Abstract:
The issue of finding the “right clothing size” is always a struggle for online shoppers. For
this reason, there is a digital gulf between users and e-commerce platforms during buying
scenarios. Though the emergence of e-commerce has revolutionized the fashion industry,
the issue of clothing fit consistently drives significant market back into traditional shops.
With the growing demands of consumers for products tailored to their individual needs and
style, there is an increase in demand for custom fitted and custom designed garments. There
is also a need for local designers to commercialize their craft in custom-fitted clothing
without the bridge of receiving inaccurate measurements. For this gap, there is a need for a
cost-effective non-contact body measurement system. In this capstone project report, we
explore the use of Convolutional Neural Networks and LSTM model to extract seventeen
body measurements from 2d images automatically (front, back, and side). After training on
10 and 50 epochs, the validation loss decreases with each iteration. This means, there is
more room for the model to learn. Limitations in dataset size caused a significant impact in
the general accuracy of the model. However, in future work, this issue would be solved to
ensure higher efficiency.
Description:
Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020