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</head>
<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>
<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.
</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">
</div>
<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 — measuring both accuracy (mAP) and detailed timing breakdown per device. Results are published here on HuggingFace with per-platform performance tables.
</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 — load, preprocessing, NPU inference, and decode — so you know exactly how a model performs on your specific platform.
</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 · COCO 80 classes · 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 · COCO 80 classes · 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 · COCO 80 classes · 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 · COCO 80 classes · 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 · COCO 80 classes · 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 · COCO 80 classes · 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 · COCO 80 classes · Nano Mask mAP: 34.1%</p>
</div>
</div>
<h2>Roadmap</h2>
<p>The EdgeFirst Model Zoo is expanding across the full spatial perception stack — from 2D detection through depth estimation, 3D scene understanding, and edge VLMs. All models are validated on real hardware with the same pipeline used for our YOLO models.</p>
<table class="roadmap-table">
<tr><th>Category</th><th>Examples</th><th>Platforms</th><th>Status</th></tr>
<tr>
<td>Detection (Apache 2.0)</td>
<td class="category">DETR-class, EfficientDet, mobile-optimized detectors</td>
<td><div class="badge-row"><span class="platform-badge">i.MX</span> <span class="platform-badge">Ara240</span> <span class="platform-badge">Hailo</span> <span class="platform-badge">Jetson</span></div></td>
<td><span class="status-coming">Coming Soon</span></td>
</tr>
<tr>
<td>Semantic Segmentation</td>
<td class="category">Lightweight real-time scene parsing</td>
<td><div class="badge-row"><span class="platform-badge">i.MX</span> <span class="platform-badge">Ara240</span> <span class="platform-badge">Hailo</span> <span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
<tr>
<td>Instance Segmentation (Apache 2.0)</td>
<td class="category">Non-YOLO mask prediction</td>
<td><div class="badge-row"><span class="platform-badge">Ara240</span> <span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
<tr>
<td>SAM-like Segmentation</td>
<td class="category">Prompted, class-agnostic masks</td>
<td><div class="badge-row"><span class="platform-badge">Ara240</span> <span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
<tr>
<td>Monocular Depth</td>
<td class="category">Relative and metric depth estimation</td>
<td><div class="badge-row"><span class="platform-badge">i.MX</span> <span class="platform-badge">Ara240</span> <span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
<tr>
<td>Stereo Depth</td>
<td class="category">Hardware stereo depth matching</td>
<td><div class="badge-row"><span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
<tr>
<td>3D Detection & Occupancy</td>
<td class="category">Monocular 3D, BEV, occupancy grids</td>
<td><div class="badge-row"><span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
<tr>
<td>Edge VLMs</td>
<td class="category">Visual language models for edge inference</td>
<td><div class="badge-row"><span class="platform-badge">Ara240</span> <span class="platform-badge">Jetson</span></div></td>
<td><span class="status-planned">Roadmap</span></td>
</tr>
</table>
<p class="roadmap-note">Roadmap is subject to change. Models are published as validation completes on each target platform.</p>
<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}.onnx</td><td>yolov8n-det.onnx</td></tr>
<tr><td>TFLite Model</td><td>{version}{size}-{task}-int8.tflite</td><td>yolov8n-det-int8.tflite</td></tr>
<tr><td>i.MX 95 TFLite</td><td>{version}{size}-{task}.imx95.tflite</td><td>yolov8n-det.imx95.tflite</td></tr>
<tr><td>i.MX 93 TFLite</td><td>{version}{size}-{task}.imx93.tflite</td><td>yolov8n-det.imx93.tflite</td></tr>
<tr><td>i.MX 943 TFLite</td><td>{version}{size}-{task}.imx943.tflite</td><td>yolov8n-det.imx943.tflite</td></tr>
<tr><td>Hailo HEF</td><td>{version}{size}-{task}.hailo{variant}.hef</td><td>yolov8n-det.hailo8l.hef</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>
<tr>
<td>Reference</td>
<td>ONNX FP32 and TFLite INT8 mAP on full COCO val2017 (5000 images)</td>
<td>EdgeFirst Studio (cloud)</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>
</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>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>
</p>
<ul class="studio-features">
<li>Dataset management & 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>
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