This project utilizes the VGG16 convolutional neural network architecture to classify waste into two categories: recycle waste and organic waste. The aim is to provide an automated solution for waste sorting, which can contribute to efficient recycling processes and environmental sustainability.

• Shivam Bhosle
  
• Karan Lashkari
  
• Sumeet Dube
  
• Pritam Mohanty
  
• Shivam Dave
  

• Introduction
• Dataset
• Model Architecture
• Training
• Evaluation
• Results
• License

Waste management is a critical issue globally, and effective waste sorting is key to maximizing recycling efforts and reducing environmental impact. This project addresses the challenge of waste classification using deep learning techniques.

The dataset used for training and evaluation consists of images of various types of waste, including recyclable and organic waste. It is collected Kaggle, click here. The dataset is divided into training and testing to ensure the robustness of the model.

The VGG16 architecture is employed as the backbone for this waste classification task. VGG16 is a deep convolutional neural network known for its simplicity and effectiveness in image classification tasks. By fine-tuning the pre-trained VGG16 model on our waste dataset, we aim to leverage its feature extraction capabilities to classify waste accurately.

The model is trained using the labeled dataset mentioned earlier. During training, data augmentation techniques such as rotation, flipping, and scaling are applied to enhance the model's generalization ability. The training process involves optimizing the model parameters using a suitable optimization algorithm such as stochastic gradient descent (SGD) or Adam.

The performance of the trained model is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Additionally, confusion matrices and ROC curves are generated to assess the model's performance comprehensively. The evaluation results provide insights into the model's strengths and areas for improvement.

The trained model achieves promising results in waste classification, with high accuracy and robustness across different types of waste. The model's performance can be further improved through fine-tuning and optimization techniques.

This project is licensed under the MIT License, which permits unrestricted use, distribution, and modification, subject to the terms of the license agreement.