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Update org landing page with EdgeFirst branding and model zoo links

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README.md +33 -6
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-title: README
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-Edit this `README.md` markdown file to author your organization card.

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+title: EdgeFirst AI
+emoji: 🔬
+colorFrom: indigo
+colorTo: red
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+pinned: true
+license: apache-2.0
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+# EdgeFirst AI — Spatial Perception at the Edge
+Open-source libraries and microservices for AI-driven spatial perception on edge devices.
+**EdgeFirst Perception** supports cameras, LiDAR, radar, and time-of-flight sensors — enabling real-time object detection, segmentation, sensor fusion, and 3D spatial understanding, all optimized for resource-constrained embedded hardware.
+## Architecture
+| Layer | Description | Status |
+|-------|-------------|--------|
+| **Foundation** | Hardware abstraction, video I/O, accelerated inference delegates | Stable |
+| **Zenoh** | Modular perception pipeline over Zenoh pub/sub | Stable |
+| **GStreamer** | Spatial perception elements for GStreamer / NNStreamer | Stable |
+| **ROS 2** | Native ROS 2 nodes extending Zenoh microservices | Roadmap |
+## Supported Hardware
+NXP i.MX 8M Plus | NXP i.MX 93 | NXP i.MX 95 | NXP Ara240 | RPi5 + Hailo-8/8L | NVIDIA Jetson
+## Links
+- [EdgeFirst Studio](https://edgefirst.studio) — MLOps platform (free tier available)
+- [GitHub](https://github.com/EdgeFirstAI) — Open-source repositories
+- [Documentation](https://doc.edgefirst.ai) — Full documentation
+- [Au-Zone Technologies](https://www.au-zone.com) — Company
+Apache 2.0 | Au-Zone Technologies Inc.

