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README.md

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 pinned: true
 license: apache-2.0
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-  https://cdn-uploads.huggingface.co/production/uploads/681a7e2fa7b205d31076a7ad/iBAkLGY4I0-VJbuiKZ50w.png
 ---
 
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@@ -73,4 +71,4 @@ Pre-trained models validated on real edge hardware with full-dataset accuracy me
 
 Apache 2.0 · [Au-Zone Technologies Inc.](https://www.au-zone.com)
 
-</div>
+</div>

index.html

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@@ -194,140 +126,63 @@
 <body>
     <div class="container">
         <h1><span class="edge">Edge</span><span class="first">First</span> AI</h1>
-        <p class="tagline">AI for Spatial Perception</p>
+        <p class="tagline">Spatial Perception at the Edge</p>
 
-        <p>
-            <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 &mdash; enabling real-time object detection, segmentation, sensor fusion, and 3D spatial understanding, optimized for resource-constrained embedded hardware.
-        </p>
+        <p style="text-align:center;">Open-source libraries and microservices for AI-driven spatial perception on embedded devices.</p>
 
         <div class="link-badges">
             <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>
             <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>
-            <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>
-            <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>
-        </div>
-
-        <h2>Workflow</h2>
-
-        <div class="diagram-container">
-            <img src="01-ecosystem.png" alt="EdgeFirst Model Zoo Ecosystem: Training, Validation, and Publication Workflow">
+            <a href="https://doc.edgefirst.ai"><img src="https://img.shields.io/badge/Docs-1FA0A8?style=for-the-badge&logo=readthedocs&logoColor=white" alt="Docs"></a>
+            <a href="https://edgefirst.ai"><img src="https://img.shields.io/badge/edgefirst.ai-E8B820?style=for-the-badge&logoColor=333" alt="Website"></a>
         </div>
 
+        <h2>About</h2>
         <p>
-            Every model in the EdgeFirst Model Zoo passes through a validated pipeline. <a href="https://edgefirst.studio"><strong>EdgeFirst Studio</strong></a> manages datasets, training, multi-format export (ONNX, TFLite INT8, eIQ Neutron, Kinara DVM, HailoRT HEF, TensorRT), and reference validation. Models are then deployed to our board farm for <strong>full-dataset on-target validation</strong> on real hardware &mdash; measuring both accuracy (mAP) and detailed timing breakdown per device. Results are published here on HuggingFace with per-platform performance tables.
+            <strong>Au-Zone Technologies</strong> builds EdgeFirst &mdash; a comprehensive platform for deploying AI perception on edge devices. We support cameras, LiDAR, radar, and time-of-flight sensors for real-time object detection, segmentation, sensor fusion, and 3D spatial understanding.
         </p>
-
-        <h2>Model Lifecycle</h2>
-
-        <div class="diagram-container">
-            <img src="02-model-lifecycle.png" alt="Model Lifecycle: 5 stages from training to publication">
-        </div>
-
-        <h2>On-Target Validation</h2>
-
-        <div class="diagram-container">
-            <img src="03-on-target-validation.png" alt="On-Target Validation Pipeline: full dataset validation on real hardware">
-        </div>
-
         <p>
-            Unlike desktop-only benchmarks, EdgeFirst validates every model on <strong>real target hardware</strong> with the full dataset. Each device produces both accuracy metrics (mAP) and a detailed timing breakdown &mdash; load, preprocessing, NPU inference, and decode &mdash; so you know exactly how a model performs on your specific platform.
+            Our stack spans four layers: <strong>Foundation</strong> (hardware abstraction and inference delegates), <strong>Zenoh</strong> (pub/sub microservices), <strong>GStreamer</strong> (media pipeline plugins), and <strong>ROS 2</strong> integration. All released under <strong>Apache 2.0</strong>.
         </p>
 
