README.md

---
license: mit
language:
- en
- id
metrics:
- recall
- precision
- f1
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
tags:
- waste-classification
- sustainability
- yolov8
---
# 🗑️ 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:

```bash
pip install ultralytics
```

Run inference:

```python
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()
```

---

## 👨‍💻 Author

**Kendrick**
Alumni — REA AI Engineering Bootcamp

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