Visible to the public Fashion Images Classification using Machine Learning, Deep Learning and Transfer Learning Models

TitleFashion Images Classification using Machine Learning, Deep Learning and Transfer Learning Models
Publication TypeConference Paper
Year of Publication2022
AuthorsSamia, Bougareche, Soraya, Zehani, Malika, Mimi
Conference Name2022 7th International Conference on Image and Signal Processing and their Applications (ISPA)
Date Publishedmay
KeywordsClothing, composability, Deep Learning, deep learning (dl), Fashion classification, image classification, machine learning, machine learning (ML), machine learning algorithms, privacy, pubcrawl, resilience, Resiliency, Signal processing, Signal processing algorithms, Support vector machines, transfer learning, transfert learning
AbstractFashion is the way we present ourselves which mainly focuses on vision, has attracted great interest from computer vision researchers. It is generally used to search fashion products in online shopping malls to know the descriptive information of the product. The main objectives of our paper is to use deep learning (DL) and machine learning (ML) methods to correctly identify and categorize clothing images. In this work, we used ML algorithms (support vector machines (SVM), K-Nearest Neirghbors (KNN), Decision tree (DT), Random Forest (RF)), DL algorithms (Convolutionnal Neurals Network (CNN), AlexNet, GoogleNet, LeNet, LeNet5) and the transfer learning using a pretrained models (VGG16, MobileNet and RestNet50). We trained and tested our models online using google colaboratory with Tensorflow/Keras and Scikit-Learn libraries that support deep learning and machine learning in Python. The main metric used in our study to evaluate the performance of ML and DL algorithms is the accuracy and matrix confusion. The best result for the ML models is obtained with the use of ANN (88.71%) and for the DL models is obtained for the GoogleNet architecture (93.75%). The results obtained showed that the number of epochs and the depth of the network have an effect in obtaining the best results.
DOI10.1109/ISPA54004.2022.9786364
Citation Keysamia_fashion_2022