Organic and Inorganic Waste Classification

This project aims to train an image classification model that distinguishes between organic and inorganic waste using the DenseNet121 architecture. The model classifies waste into six categories: organic, plastic, glass, metal, cardboard, and paper.

Project Description

The project seeks to automate waste identification using a pre-trained convolutional neural network (DenseNet121) to improve classification accuracy. This system can be integrated into smart recycling platforms or waste management applications, enabling more efficient material separation.

Key Features:

• Classification into Six Categories: The model identifies and classifies waste images into organic, plastic, glass, metal, cardboard, and paper.
• Customized Training: The model is trained with an image dataset split into training and testing sets.
• DenseNet121 Architecture: Utilizes the DenseNet121 architecture, optimized for image classification.
• Automation: Facilitates automatic waste classification, making it useful for recycling and waste management environments.

Benefits

This software provides several key benefits:

• Efficiency: Enhances waste management efficiency by automating the classification process.
• Accuracy: Leverages a state-of-the-art neural network to improve the accuracy of waste type identification.
• Scalability: The model is adaptable and can be fine-tuned to include new waste categories or improve performance with additional data.
• Environmental Impact: By enabling better waste classification, this project can contribute to improved recycling processes and reduced environmental pollution.