Research Article
An Intelligent Fashion Object Classification Using CNN
@ARTICLE{10.4108/eetinis.v10i4.4315, author={Debabrata Swain and Kaxit Pandya and Jay Sanghvi and Yugandhar Manchala}, title={An Intelligent Fashion Object Classification Using CNN}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={10}, number={4}, publisher={EAI}, journal_a={INIS}, year={2023}, month={11}, keywords={CNN, Lenet, Fashion items, Adam, ReLu, Fashion MNIST}, doi={10.4108/eetinis.v10i4.4315} }
- Debabrata Swain
Kaxit Pandya
Jay Sanghvi
Yugandhar Manchala
Year: 2023
An Intelligent Fashion Object Classification Using CNN
INIS
EAI
DOI: 10.4108/eetinis.v10i4.4315
Abstract
Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.
Copyright © 2023 D. Swain et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.