Rebrand landing page: EdgeFirst palette, Barlow/Crimson Text typography, proper light+dark theme
Browse files- index.html +216 -67
index.html
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>EdgeFirst AI — Spatial Perception at the Edge</title>
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<style>
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:root {
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}
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@media (prefers-color-scheme: dark) {
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:root {
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--bg: #1a1a2e;
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--border: rgba(255,255,255,0.1);
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}
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}
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body {
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font-family:
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background: var(--bg);
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color: var(--text);
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line-height: 1.
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.badges img { height: 22px; }
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text-align: left;
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padding: 0.5rem 0.75rem;
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border-bottom: 1px solid var(--border);
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}
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.arch-table
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.roadmap-tag {
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font-
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border-radius: 3px;
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background:
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color: #fff;
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vertical-align: middle;
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margin-left: 0.3rem;
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}
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.model-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(250px, 1fr)); gap: 1rem; margin: 1rem 0; }
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.model-card {
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border-
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</style>
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</head>
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<body>
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<div class="container">
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<h1>
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<p class="tagline">
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<p
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<strong>EdgeFirst Perception</strong> is
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</p>
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<div class="
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<a href="https://edgefirst.studio"><img src="https://img.shields.io/badge/EdgeFirst_Studio-
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<a href="https://github.com/EdgeFirstAI"><img src="https://img.shields.io/badge/GitHub-
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<a href="https://doc.edgefirst.ai"><img src="https://img.shields.io/badge/
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<a href="https://www.au-zone.com"><img src="https://img.shields.io/badge/Au--Zone_Technologies-
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</div>
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<h2>Supported Hardware</h2>
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<div class="badges">
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<img src="https://img.shields.io/badge/NXP-i.MX_8M_Plus-
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<img src="https://img.shields.io/badge/NXP-i.MX_93-
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<img src="https://img.shields.io/badge/NXP-i.MX_95-
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<img src="https://img.shields.io/badge/NXP-Ara240-
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<img src="https://img.shields.io/badge/RPi5-Hailo--8%2F8L-
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<img src="https://img.shields.io/badge/NVIDIA-Jetson-76B900?style=flat-square" alt="NVIDIA Jetson">
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</div>
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<h2>Architecture</h2>
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<th>Description</th>
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</tr>
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<tr>
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<td>
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<td>Hardware abstraction, video I/O, accelerated inference delegates</td>
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</tr>
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<tr>
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<td>
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<td>Modular perception pipeline over Zenoh pub/sub</td>
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</tr>
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<tr>
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<td>
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<td>Spatial perception elements for GStreamer / NNStreamer</td>
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</tr>
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<tr>
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<td>
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<td>Native ROS 2 nodes extending Zenoh microservices <span class="roadmap-tag">Roadmap</span></td>
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</tr>
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</table>
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<h2>Model Zoo</h2>
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<p>Pre-trained YOLO models
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<div class="model-grid">
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<div class="model-card">
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<h3><a href="https://huggingface.co/EdgeFirst/
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<p class="meta">5 sizes · COCO 80 classes · Nano mAP@0.5:
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</div>
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<div class="model-card">
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<h3><a href="https://huggingface.co/EdgeFirst/
