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Waste Classification using Deep Learning Convolution Neural Nets

Authors: null Divy Mohan Rai and Ms. ShikhaGupta;

Waste Classification using Deep Learning Convolution Neural Nets

Abstract

The generation of waste India is becoming a great concern, and it has affected our environment and may even affect the life of people living near these dump sites. The recent study figures show that India generates nearly 26,000 MT of plastic waste on a daily basis and 94 lakh tonnes trash every year. In 2017-18, the Capital generated 6,800 tonnes of municipal waste daily, out of which 690 tonnes of trash was plastic - the highest in the country.This project focuses on building a Deep learning classifier to classify a given waste product into one of the recycling categories such as plastic, glass so that it is easier for people to recycle waste and less waste reaches the wrong place like illegal dumpsites which can lead to not only health issues but also cause a big damage to our surrounding environment.The model is based on the InceptionV3 architecture and uses the concept of transfer learning for training. The system has been tested on the dataset collected from various resources ranging from the TACO dataset to the TrashNet dataset the overall accuracy of the model was around 94.6%.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
gold