Name
Abstract | ||
|---|---|---|
The research proposes an approach that can automatically classify real world pictures of some traditional clothing worn in Bangladesh into the predefined classes using Convolutional Neural Networks (CNN). The research is driven by considering the growing market of online shops in mind. For classification purpose, we have collected clothing images from several online stores and labeled them accordingly. Our CNN model is based on the Google Inception model. For comparison purposes we have tried several architectures of CNN and some variations to see how our model perform against them. We have tested our own model with three different optimizers – SGD, Adam and RmsProp. Among these optimizers, RmsProp performed the best. The final result shows our model could classify the images of the training and testing set with 92.05% and 89.22% accuracy respectively. |
Year | Venue | Field |
|---|---|---|
2018 | ICIS | Categorization,Pattern recognition,Convolutional neural network,Computer science,Clothing,Feature extraction,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. M. Tanzim Nawaz | 1 | 0 | 0.34 |
| Rasik Hasan | 2 | 0 | 0.34 |
| Md. Abid Hasan | 3 | 0 | 0.34 |
| Mahadi Hassan | 4 | 0 | 0.34 |
| Rashedur M. Rahman | 5 | 1 | 8.15 |