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
bash
1from ultralytics import YOLO
23model = YOLO("best.pt")# path to the downloaded weights4results = model("test.jpg", conf=0.25)5results[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 ⭐