-        <h2>Supported Hardware</h2>
-        <div class="badges">
-            <img src="https://img.shields.io/badge/NXP-i.MX_8M_Plus-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 8M Plus">
-            <img src="https://img.shields.io/badge/NXP-i.MX_95-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 95">
-            <img src="https://img.shields.io/badge/NXP-Ara240-3E3371?style=flat-square&logoColor=white" alt="NXP Ara240">
-            <img src="https://img.shields.io/badge/RPi5-Hailo--8%2F8L-1FA0A8?style=flat-square&logoColor=white" alt="RPi5 + Hailo-8/8L">
-            <img src="https://img.shields.io/badge/NVIDIA-Jetson-76B900?style=flat-square&logoColor=white" alt="NVIDIA Jetson">
-        </div>
-
         <h2>Model Zoo</h2>
-        <p>Pre-trained YOLO models for edge deployment. Each model repo contains all sizes (nano through x-large), ONNX FP32 and TFLite INT8 formats, with platform-specific compiled variants as they become available.</p>
-
-        <h3>Detection</h3>
-        <div class="model-grid">
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolo26-det">YOLO26</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano mAP@0.5: 54.9%</p>
-            </div>
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolo11-det">YOLO11</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano mAP@0.5: 53.4%</p>
-            </div>
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolov8-det">YOLOv8</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano mAP@0.5: 50.2%</p>
-            </div>
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolov5-det">YOLOv5</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano mAP@0.5: 49.6%</p>
-            </div>
-        </div>
-
-        <h3>Instance Segmentation</h3>
-        <div class="model-grid">
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolo26-seg">YOLO26</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano Mask mAP: 37.0%</p>
-            </div>
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolo11-seg">YOLO11</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano Mask mAP: 35.5%</p>
-            </div>
-            <div class="model-card">
-                <h3><a href="https://huggingface.co/EdgeFirst/yolov8-seg">YOLOv8</a></h3>
-                <p class="meta">n/s/m/l/x &middot; COCO 80 classes &middot; Nano Mask mAP: 34.1%</p>
-            </div>
-        </div>
-
-        <h2>Naming Convention</h2>
-        <p>Each HuggingFace repo contains one model family for one task, with all size variants inside.</p>
-        <table class="naming-table">
-            <tr><th>Component</th><th>Pattern</th><th>Example</th></tr>
-            <tr><td>HF Repo</td><td>EdgeFirst/{version}-{task}</td><td>EdgeFirst/yolov8-det</td></tr>
-            <tr><td>ONNX Model</td><td>{version}{size}-{task}-coco.onnx</td><td>yolov8n-det-coco.onnx</td></tr>
-            <tr><td>TFLite Model</td><td>{version}{size}-{task}-coco-int8.tflite</td><td>yolov8n-det-coco-int8.tflite</td></tr>
-            <tr><td>Studio Project</td><td>{Dataset} {Task}</td><td>COCO Detection</td></tr>
-            <tr><td>Studio Experiment</td><td>{Version} {Task}</td><td>YOLOv8 Detection</td></tr>
-        </table>
-
-        <h2>Validation Pipeline</h2>
-        <p>Models go through two validation stages before publication:</p>
-        <table class="arch-table">
-            <tr><th>Stage</th><th>What</th><th>Where</th></tr>
+        <p>Pre-trained models validated on real edge hardware with full-dataset accuracy metrics and detailed timing breakdowns per device.</p>
+        <a class="cta" href="https://huggingface.co/spaces/EdgeFirst/Models">Browse the EdgeFirst Model Zoo &rarr;</a>
+        <table class="model-table">
+            <tr><th>Task</th><th>Models</th></tr>
             <tr>
-                <td>Reference</td>
-                <td>ONNX FP32 and TFLite INT8 mAP on full COCO val2017 (5000 images)</td>
-                <td>EdgeFirst Studio (cloud)</td>
+                <td>Detection</td>
+                <td><a href="https://huggingface.co/EdgeFirst/yolo26-det">YOLO26</a> &middot; <a href="https://huggingface.co/EdgeFirst/yolo11-det">YOLO11</a> &middot; <a href="https://huggingface.co/EdgeFirst/yolov8-det">YOLOv8</a> &middot; <a href="https://huggingface.co/EdgeFirst/yolov5-det">YOLOv5</a></td>
             </tr>
             <tr>
-                <td>On-Target</td>
-                <td>Full dataset mAP + timing breakdown (load, preproc, invoke, decode, e2e) per device</td>
-                <td>Board farm (real hardware) <span class="wip-tag">In Progress</span></td>
+                <td>Segmentation</td>
+                <td><a href="https://huggingface.co/EdgeFirst/yolo26-seg">YOLO26</a> &middot; <a href="https://huggingface.co/EdgeFirst/yolo11-seg">YOLO11</a> &middot; <a href="https://huggingface.co/EdgeFirst/yolov8-seg">YOLOv8</a></td>
             </tr>
         </table>
 
