♻️ TrashNet: Smart Waste Classification with Deep Learning#

🧠 What is TrashNet?#

TrashNet is a deep learning-based image classification system that can automatically identify different types of waste, such as:

• 🧻 Paper
• 🥤 Plastic
• 🪞 Glass
• 🥫 Metal
• 📦 Cardboard
• 🚮 General Trash

It uses Convolutional Neural Networks (CNNs) powered by TensorFlow and trained on a labeled image dataset to classify waste into the correct category.

🔧 Technologies Used#

• Python 3
• TensorFlow / Keras
• OpenCV
• Scikit-learn
• NumPy & Matplotlib
• Jupyter Notebook

📦 Dataset#

The dataset is organized into folders for each waste class and contains hundreds of labeled images.
All images are resized to 128x128 pixels and normalized for training.

Structure:

    dataset/
    ├── cardboard/
    ├── glass/
    ├── metal/
    ├── paper/
    ├── plastic/
    └── trash/

🧼 Preprocessing#

Images are:

• Resized to a fixed shape
• Normalized between 0 and 1
• Labeled using folder names
• Split into train and test sets (80/20)
• Saved in a processed_data.pkl file for fast reuse

img = cv2.imread(img_path)
img = cv2.resize(img, (128, 128))
img = img / 255.0

📊 Results (Coming Soon!)#

I’ll publish model performance, confusion matrix, and sample predictions in the next update — stay tuned! 🚀 If you’d like to contribute or suggest improvements, feel free to open a pull request or issue.

📺 Full Video + Code Coming Soon#

I’m working on a full YouTube video tutorial explaining how I built this project step-by-step — from data loading to model evaluation.

Follow me to stay updated!

GitHub Repository#

🌍 Let’s make AI work for the planet.