WASTE CLASSIFICATION USING RESNET-152

Abstract

Waste classification is the process of identifying and separating different types of waste materials based on their characteristics, such as composition, size, and shape. Accurate waste classification is important for efficient waste management and resource recovery, as it allows for the proper disposal or reuse of waste materials. One method for automated waste classification is the use of deep learning algorithms, such as the ResNet-152 model. ResNet-152 is a convolutional neural network (CNN) that has been trained on a large dataset of images and is able to classify objects in images with high accuracy. By applying ResNet-152 to images of waste materials, it is possible to classify the materials into different categories, such as paper, plastic, or metal. This can be done in real-time using computer vision techniques and can significantly improve the efficiency and accuracy of waste classification processes. However, the success of waste classification using ResNet-152 or other CNNs depends on the quality and diversity of the training data, as well as the availability of computational resources and infrastructure. Key Words: Computer Vision, Deep Learning, Transfer Learning, ResNet-152, Waste Management.