-        <h2>Perception Architecture</h2>
-        <table class="arch-table">
-            <tr><th>Layer</th><th>Description</th></tr>
-            <tr><td>Foundation</td><td>Hardware abstraction, video I/O, accelerated inference delegates</td></tr>
-            <tr><td>Zenoh</td><td>Modular perception pipeline over Zenoh pub/sub</td></tr>
-            <tr><td>GStreamer</td><td>Spatial perception elements for GStreamer / NNStreamer</td></tr>
-            <tr><td>ROS 2</td><td>Native ROS 2 nodes extending Zenoh microservices <span class="roadmap-tag">Roadmap</span></td></tr>
-        </table>
+        <h2>Supported Hardware</h2>
+        <div class="badges">
+            <img src="https://img.shields.io/badge/NXP-i.MX_8M_Plus-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 8M Plus">
+            <img src="https://img.shields.io/badge/NXP-i.MX_95-3E3371?style=flat-square&logoColor=white" alt="NXP i.MX 95">
+            <img src="https://img.shields.io/badge/NXP-Ara240-3E3371?style=flat-square&logoColor=white" alt="NXP Ara240">
+            <img src="https://img.shields.io/badge/RPi5-Hailo--8%2F8L-1FA0A8?style=flat-square&logoColor=white" alt="RPi5 + Hailo">
+            <img src="https://img.shields.io/badge/NVIDIA-Jetson-76B900?style=flat-square&logoColor=white" alt="NVIDIA Jetson">
+        </div>
 
         <h2>EdgeFirst Studio</h2>
         <p>
-            <a href="https://edgefirst.studio"><strong>EdgeFirst Studio</strong></a> is the MLOps platform that drives the entire model zoo pipeline. <strong>Free tier available.</strong>
+            <a href="https://edgefirst.studio"><strong>EdgeFirst Studio</strong></a> is our MLOps platform for the complete perception development lifecycle &mdash; dataset management, model training, INT8 quantization, on-target validation, and deployment. <strong>Free tier available.</strong>
         </p>
-        <ul class="studio-features">
-            <li>Dataset management &amp; AI-assisted annotation</li>
-            <li>Model training with automatic multi-format export and INT8 quantization</li>
-            <li>Reference and on-target validation with full metrics collection</li>
-            <li>CameraAdaptor integration for native sensor format training</li>
-            <li>Deploy trained models to edge devices via the <a href="https://github.com/EdgeFirstAI/client">EdgeFirst Client</a> CLI</li>
-        </ul>
+
+        <h2>Links</h2>
+        <table class="links-table">
+            <tr><td>Website</td><td><a href="https://edgefirst.ai">edgefirst.ai</a></td></tr>
+            <tr><td>Studio</td><td><a href="https://edgefirst.studio">edgefirst.studio</a></td></tr>
+            <tr><td>GitHub</td><td><a href="https://github.com/EdgeFirstAI">github.com/EdgeFirstAI</a></td></tr>
+            <tr><td>Documentation</td><td><a href="https://doc.edgefirst.ai">doc.edgefirst.ai</a></td></tr>
+            <tr><td>Model Zoo</td><td><a href="https://huggingface.co/spaces/EdgeFirst/Models">EdgeFirst/Models</a></td></tr>
+            <tr><td>Company</td><td><a href="https://www.au-zone.com">Au-Zone Technologies</a></td></tr>
+        </table>
 
         <div class="footer">
             <p>Apache 2.0 &middot; &copy; <a href="https://www.au-zone.com">Au-Zone Technologies Inc.</a></p>