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  <title>HyperVision — Object Detection</title>
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<body>
  <header>
    <h1>🔍 <span>HyperVision</span> — Object Detection</h1>
    <div class="subtitle">
      Premium Edition by <a href="https://myndlabs.tech" target="_blank">Myndlabs.tech</a>
      &nbsp;·&nbsp; Lightweight anchor-free object detection
    </div>
  </header>

  <div class="container">
    <!-- Overview -->
    <div class="card">
      <h2>📋 Overview</h2>
      <p>
        HyperVision is a lightweight anchor-free object detection model, evolved from the 
        NanoDet-Plus architecture. It delivers real-time detection on mobile and edge devices 
        while maintaining competitive accuracy. The model uses Generalized Focal Loss with 
        a ShuffleNetV2 backbone and Ghost-PAN feature pyramid.
      </p>
      <div class="tags">
        <span class="tag accent">object-detection</span>
        <span class="tag">computer-vision</span>
        <span class="tag">lightweight</span>
        <span class="tag">mobile</span>
        <span class="tag">pytorch</span>
        <span class="tag">ncnn</span>
        <span class="tag">mnn</span>
        <span class="tag">openvino</span>
        <span class="tag">onnx</span>
      </div>
      <div class="btn-group">
        <a href="https://github.com/Yethikrishna/hypervision" target="_blank" class="btn">GitHub</a>
        <a href="https://huggingface.co/Yethikrishna/Hypervision" target="_blank" class="btn btn-outline">Hugging Face</a>
        <a href="https://myndlabs.tech" target="_blank" class="btn btn-outline">Myndlabs.tech</a>
      </div>
    </div>

    <!-- Architecture & Deployment -->
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      <div class="card">
        <h2>🏗️ Architecture</h2>
        <table class="arch-table">
          <tr><td>Type</td><td>Anchor-free one-stage detector (FCOS-style)</td></tr>
          <tr><td>Loss</td><td>Generalized Focal Loss (QFL + DFL + GIoU)</td></tr>
          <tr><td>Backbone</td><td>ShuffleNetV2 (configurable)</td></tr>
          <tr><td>Neck</td><td>Ghost-PAN feature pyramid</td></tr>
          <tr><td>Head</td><td>NanoDet-Plus head with AGM & DSLA</td></tr>
        </table>
      </div>

      <div class="card">
        <h2>🚀 Deployment Backends</h2>
        <table class="arch-table">
          <tr><td>ncnn</td><td>Mobile / Android C++</td></tr>
          <tr><td>MNN</td><td>Mobile / Embedded C++</td></tr>
          <tr><td>OpenVINO</td><td>Intel CPU / GPU</td></tr>
          <tr><td>ONNX</td><td>Cross-platform</td></tr>
          <tr><td>LibTorch</td><td>C++ inference</td></tr>
          <tr><td>PyTorch</td><td>Python inference</td></tr>
        </table>
      </div>
    </div>

    <!-- Supported Backbones -->
    <div class="card">
      <h2>🧠 Supported Backbones</h2>
      <p>
        ShuffleNetV2, ResNet, MobileNetV2, EfficientNet-Lite, GhostNet, RepVGG, 
        Custom CSPNet, TIMM models
      </p>
    </div>

    <!-- Try It -->
    <div class="card">
      <h2>🎯 Try It Yourself</h2>
      <p>
        To run inference with HyperVision, you can use the 
        <a href="https://github.com/Yethikrishna/hypervision" style="color: var(--accent);">GitHub repository</a> 
        or the Marimo notebook below.
      </p>
      <div class="btn-group">
        <a href="https://github.com/Yethikrishna/hypervision/blob/main/demo/demo-inference-with-pytorch.ipynb" target="_blank" class="btn">📓 Jupyter Notebook</a>
        <a href="https://github.com/Yethikrishna/hypervision" target="_blank" class="btn btn-outline">💻 CLI Inference</a>
      </div>
    </div>

    <!-- Citation -->
    <div class="card">
      <h2>📖 Citation</h2>
      <pre>@misc{hypervision,
    title={HyperVision: Lightweight anchor-free object detection model},
    author={Yethikrishna R},
    howpublished={\url{https://github.com/Yethikrishna/hypervision}},
    year={2025},
    note={Premium edition published by Myndlabs.tech}
}</pre>
    </div>
  </div>

  <footer>
    <a href="https://myndlabs.tech" target="_blank">Myndlabs.tech</a> — Enterprise-grade object detection solutions
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