Week 1 — Waste Classification

Hello everyone, today we want to talk about our machine learning project and ourselves. Pleasant readings.

Recycling Bins Recycle Environment from https://www.needpix.com/

Who are we?

Our project team consists of three people. You can find detailed information about people through the links below.

Hasan Akalp - LinkedIn - GitHub

Umut Piri - LinkedIn - GitHub

Dilara İşeri - LinkedIn - GitHub

What is out project and why is it important?

The main purpose of our project is to classify wastes with high performance. In addition, our aim is to achieve this with high speed.

So why are our goals this way? Today, one of the main problems of countries is waste management and waste classification. In many countries people dispose of their waste without classifying it, so countries need to establish facilities to classify waste. It is important for the economy of the country that these wastes are classified with high performance. Because the more accurate the classification of waste, the more they can contribute to their economy. More importantly, they can leave a greener world for tomorrow. So why do we want to do it fast? According to World Bank data¹, we produce 1.3 billion tons of solid waste annually, which means 1.2 kg of waste per person per day. The World Bank estimates that by 2025 the amount of waste per person per day will be 1.42 kg. Based on these data, we can understand that we produce very fast waste. In order to achieve this speed, waste sorting facilities should also be fast.

Which datasets we will use?

We will use Gary Thung’s TrashNet dataset. This dataset spans six classes: glass, paper, cardboard, plastic, metal, and trash. The dataset consists of 2527 images: 501 glass, 594 paper, 403 cardboard, 482 plastic, 410 metal, 137 trash.
Another dataset is Sashaank Sekar’s Waste Classification dataset. This dataset spans two classes: organic, and inorganic. The dataset has 22564 train samples and 2513 test samples.

Related Works

1. Awe, O., Mengistu, R., & Sreedhar, V. (2017). Final Report: Smart Trash Net: Waste Localization and Classification. CS229 Project Report. URL: https://pdfs.semanticscholar.org/581f/b0f0405c7f0e60610d88ceaceb9af44d8569.pdf
2. Bircanoğlu, C., Atay, M., Beşer, F., Genç, Ö., & Kızrak, M. A. (2018, July). RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks. In 2018 Innovations in Intelligent Systems and Applications (INISTA) (pp. 1–7). IEEE. URL: https://www.researchgate.net/profile/Meltem_Atay/publication/325626219_RecycleNet_Intelligent_Waste_Sorting_Using_Deep_Neural_Networks/links/5b904ad545851540d1cd36a1/RecycleNet-Intelligent-Waste-Sorting-Using-Deep-Neural-Networks.pdf
3. Ozkaya, U., & Seyfi, L. (2019). Fine-Tuning Models Comparisons on Garbage Classification for Recyclability. arXiv preprint arXiv:1908.04393. URL: https://arxiv.org/pdf/1908.04393
4. R. A. Aral, Ş. R. Keskin, M. Kaya and M. Hacıömeroğlu, “Classification of TrashNet Dataset Based on Deep Learning Models,” 2018 IEEE International Conference on Big Data. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8622212&isnumber=8621858
5. Yang, M., & Thung, G. (2016). Classification of trash for recyclability status. CS229 Project Report, 2016. URL: https://pdfs.semanticscholar.org/c908/11082924011c73fea6252f42b01af9076f28.pdf

References

1. World Bank, URBAN DEVELOPMENT SERIES — KNOWLEDGE PAPERS, Chapter 3: Waste Generation. URL: https://siteresources.worldbank.org/INTURBANDEVELOPMENT/Resources/336387-1334852610766/Chap3.pdf