Waste Detection & Classification – YOLOv8

This model detects and classifies different types of waste to support smart waste segregation, recycling automation, and sustainability projects.
It was trained using Ultralytics YOLOv8 on a custom waste dataset with ~250 images per class.

🔎 Detected Classes

Cardboard
E-waste
Glass
Medical waste
Metal
Organic waste
Paper
Plastic

🧠 Model Details

Architecture: YOLOv8
Framework: Ultralytics (PyTorch)
Task: Object Detection
Input: RGB images
Output: Bounding boxes + class labels
Training Data: ~250 images per class
Total Classes: 8

📊 Evaluation (Validation Set – Normalized Confusion Matrix Insights)

Organic: ~96% recall
Metal: ~81% recall
Paper: ~83% recall
Plastic: ~63% recall
Medical waste: ~54% recall
Cardboard: ~76% recall
E-waste: ~75% recall
Glass: ~60% recall (approx)

The model performs strongly on organic, metal, and paper categories.
Performance on medical waste and plastic can be improved with more diverse training samples.

📈 Training Curves & Confusion Matrix

You can find:

Precision-Recall curves
F1 curve
Confusion matrix
Training visualizations

inside the repository files.

🚀 How to Use

Install Ultralytics

pip install ultralytics

Run Inference

from ultralytics import YOLO

model = YOLO("best.pt")  # path to the downloaded weights
results = model("test.jpg", conf=0.25)
results[0].show()

Batch Inference

results = model("path/to/images/", save=True)

🧪 Example Use Cases

Smart waste segregation systems
Recycling automation
Smart bins
Campus or city-level waste monitoring
Sustainability & eco-tech projects

⚠️ Limitations

Performance may drop on blurry or low-light images
Medical and plastic waste classes may need more data for higher accuracy
Not suitable for safety-critical decisions without human verification

📄 License

MIT License

👤 Author

Hrutik Adsare If you use this model, consider giving it a ⭐

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