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<p class="meta">5 sizes · COCO 80 classes · Nano Mask mAP:
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</div>
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<div class="model-card">
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<h3><a href="https://huggingface.co/EdgeFirst/yolo11-det">YOLO11 Detection</a></h3>
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<p class="meta">5 sizes · COCO 80 classes · Nano Mask mAP: 35.5%</p>
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</div>
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<div class="model-card">
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<h3><a href="https://huggingface.co/EdgeFirst/
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<p class="meta">5 sizes · COCO 80 classes · Nano mAP@0.5:
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</div>
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<div class="model-card">
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<h3><a href="https://huggingface.co/EdgeFirst/
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<p class="meta">5 sizes · COCO 80 classes · Nano Mask mAP:
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</div>
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<div class="model-card">
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<h3><a href="https://huggingface.co/EdgeFirst/yolov5-det">YOLOv5 Detection</a></h3>
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<p>
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<a href="https://edgefirst.studio"><strong>EdgeFirst Studio</strong></a> is the companion SaaS platform for the complete perception development lifecycle. <strong>Free tier available.</strong>
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</p>
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<ul
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<li>Dataset management & AI-assisted annotation</li>
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<li>YOLO model training with automatic INT8 quantization</li>
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<li>CameraAdaptor integration for native sensor format training</li>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>EdgeFirst AI — Spatial Perception at the Edge</title>
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
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<link href="https://fonts.googleapis.com/css2?family=Barlow:wght@300;400;500;600;700&family=Crimson+Text:wght@400;600&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
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<style>
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:root {
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/* Brand primary */
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--navy: #3E3371;
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--gold: #E8B820;
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--teal: #1FA0A8;
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--teal-text: #167A80;
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--indigo: #4B0082;
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--blue: #8FA3D4;
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/* Light theme (default) */
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--bg: #FFFFFF;
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--bg-subtle: #F8F9FA;
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--bg-card: #F0EDF8;
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--text: #343A40;
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--text-strong: #212529;
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--text-muted: #6C757D;
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--border: #E9ECEF;
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--heading: var(--navy);
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--link: var(--teal-text);
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--accent-bg: var(--navy);
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--accent-text: #FFFFFF;
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}
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@media (prefers-color-scheme: dark) {
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:root {
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--bg: #1a1a2e;
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--bg-subtle: #16213e;
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--bg-card: rgba(75, 0, 130, 0.2);
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--text: #F1F3F5;
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--text-strong: #FFFFFF;
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--text-muted: #aaa;
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--border: rgba(255,255,255,0.1);
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--heading: #FFFFFF;
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--link: #1FA0A8;
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--accent-bg: var(--navy);
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--accent-text: #FFFFFF;
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}
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}
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body {
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font-family: 'Crimson Text', Georgia, serif;
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background: var(--bg);
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color: var(--text);
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line-height: 1.7;
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font-size: 16px;
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}
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.container {
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max-width: 860px;
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margin: 0 auto;
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padding: 2.5rem 2rem;
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}
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/* Typography */
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h1, h2, h3 {
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font-family: 'Barlow', -apple-system, sans-serif;
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color: var(--heading);
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line-height: 1.3;
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}
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h1 {
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font-size: 2.2rem;
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font-weight: 700;
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margin-bottom: 0.15rem;
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}
