An AICTE Virtual Internship (Cycle 3) project focused on waste classification using Convolutional Neural Networks (CNN).
This project helps identify and categorize waste into different types using image classification techniques, contributing towards environmental sustainability.
The dataset used in this project is available on Kaggle.
You can also download it programmatically using:
import kagglehub # You may need to run: pip install kagglehub
path = kagglehub.dataset_download("techsash/waste-classification-data")
print("Path to dataset files:", path)- Python 🐍
- TensorFlow / Keras
- NumPy, Pandas
- Matplotlib, Seaborn
- Jupyter Notebook
We use a CNN (Convolutional Neural Network) to classify waste images into categories like:
- Organic
- Recyclable
The model is trained on labeled images and evaluated using accuracy, loss, and confusion matrix.
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Clone the Repository
git clone https://github.com/Shrutik1008/WasteClassificationUsingCNN.git cd WasteClassificationUsingCNN -
Install Dependencies
pip install -r requirements.txt
-
Download Dataset Use the Kaggle link above or download via
kagglehub. -
Run the Project Launch the notebook or script to start training:
jupyter notebook WasteClassificationCNN.ipynb
- Improve classification accuracy with deeper architectures.
- Add data augmentation and transfer learning.
- Deploy model with a user interface using Streamlit or Flask.