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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> |
|
|
| <div class="footer"> |
| <p>Apache 2.0 · © <a href="https://www.au-zone.com">Au-Zone Technologies Inc.</a></p> |
| </div> |
| </div> |
| </body> |
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