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--- |
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license: mit |
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language: |
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- en |
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- id |
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metrics: |
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- recall |
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- precision |
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- f1 |
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base_model: |
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- Ultralytics/YOLOv8 |
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pipeline_tag: object-detection |
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tags: |
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- waste-classification |
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- sustainability |
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- yolov8 |
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--- |
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# 🗑️ Real Time Waste Classification – YOLOv8 Large (High Resolution) |
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A high-resolution real time waste-classification model built using **YOLOv8-Large**, fine-tuned to detect and categorize **12 types of waste** use camera. |
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This model was developed as a Capstone Mini-Project for the **REA AI Engineering Bootcamp**. |
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--- |
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## 📌 Overview |
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This project provides a custom object-detection model designed to support: |
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* ♻️ **Automated waste-sorting systems** |
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* 📱 **Recycling education applications** |
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* 🌍 **Environmental monitoring tools** |
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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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--- |
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## 🧠 Model Details |
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| Property | Value | |
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| --------------------- | ---------------------- | |
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| **Architecture** | YOLOv8-Large (yolov8l) | |
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| **Task** | Object (Real Time) Detection| |
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| **Input Size** | 1280 × 1280 | |
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| **Number of Classes** | 12 | |
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| **Base Model** | `ultralytics/yolov8l` | |
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| **License** | MIT | |
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--- |
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## 🎯 Classes |
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The model detects 12 waste categories, including: |
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* Clothes |
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* Brown-Glass |
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* Shoes |
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* Plastic |
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* Biological |
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* (and others) |
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--- |
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## 📊 Evaluation Results |
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The model was evaluated on **1,935 validation images**. |
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### **Overall Performance** |
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| Metric | Score | Description | |
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| ---------- | --------- | ------------------------------------ | |
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| **mAP@50** | **0.783** | Strong overall detection performance | |
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--- |
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### **Performance by Class** |
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The model performs extremely well on rigid, well-shaped objects but struggles with amorphous organic materials. |
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| Class | mAP@50 | Status | Insight | |
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| --------------- | ------ | ------------------- | ------------------------------- | |
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| **Clothes** | 0.987 | 🌟 Excellent | Consistent shape and texture | |
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| **Brown-Glass** | 0.905 | ✅ Very Good | Strong geometric patterns | |
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| **Shoes** | 0.847 | ✅ Good | High recall and precision | |
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| **Plastic** | 0.706 | ⚠️ Moderate | Transparency/deformation issues | |
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| **Biological** | 0.580 | ❌ Needs Improvement | Blends into background | |
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--- |
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## 🔍 Deep Dive: Key Insights |
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### **1. Biological Waste Challenge** |
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* **Recall:** 0.445 |
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* **Missed detections:** 741 biological items labeled as background |
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* **Cause:** Organic waste lacks distinct shape or edges, making it harder for YOLO to detect. |
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### **2. False Positives** |
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* **540 biological false positives** on plain backgrounds |
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* Possibly caused by: |
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* Noisy labels |
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* Complex textures that resemble organic material |
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### **3. Background Confusion** |
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* Cardboard items have **159 false positives** due to color similarity with ground surfaces. |
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--- |
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## ⚙️ Training Configuration |
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| Setting | Value | |
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| ----------------- | --------------------------------------------------- | |
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| **Hardware** | NVIDIA A100-SXM4-40GB | |
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| **Training Time** | 6.26 hours | |
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| **Epochs** | 50 | |
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| **Batch Size** | 8 | |
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| **Optimizer** | AdamW (lr = 0.000625) | |
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| **Image Size** | 1280 | |
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| **Augmentations** | Standard YOLOv8 + Mosaic (disabled final 10 epochs) | |
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--- |
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## 💻 Usage |
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Install Ultralytics: |
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```bash |
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pip install ultralytics |
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``` |
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Run inference: |
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```python |
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from ultralytics import YOLO |
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# Load model |
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model = YOLO("path/to/best.pt") |
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# Run inference |
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results = model("path/to/image.jpg") |
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# Display results |
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results[0].show() |
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``` |
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--- |
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## 👨💻 Author |
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**Kendrick** |
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Alumni — REA AI Engineering Bootcamp |
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This README and model card were generated with training logs and evaluation outputs. |
