• Anas Baig
• Rehan Khan
• Hatim Anandwala
• Kashif Kaularikar

Waste has always been a problem for humans all around the world. To find a way to reduce it and get rid of it various methods are used, these waste cause a suppressing issue of landfills and an increase in toxicity of the soil. Sometimes, these toxic wastes are consumed by animals. Therefore, waste classification is the initial step in the separation and segregation of organic and recyclable. A CNN model is created from scratch that classifies waste into organic and recyclable categories. A dataset of 25077 images is used and all of them having .jpg extension. The training set had 80% of the dataset and the test set has 20% of the dataset. This paper explores the model and performances of the model which gave an accuracy of 90.66% in classifying waste into organic and recyclable.

Convolutional neural network, Image classification, Organic, Recyclable, Waste classification

"Waste Classification with Convolution Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.d52-d55, May-2021, Available :http://www.jetir.org/papers/JETIR2105389.pdf

"Waste Classification with Convolution Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppd52-d55, May-2021, Available at : http://www.jetir.org/papers/JETIR2105389.pdf