kendrickfff/YOLOv8GarbageClassification

🗑️ 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.

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📌 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.

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🧠 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

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🎯 Classes

The model detects 12 waste categories, including:

• Clothes
• Brown-Glass
• Shoes
• Plastic
• Biological
• (and others)

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📊 Evaluation Results

The model was evaluated on 1,935 validation images.

Overall Performance

Metric	Score	Description
mAP@50	0.783	Strong overall detection performance

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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

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🔍 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.

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⚙️ 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)

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💻 Usage

Install Ultralytics:

pip install ultralytics

Run inference:

from ultralytics import YOLO

# Load model
model = YOLO("path/to/best.pt")

# Run inference
results = model("path/to/image.jpg")

# Display results
results[0].show()

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👨‍💻 Author

Kendrick Alumni — REA AI Engineering Bootcamp

This README and model card were generated with training logs and evaluation outputs.