ImageNet dataset, hereinafter referred to as the pre-trained model, can be utilized to address the classification issue of NTT woven fabric motifs. This involves retraining the model using a new output layer and dataset, a method known as Transfer Learning. In addition to Transfer Learning, this research employs Fine Tuning, which entails retraining several classification layers. The pre-trained model used in this research is DenseNet121. This model was chosen because it does not require too much storage space and has good classification performance so that it can be embedded in smartphones. The results of this study indicate that of the three pre-trained models tested (DenseNet121, MobileNetV2, and ResNet50V2), the pre-trained Model DenseNet121 is the model that has the highest accuracy and the smallest loss, namely 92.58% accuracy and 29.62% Loss. Tests on mobile devices also show that from 130 test data, this model gets an accuracy of 99.23%. Overall, the classification model of NTT woven fabric motifs embedded in mobile devices can be used as an alternative to help the community or people who want to learn about NTT woven fabric motifs.


Transfer learning; fine tuning; mobile application; NTT woven fabrics

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