The Performances of Pre-trained Convolutional Neural Networks in Clothing Sketch Classification

Sketch is a unique source of information. Unlike an image (photograph), a sketch contains mostly edges and fewer textures; thus, it has fewer visual cues. Besides, it contains inherent intra-class variations - the geometric and shape variations between sketches of the same object - which lead to a more challenging recognition/classification (R/C) task. The sketch R/C is required in many areas; however, the explorations in this direction - particularly those using deep neural networks - are still limited. This work, thus, aims to present and compare the performances of four pre-trained convolutional neural network (CNN) architectures, namely ResNet101, DenseNet, MobileNetv2, and ShuffleNetv2, in classifying clothing sketches that have intra-class variations and fewer visual cues. We measured the training accuracy, training time, validation accuracy as well as testing accuracy. The simulation results showed that the CNN models had good performances in classifying clothing sketches, with the overall testing accuracy being over 94%. All of the architectures set at epoch=20 resulted in the highest accuracy.