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h1 .edge { color: var(--heading); }
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h1 .first { color: var(--gold); }
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h2 {
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font-size: 1.15rem;
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font-weight: 600;
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text-transform: uppercase;
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letter-spacing: 0.06em;
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margin-top: 2.5rem;
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margin-bottom: 0.75rem;
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padding-bottom: 0.35rem;
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border-bottom: 2px solid var(--gold);
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}
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.tagline {
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font-family: 'Barlow', sans-serif;
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font-weight: 500;
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letter-spacing: 0.12em;
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text-transform: uppercase;
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color: var(--text-muted);
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font-size: 0.85rem;
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margin-bottom: 1.5rem;
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}
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p { margin-bottom: 1rem; }
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a { color: var(--link); text-decoration: none; }
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a:hover { text-decoration: underline; color: var(--navy); }
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@media (prefers-color-scheme: dark) {
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a:hover { color: var(--gold); }
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}
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/* Badges row */
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.badges {
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display: flex;
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flex-wrap: wrap;
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gap: 0.4rem;
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margin: 1.25rem 0;
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}
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.badges img { height: 22px; }
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/* Link badges */
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.link-badges {
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display: flex;
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flex-wrap: wrap;
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gap: 0.4rem;
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margin: 1.5rem 0;
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}
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.link-badges img { height: 28px; }
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/* Architecture table */
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.arch-table {
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width: 100%;
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border-collapse: collapse;
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margin: 0.75rem 0;
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font-family: 'Barlow', sans-serif;
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font-size: 0.95rem;
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}
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.arch-table th {
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text-align: left;
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padding: 0.5rem 0.75rem;
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font-weight: 600;
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font-size: 0.8rem;
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text-transform: uppercase;
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letter-spacing: 0.05em;
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color: var(--text-muted);
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border-bottom: 2px solid var(--border);
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}
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.arch-table td {
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padding: 0.5rem 0.75rem;
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border-bottom: 1px solid var(--border);
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}
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.arch-table td:first-child {
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font-weight: 600;
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color: var(--text-strong);
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white-space: nowrap;
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}
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.roadmap-tag {
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font-family: 'Barlow', sans-serif;
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font-size: 0.7rem;
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font-weight: 600;
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padding: 0.1rem 0.45rem;
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border-radius: 3px;
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background: var(--indigo);
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color: #fff;
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vertical-align: middle;
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margin-left: 0.3rem;
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letter-spacing: 0.03em;
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text-transform: uppercase;
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}
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/* Model grid */
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.model-grid {
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display: grid;
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grid-template-columns: repeat(auto-fill, minmax(240px, 1fr));
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gap: 0.75rem;
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margin: 1rem 0;
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}
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.model-card {
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font-family: 'Barlow', sans-serif;
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background: var(--bg-card);
