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| <title>Garbage Classification Model — CS549</title> |
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| <h1>🗑️ Garbage Classification Model — CS549</h1> |
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| <p>A Convolutional Neural Network trained to classify garbage images into 7 categories:</p> |
| <ul> |
| <li>Cardboard</li> |
| <li>Glass</li> |
| <li>Metal</li> |
| <li>Paper</li> |
| <li>Plastic</li> |
| <li>Trash</li> |
| <li>Biodegradable</li> |
| </ul> |
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| <h2>🔍 Use Case</h2> |
| <p>Helps automate waste sorting for better recycling. Great for demos, PoC apps, or smart bin integration.</p> |
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| <h2>🧠 Model Overview</h2> |
| <ul> |
| <li><strong>Architecture:</strong> CNN (Conv2D, MaxPooling, Dense)</li> |
| <li><strong>Input:</strong> 224x224 RGB</li> |
| <li><strong>Output:</strong> 7-class probability vector</li> |
| <li><strong>Accuracy:</strong> ~92% (validation)</li> |
| <li><strong>Explainability:</strong> Supports Grad-CAM</li> |
| </ul> |
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| <h2>📦 How to Use</h2> |
| <pre><code>from tensorflow.keras.models import load_model |
| from PIL import Image |
| import numpy as np |
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| model = load_model("GarbageMLModel_CS549.h5") |
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| img = Image.open("example.jpg").resize((224, 224)) |
| img_array = np.expand_dims(np.array(img) / 255.0, axis=0) |
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| pred = model.predict(img_array) |
| classes = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash', 'biodegradable'] |
| print("Prediction:", classes[np.argmax(pred)])</code></pre> |
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| <h2>📁 Files</h2> |
| <ul> |
| <li><code>GarbageMLModel_CS549.h5</code> – Trained model</li> |
| <li><code>label_map.json</code> – Label mapping</li> |
| </ul> |
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| <h2>👤 Author</h2> |
| <p>Vincent Huynh<br> |
| 📧 <a href="mailto:vintendohuynh@gmail.com">vintendohuynh@gmail.com</a><br> |
| 🔗 <a href="https://www.linkedin.com/in/vhuynh19/">LinkedIn</a> | 🔗 <a href="https://github.com/vintendohuynh">GitHub</a></p> |
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