🗑️ Real Time Waste Classification – YOLOv8 Large (High Resolution)
A high-resolution real time waste-classification model built using YOLOv8-Large, fine-tuned to detect and categorize 12 types of waste use camera.
This model was developed as a Capstone Mini-Project for the REA AI Engineering Bootcamp.
📌 Overview
This project provides a custom object-detection model designed to support:
- ♻️ Automated waste-sorting systems
- 📱 Recycling education applications
- 🌍 Environmental monitoring tools
The model was trained on 1280×1280 high-resolution images to better capture fine-grained details common in trash and recyclables.
🧠 Model Details
| Property | Value |
|---|
| Architecture | YOLOv8-Large (yolov8l) |
| Task | Object (Real Time) Detection |
| Input Size | 1280 × 1280 |
| Number of Classes | 12 |
| Base Model | ultralytics/yolov8l |
| License | MIT |
🎯 Classes
The model detects 12 waste categories, including:
- Clothes
- Brown-Glass
- Shoes
- Plastic
- Biological
- (and others)
📊 Evaluation Results
The model was evaluated on 1,935 validation images.
Overall Performance
| Metric | Score | Description |
|---|
| mAP@50 | 0.783 | Strong overall detection performance |
Performance by Class
The model performs extremely well on rigid, well-shaped objects but struggles with amorphous organic materials.
| Class | mAP@50 | Status | Insight |
|---|
| Clothes | 0.987 | 🌟 Excellent | Consistent shape and texture |
| Brown-Glass | 0.905 | ✅ Very Good | Strong geometric patterns |
| Shoes | 0.847 | ✅ Good | High recall and precision |
| Plastic | 0.706 | ⚠️ Moderate | Transparency/deformation issues |
| Biological | 0.580 | ❌ Needs Improvement | Blends into background |
🔍 Deep Dive: Key Insights
1. Biological Waste Challenge
- Recall: 0.445
- Missed detections: 741 biological items labeled as background
- Cause: Organic waste lacks distinct shape or edges, making it harder for YOLO to detect.
2. False Positives
-
540 biological false positives on plain backgrounds
-
Possibly caused by:
- Noisy labels
- Complex textures that resemble organic material
3. Background Confusion
- Cardboard items have 159 false positives due to color similarity with ground surfaces.
⚙️ Training Configuration
| Setting | Value |
|---|
| Hardware | NVIDIA A100-SXM4-40GB |
| Training Time | 6.26 hours |
| Epochs | 50 |
| Batch Size | 8 |
| Optimizer | AdamW (lr = 0.000625) |
| Image Size | 1280 |
| Augmentations | Standard YOLOv8 + Mosaic (disabled final 10 epochs) |
💻 Usage
Install Ultralytics:
Run inference:
1from ultralytics import YOLO
2
3# Load model
4model = YOLO("path/to/best.pt")
5
6# Run inference
7results = model("path/to/image.jpg")
8
9# Display results
10results[0].show()
👨💻 Author
Kendrick
Alumni — REA AI Engineering Bootcamp
This README and model card were generated with training logs and evaluation outputs.