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padding: 0.85rem 1rem;
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border-radius: 5px;
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border-left: 3px solid var(--gold);
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transition: border-color 0.15s;
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}
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.model-card:hover { border-left-color: var(--teal); }
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.model-card h3 {
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font-size: 0.95rem;
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font-weight: 600;
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margin-bottom: 0.2rem;
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}
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.model-card h3 a { color: var(--text-strong); }
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</head>
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<body>
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<div class="container">
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<h1><span class="edge">Edge</span><span class="first">First</span> AI</h1>
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<p class="tagline">AI for Spatial Perception</p>
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<p>
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<strong>EdgeFirst Perception</strong> is an open-source suite of libraries and microservices for AI-driven spatial perception on edge devices. It supports cameras, LiDAR, radar, and time-of-flight sensors — enabling real-time object detection, segmentation, sensor fusion, and 3D spatial understanding, optimized for resource-constrained embedded hardware.
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</p>
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<div class="link-badges">
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<a href="https://edgefirst.studio"><img src="https://img.shields.io/badge/EdgeFirst_Studio-3E3371?style=for-the-badge&logoColor=white" alt="EdgeFirst Studio"></a>
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<a href="https://github.com/EdgeFirstAI"><img src="https://img.shields.io/badge/GitHub-212529?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
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<a href="https://doc.edgefirst.ai"><img src="https://img.shields.io/badge/Documentation-1FA0A8?style=for-the-badge&logo=readthedocs&logoColor=white" alt="Documentation"></a>
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<a href="https://www.au-zone.com"><img src="https://img.shields.io/badge/Au--Zone_Technologies-6C757D?style=for-the-badge" alt="Au-Zone Technologies"></a>
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</div>
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<h2>Supported Hardware</h2>
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<div class="badges">
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<img src="https://img.shields.io/badge/NXP-i.MX_8M_Plus-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 8M Plus">
|
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+
<img src="https://img.shields.io/badge/NXP-i.MX_93-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 93">
|
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+
<img src="https://img.shields.io/badge/NXP-i.MX_95-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 95">
|
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+
<img src="https://img.shields.io/badge/NXP-Ara240-3E3371?style=flat-square&logoColor=white" alt="NXP Ara240">
|
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+
<img src="https://img.shields.io/badge/RPi5-Hailo--8%2F8L-1FA0A8?style=flat-square&logoColor=white" alt="RPi5 + Hailo-8/8L">
|
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+
<img src="https://img.shields.io/badge/NVIDIA-Jetson-76B900?style=flat-square&logoColor=white" alt="NVIDIA Jetson">
|
| 245 |
</div>
|
| 246 |
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<h2>Architecture</h2>
|
|
|
|
| 251 |
<th>Description</th>
|
| 252 |
</tr>
|
| 253 |
<tr>
|
| 254 |
+
<td>Foundation</td>
|
| 255 |
<td>Hardware abstraction, video I/O, accelerated inference delegates</td>
|
| 256 |
</tr>
|
| 257 |
<tr>
|
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+
<td>Zenoh</td>
|
| 259 |
<td>Modular perception pipeline over Zenoh pub/sub</td>
|
| 260 |
</tr>
|
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<tr>
|
| 262 |
+
<td>GStreamer</td>
|
| 263 |
<td>Spatial perception elements for GStreamer / NNStreamer</td>
|
| 264 |
</tr>
|
| 265 |
<tr>
|
| 266 |
+
<td>ROS 2</td>
|
| 267 |
<td>Native ROS 2 nodes extending Zenoh microservices <span class="roadmap-tag">Roadmap</span></td>
|
| 268 |
</tr>
|
| 269 |
</table>
|
| 270 |
|
| 271 |
<h2>Model Zoo</h2>
|
| 272 |
+
<p>Pre-trained YOLO models for edge deployment — ONNX FP32 and TFLite INT8 with platform-specific compiled variants.</p>
|
| 273 |
|
| 274 |
<div class="model-grid">
|
| 275 |
<div class="model-card">
|
| 276 |
+
<h3><a href="https://huggingface.co/EdgeFirst/yolo26-det">YOLO26 Detection</a></h3>
|
| 277 |
+
<p class="meta">5 sizes · COCO 80 classes · Nano mAP@0.5: 54.9%</p>
|
| 278 |
</div>
|
| 279 |
<div class="model-card">
|
| 280 |
+
<h3><a href="https://huggingface.co/EdgeFirst/yolo26-seg">YOLO26 Segmentation</a></h3>
|
| 281 |
+
<p class="meta">5 sizes · COCO 80 classes · Nano Mask mAP: 37.0%</p>
|
| 282 |
</div>
|
| 283 |
<div class="model-card">
|
| 284 |
<h3><a href="https://huggingface.co/EdgeFirst/yolo11-det">YOLO11 Detection</a></h3>
|
|
|
|
| 289 |
<p class="meta">5 sizes · COCO 80 classes · Nano Mask mAP: 35.5%</p>
|
| 290 |
</div>
|
| 291 |
<div class="model-card">
|
| 292 |
+
<h3><a href="https://huggingface.co/EdgeFirst/yolov8-det">YOLOv8 Detection</a></h3>
|
| 293 |
+
<p class="meta">5 sizes · COCO 80 classes · Nano mAP@0.5: 50.2%</p>
|
| 294 |
</div>
|
| 295 |
<div class="model-card">
|
| 296 |
+
<h3><a href="https://huggingface.co/EdgeFirst/yolov8-seg">YOLOv8 Segmentation</a></h3>
|
| 297 |
+
<p class="meta">5 sizes · COCO 80 classes · Nano Mask mAP: 34.1%</p>
|
| 298 |
</div>
|
| 299 |
<div class="model-card">
|
| 300 |
<h3><a href="https://huggingface.co/EdgeFirst/yolov5-det">YOLOv5 Detection</a></h3>
|
|
|
|
| 306 |
<p>
|
| 307 |
<a href="https://edgefirst.studio"><strong>EdgeFirst Studio</strong></a> is the companion SaaS platform for the complete perception development lifecycle. <strong>Free tier available.</strong>
|
| 308 |
</p>
|
| 309 |
+
<ul class="studio-features">
|
| 310 |
<li>Dataset management & AI-assisted annotation</li>
|
| 311 |
<li>YOLO model training with automatic INT8 quantization</li>
|
| 312 |
<li>CameraAdaptor integration for native sensor format training</li>
|