The project involves the development of a Convolutional Neural Network (CNN) model to classify images of plastic waste into different categories. The aim is to build a robust and accurate model that can assist in identifying and categorizing plastic waste, thereby contributing to more efficient recycling and waste management practices.
Develop a CNN model capable of distinguishing between various categories of plastic waste.
Achieve high accuracy and performance on the classification tasks.
Regularly update and improve the model based on weekly progress and new data.
Dataset: Utilize a comprehensive dataset of labeled plastic waste images, covering various categories such as PET bottles, HDPE containers, PVC products, LDPE bags, PP items, and others.
Model Architecture: Implement a CNN architecture optimized for image classification tasks, incorporating layers such as convolutional, pooling, dropout, and fully connected layers.
Training and Evaluation: Train the model using a suitable dataset split (training, validation, and test sets) and evaluate its performance using metrics like accuracy, precision, recall, and F1-score.
Data Augmentation: Apply data augmentation techniques to enhance the dataset's diversity and improve the model's generalization capabilities.
Hyperparameter Tuning: Experiment with different hyperparameters to achieve optimal model performance.
Python: The primary programming language for developing the CNN model.
TensorFlow/Keras: Deep learning frameworks for building and training the CNN.
OpenCV: Library for image processing and augmentation.
NumPy: Library for numerical computations.
Pandas: Library for data manipulation and analysis.
Matplotlib: Visualization library for plotting training progress and results.
tqdm: Library for displaying progress bars during model training.
A trained CNN model that can accurately classify images of plastic waste.
Improved waste management and recycling processes through automated plastic waste categorization.
Contributions to environmental sustainability by promoting efficient waste sorting and recycling.
https://www.kaggle.com/datasets/techsash/waste-classification-data