Abstract—In 2019, Shanghai has published a new regulation
of trash management, which obtains a series of achievements in
managing trash. In order to implement this regulation further
and help citizens understand clearer about trash classification,
we decided to develop a deep learning dataset and models to
classify waste automatically. The object of this study is to take
an image as input and identify the category of trash. We write
a web crawler to capture images. After pre-processing, we gain
about 14,000 images in total. We compare models including
CNN, a ResNet50 model, and a VGG16 model on the dataset.
Our experiments show that the ResNet50 model perform better
than the others.
Index Terms—Dataset, garbage classification, web crawler, convolutional neural network, transfer learning.
The author is with the School of Information Science and Technology, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor Darul Ehsan, Malaysia (e-mail: firstname.lastname@example.org).
Cite: Yunyi Liao, "A Web-Based Dataset for Garbage Classification Based on Shanghai’s Rule," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 599-604, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).