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<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>EmbeddedML-Benchmark Results</title>
  <link rel="stylesheet" href="styles.css">
</head>
<body>
  <header>
    <div class="container">
      <h1>EmbeddedML-Benchmark</h1>
      <nav>
        <ul>
          <li><a href="#">Home</a></li>
          <li><a href="#">Benchmarks</a></li>
          <li><a href="#">Documentation</a></li>
          <li><a href="#">About</a></li>
        </ul>
      </nav>
    </div>
  </header>

  <section class="hero">
    <div class="container">
      <h2>Inference Benchmark Results</h2>
      <p>Explore the performance metrics of various machine learning models optimized for embedded systems.</p>
    </div>
  </section>

  <main class="container">
    <section class="intro">
      <p>Welcome to the EmbeddedML-Benchmark results page. Explore the performance metrics of various machine learning models optimized for embedded systems below. The results are based on real-world benchmarks for different ML tasks: Keyword Spotting, Image Classification, Anomaly Detection, and Emotion Detection.</p>
    </section>

    <section class="results">
      <h3>Benchmark Results</h3>
      <table class="benchmark-table">
        <thead>
          <tr>
            <th>Use Case</th>
            <th>Dataset</th>
            <th>TFLite Model Size</th>
            <th>Quality Target</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Image Classification</td>
            <td>CIFAR 10 (32x32)</td>
            <td>TinyML (9 KB)</td>
            <td>85% (Top-1)</td>
          </tr>
          <tr>
            <td>Keyword Spotting</td>
            <td>Speech Communication (49x10)</td>
            <td>Dense-CNN (53 KB)</td>
            <td>89% (Top-1)</td>
          </tr>
          <tr>
            <td>Anomaly Detection</td>
            <td>ToyADMOS (5x128)</td>
            <td>FC-AutoEncoder (41 KB)</td>
            <td>99% (Top-1)</td>
          </tr>
          <tr>
            <td>Emotion Detection</td>
            <td>Multi-Class Emotional Sentences (298x12)</td>
            <td>LSTM (161 KB)</td>
            <td>89% (Top-1)</td>
          </tr>
        </tbody>
      </table>
    </section>

    <section class="graphs">
      <h3>Performance Comparison</h3>
      <div class="graph">
        <img src="https://via.placeholder.com/800x400" alt="Anomaly Detection Performance" />
        <p>Anomaly Detection Results</p>
      </div>
      <div class="graph">
        <img src="https://via.placeholder.com/800x400" alt="Emotion Detection Performance" />
        <p>Emotion Detection Results</p>
      </div>
    </section>

    <section class="impact">
      <h3>Impact of the Benchmark</h3>
      <p>This benchmarking framework helps to evaluate the deployment of machine learning models in TinyML applications, offering insight into the trade-offs between accuracy, execution speed, and memory efficiency. It will assist researchers and engineers in choosing the most effective models and optimizations for real-time embedded systems.</p>
    </section>
  </main>

  <footer>
    <div class="container">
      <p>&copy; 2025 EmbeddedML-Benchmark | <a href="https://EmbeddedML-Benchmark.github.io" target="_blank">EmbeddedML-Benchmark.github.io</a></p>
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