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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>EdgeFirst AI — Spatial Perception at the Edge</title>
+    <style>
+        :root {
+            --primary: #1a1a2e;
+            --accent: #0f3460;
+            --highlight: #e94560;
+            --text: #eee;
+            --muted: #aaa;
+            --card-bg: #16213e;
+        }
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background: var(--primary);
+            color: var(--text);
+            line-height: 1.6;
+        }
+        .container { max-width: 900px; margin: 0 auto; padding: 2rem; }
+        h1 { font-size: 2rem; margin-bottom: 0.25rem; }
+        h2 { font-size: 1.3rem; margin-top: 2rem; margin-bottom: 0.75rem; color: var(--highlight); }
+        .tagline { color: var(--muted); font-size: 1.1rem; margin-bottom: 1.5rem; }
+        .description { margin-bottom: 1.5rem; }
+        .badges { display: flex; flex-wrap: wrap; gap: 0.5rem; margin: 1.5rem 0; }
+        .badge {
+            background: var(--accent);
+            padding: 0.3rem 0.75rem;
+            border-radius: 4px;
+            font-size: 0.85rem;
+            white-space: nowrap;
+        }
+        .links { display: flex; gap: 1rem; margin: 1.5rem 0; flex-wrap: wrap; }
+        .links a {
+            color: var(--highlight);
+            text-decoration: none;
+            padding: 0.5rem 1rem;
+            border: 1px solid var(--highlight);
+            border-radius: 4px;
+            transition: all 0.2s;
+        }
+        .links a:hover { background: var(--highlight); color: #fff; }
+        .arch-table { width: 100%; border-collapse: collapse; margin: 1rem 0; }
+        .arch-table th, .arch-table td {
+            text-align: left;
+            padding: 0.5rem 0.75rem;
+            border-bottom: 1px solid rgba(255,255,255,0.1);
+        }
+        .arch-table th { color: var(--highlight); font-size: 0.85rem; text-transform: uppercase; }
+        .status { font-size: 0.8rem; padding: 0.15rem 0.5rem; border-radius: 3px; }
+        .stable { background: #1b5e20; }
+        .roadmap { background: #4a148c; }
+        .model-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(250px, 1fr)); gap: 1rem; margin: 1rem 0; }
+        .model-card {
+            background: var(--card-bg);
+            padding: 1rem;
+            border-radius: 6px;
+            border-left: 3px solid var(--highlight);
+        }
+        .model-card h3 { font-size: 1rem; margin-bottom: 0.25rem; }
+        .model-card .meta { color: var(--muted); font-size: 0.85rem; }
+        .model-card a { color: var(--highlight); text-decoration: none; }
+        .footer { margin-top: 3rem; text-align: center; color: var(--muted); font-size: 0.85rem; }
+        .footer a { color: var(--muted); }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>EdgeFirst AI</h1>
+        <p class="tagline">Spatial Perception at the Edge</p>
+        <p class="description">
+            <strong>EdgeFirst Perception</strong> is a comprehensive suite of open-source libraries and microservices for building AI-driven spatial perception systems on edge devices. It supports cameras, LiDAR, radar, and time-of-flight sensors &mdash; enabling real-time object detection, segmentation, sensor fusion, and 3D spatial understanding, all optimized for resource-constrained embedded hardware.
+        </p>
+        <div class="links">
+            <a href="https://edgefirst.studio">EdgeFirst Studio</a>
+            <a href="https://github.com/EdgeFirstAI">GitHub</a>
+            <a href="https://doc.edgefirst.ai">Documentation</a>
+            <a href="https://www.au-zone.com">Au-Zone Technologies</a>
+        </div>
+        <h2>Supported Hardware</h2>
+        <div class="badges">
+            <span class="badge">NXP i.MX 8M Plus</span>
+            <span class="badge">NXP i.MX 93</span>
+            <span class="badge">NXP i.MX 95</span>
+            <span class="badge">NXP Ara240</span>
+            <span class="badge">RPi5 + Hailo-8/8L</span>
+            <span class="badge">NVIDIA Jetson</span>
+        </div>
+        <h2>Architecture</h2>
+        <table class="arch-table">
+            <tr>
+                <th>Layer</th>
+                <th>Description</th>
+                <th>Status</th>
+            </tr>
+            <tr>
+                <td><strong>Foundation</strong></td>
+                <td>Hardware abstraction, video I/O, accelerated inference delegates</td>
+                <td><span class="status stable">Stable</span></td>
+            </tr>
+            <tr>
+                <td><strong>Zenoh</strong></td>
+                <td>Modular perception pipeline over Zenoh pub/sub</td>
+                <td><span class="status stable">Stable</span></td>
+            </tr>
+            <tr>
+                <td><strong>GStreamer</strong></td>
+                <td>Spatial perception elements for GStreamer / NNStreamer</td>
+                <td><span class="status stable">Stable</span></td>
+            </tr>
+            <tr>
+                <td><strong>ROS 2</strong></td>
+                <td>Native ROS 2 nodes extending Zenoh microservices</td>
+                <td><span class="status roadmap">Roadmap</span></td>
+            </tr>
+        </table>
+        <h2>Model Zoo</h2>
+        <p>Pre-trained YOLO models optimized for edge deployment &mdash; ONNX FP32 and TFLite INT8 with platform-specific compiled variants.</p>
+        <div class="model-grid">
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolov8-det">YOLOv8 Detection</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano mAP@0.5: 50.2%</p>
+            </div>
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolov8-seg">YOLOv8 Segmentation</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano Mask mAP: 34.1%</p>
+            </div>
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolo11-det">YOLO11 Detection</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano mAP@0.5: 53.4%</p>
+            </div>
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolo11-seg">YOLO11 Segmentation</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano Mask mAP: 35.5%</p>
+            </div>
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolo26-det">YOLO26 Detection</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano mAP@0.5: 54.9%</p>
+            </div>
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolo26-seg">YOLO26 Segmentation</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano Mask mAP: 37.0%</p>
+            </div>
+            <div class="model-card">
+                <h3><a href="https://huggingface.co/EdgeFirst/yolov5-det">YOLOv5 Detection</a></h3>
+                <p class="meta">5 sizes &middot; COCO 80 classes &middot; Nano mAP@0.5: 49.6%</p>
+            </div>
+        </div>
+        <h2>EdgeFirst Studio</h2>
+        <p>
+            <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>
+        </p>
+        <ul style="margin: 0.75rem 0 0 1.5rem; color: var(--muted);">
+            <li>Dataset management &amp; AI-assisted annotation</li>
+            <li>YOLO model training with automatic INT8 quantization</li>
+            <li>CameraAdaptor integration for native sensor format training</li>
+            <li>One-click deployment to edge devices</li>
+        </ul>
+        <div class="footer">
+            <p>Apache 2.0 &middot; &copy; <a href="https://www.au-zone.com">Au-Zone Technologies Inc.</a></p>
+        </div>
+    </div>
+</body>
+</html>