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Is your feature request related to a problem? Please describe.
Effective waste management is essential for environmental sustainability, yet waste segregation remains a challenging task, especially when it relies on human sorting. Manual sorting is often inefficient and prone to errors, leading to improper waste disposal. This project addresses this challenge by creating an automated waste classification system using CNNs to accurately categorize waste images. Automating waste classification can improve sorting accuracy, reduce contamination in recycling streams, and facilitate efficient waste management processes.
Describe the solution you'd like
This project implements a waste classification system using Convolutional Neural Networks (CNNs) to categorize waste images into different types. It utilizes deep learning techniques and leverages CNN architectures to analyze images of waste and predict their classification accurately. The primary model is trained on a dataset of labeled waste images, allowing it to distinguish between various waste types, such as recyclables and non-recyclables. The classification process involves image pre-processing, feature extraction using convolutional layers, and a fully connected network to predict the waste category.