การจำแนกหมวดหมู่สินค้าบนระบบ อีคอมเมิร์ซ โดยใช้เทคนิคการเรียนรู้เชิงลึก วิเคราะห์ภาพสินค้า

Authors

นายcoauthor coauthor, ผศ.ดร.ฐิติรัตน์ ศิริบวรรัตนกุล

Published

วารสารวิจัยและพัฒนา มจธ.

Abstract

This research aimed to create a deep learning model to classify
categories of products that are being sold on an e-commerce platform
by analyzing images of the products. Different techniques were
comparatively assessed and their efficiencies were compared to arrive
at the best model. The public image dataset of the Shopee e-commerce
system, consisting of 38 product categories, with a total of 106,309
images, was used. As these images belong to actual product images in
the Shopee e-commerce system, they are highly complex in terms of
computer vision and image analytics. Six models were tested along
with 2 different loss functions. The results revealed that EfficientNetB5
was the best modelin terms of accuracy, considering the size of the
model and the time used in classifying the images; the accuracy on
the test set was noted to be 84% while the value was 92% on the
additional test set. The inference time used was 0.068 seconds per
image with the model size of 141.9 MB. When the model was tested
for the task of image classification in comparison with the actual Shopee
application, the EfficientNetB5 model was 31.5% more accurate. When
compared to four human participants, although our model was 7.16%
less accurate, the model was 25.5 times faster in classifying each image.

(2566). การจำแนกหมวดหมู่สินค้าบนระบบอีคอมเมิร์ซโดยใช้เทคนิคการเรียนรู้เชิงลึก วิเคราะห์ภาพสินค้า. วารสารวิจัยและพัฒนา มจธ., 2023(2), na-na.