A machine learning system that classifies different types of waste materials into categories. It's built on TensorFlow and leverages the VGG16 model architecture for image classification.

• Features
• Installation
• Usage

• VGG16 Model Architecture: The project utilizes the power of the pre-trained VGG16 model for image classification.

• 12 Waste Categories: Classify waste into 12 distinct categories including cardboard, metal, paper, and more.

• Image Augmentation: Uses ImageDataGenerator for real-time data augmentation.

• Visual Predictions: Provides a visual representation of predictions using Matplotlib.

1. Clone the repository:

   git clone https://github.com/supriya811106/Garbage-Classification-System.git

2. Navigate to the cloned repository:

   cd Garbage-Classification-System

3. Install the required dependencies:

   pip install -r requirements.txt

1. Running the Notebook:

   • Ensure you have Jupyter Notebook or Jupyter Lab installed. If not, install it:

     pip install jupyterlab

   • Launch Jupyter:

     jupyter lab

   • Navigate to the project notebook and run the cells to either train the model or make predictions.

2. Classifying Waste:

   • Use the waste_prediction function within the notebook to classify waste by providing the path to your image.

The trained model is saved as predictWaste12.h5 and can be loaded using TensorFlow/Keras for further predictions or improvements.