🚀 Waste Classification using CNN with Transfer Learning

📌 Overview.

This project implements a CNN-based waste classification model using Transfer Learning with the VGG16 architecture. The model classifies waste into two categories: Organic and Recyclable, leveraging a two-phase training strategy (freeze-unfreeze).

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🌟 Key Features.

• ✅ Two-phase training strategy: Freeze VGG16 layers initially, followed by fine-tuning the last few layers.
• 📈 High Performance: Achieves up to 98% accuracy on test data.
• 🎨 Extensive Data Augmentation: Various augmentation techniques applied to improve model robustness.
• 🔮 Future Potential: Plans to expand functionality, improve predictions, and cover more waste categories.

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📁 Dataset.

The Waste Classification dataset was sourced from Kaggle. Key details include:

• 🗂 Classes: Organic and Recyclable waste.
• 📦 Structure: Pre-defined training and testing splits.
• 🖼 Format: RGB images with dimensions of 224x224 pixels.

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🛠️ Requirements.

Ensure the following libraries and frameworks are installed:

tensorflow keras numpy pandas opencv-python matplotlib tqdm kagglehub

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🚀 Usage.

1️⃣ Clone the Repository

git clone https://github.com/Raviteja5469/CNN-model-for-Waste-Classification.git

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Training Script

python train.py

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📊 Model Performance.

Metric	Accuracy
Training Accuracy	~95%
Validation Accuracy	~92%
Test Accuracy	~90%

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🔍 Training Strategy.

1. Initial Training: Freeze VGG16 layers.
2. Fine-tuning: Unfreeze and train the last 5 VGG16 layers.
3. Learning Rate Adjustment: Reduce on plateau.
4. Data Augmentation: Apply transformations for robustness.

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🔮 Future Improvements.

• 🔄 Implement additional data augmentation techniques.
• 📚 Experiment with other pre-trained models.
• 🖥 Add real-time prediction capabilities.
• 🏷 Expand to more waste categories.

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👥 Contributing.

Contributions are welcome! Feel free to fork the repository, make changes, and submit a Pull Request.

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📞 Contact

Platform	Link
Author	Seguri Raviteja
E-mail	ravitejaseguri@gmail.com
GitHub	Raviteja5469
LinkedIn	Seguri Raviteja

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🙌 Acknowledgments

• 🏗 VGG16 Pre-trained Model
• 📊 Waste Classification Dataset Creators
• 🛠 TensorFlow and Keras Teams