Classification of Waste using CNN

Prediction Models/Classification of Waste using CNN/Readme.md

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+# Waste Classification with Convolutional Neural Networks (CNN)
+
+This project applies a Convolutional Neural Network (CNN) model to classify waste images into various categories to facilitate efficient waste management. The system leverages deep learning to identify and categorize images of waste, helping improve sorting accuracy in waste management processes.
+
+## Table of Contents
+- [Project Overview](#project-overview)
+- [Problem Statement](#problem-statement)
+- [Technical Approach](#technical-approach)
+- [Dataset](#dataset)
+- [Requirements](#requirements)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Model Performance](#model-performance)
+- [Contributing](#contributing)
+
+
+## Project Overview
+This project provides an automated solution to categorize waste types using CNNs. By training on a dataset of labeled waste images, the model can recognize different waste categories, such as recyclables and non-recyclables. This helps in minimizing sorting errors and improving the overall efficiency of waste management.
+
+## Problem Statement
+Manual sorting of waste can be inefficient and often leads to sorting errors, which compromise the recycling process. This project aims to address this issue by automating waste classification through machine learning. With accurate categorization, waste can be managed more effectively, reducing contamination in recycling streams and improving resource efficiency.
+
+## Technical Approach
+The project employs a Convolutional Neural Network (CNN) architecture designed to classify waste images. The CNN model includes:
+- **Image Pre-processing**: Standardizing waste images for input to the model.
+- **Convolutional Layers**: Extracting essential features from the images.
+- **Fully Connected Layers**: Aggregating features for final classification.
+
+The model is trained on a labeled dataset and validated for accuracy to ensure reliable performance.
+
+## Dataset
+The dataset used for training includes images of various waste categories, labeled for supervised learning. Each image is categorized based on its type to provide a foundation for model learning.
+
+## Requirements
+- Python 3.7+
+- TensorFlow
+- Keras
+- NumPy
+- OpenCV
+- Matplotlib
+
+## Installation
+1. Clone this repository:
+    ```bash
+    git clone https://github.com/yourusername/waste-classification-with-cnn.git
+    ```
+2. Install dependencies:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Usage
+1. To train the model:
+    ```bash
+    python train.py
+    ```
+2. To classify an image:
+    ```bash
+    python classify.py --image <path_to_image>
+    ```
+
+## Model Performance
+The model has been trained and evaluated on the dataset, achieving a classification accuracy of XX% on the test data.
+
+## Contributing
+Contributions are welcome! Please open an issue or submit a pull request.