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<title>Garbage Classification Model — CS549</title>
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<h1>🗑️ Garbage Classification Model — CS549</h1>
<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>
<h2>🔍 Use Case</h2>
<p>Helps automate waste sorting for better recycling. Great for demos, PoC apps, or smart bin integration.</p>
<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>
<h2>📦 How to Use</h2>
<pre><code>from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
model = load_model("GarbageMLModel_CS549.h5")
img = Image.open("example.jpg").resize((224, 224))
img_array = np.expand_dims(np.array(img) / 255.0, axis=0)
pred = model.predict(img_array)
classes = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash', 'biodegradable']
print("Prediction:", classes[np.argmax(pred)])</code></pre>
<h2>📁 Files</h2>
<ul>
<li><code>GarbageMLModel_CS549.h5</code> – Trained model</li>
<li><code>label_map.json</code> – Label mapping</li>
</ul>
<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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