EdgeFirst Model Zoo landing page

.gitattributes

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+01-ecosystem.png filter=lfs diff=lfs merge=lfs -text
+02-model-lifecycle.png filter=lfs diff=lfs merge=lfs -text
+03-on-target-validation.png filter=lfs diff=lfs merge=lfs -text
+04-coverage-matrix.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,10 +1,112 @@
 ---
-title: Models
-emoji: 🏢
+title: EdgeFirst AI
+emoji: 🔬
 colorFrom: indigo
-colorTo: blue
+colorTo: red
 sdk: static
-pinned: false
+pinned: true
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# EdgeFirst AI — Spatial Perception at the Edge
+
+**EdgeFirst Perception** 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.
+
+[![EdgeFirst Studio](https://img.shields.io/badge/EdgeFirst_Studio-3E3371?style=for-the-badge&logoColor=white)](https://edgefirst.studio)
+[![GitHub](https://img.shields.io/badge/GitHub-212529?style=for-the-badge&logo=github&logoColor=white)](https://github.com/EdgeFirstAI)
+[![Documentation](https://img.shields.io/badge/Documentation-1FA0A8?style=for-the-badge&logo=readthedocs&logoColor=white)](https://doc.edgefirst.ai)
+[![Au-Zone Technologies](https://img.shields.io/badge/Au--Zone_Technologies-6C757D?style=for-the-badge)](https://www.au-zone.com)
+
+---
+
+## Workflow
+
+<img src="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/01-ecosystem.png" alt="EdgeFirst Model Zoo Ecosystem"/>
+
+Every model in the EdgeFirst Model Zoo passes through a validated pipeline. [**EdgeFirst Studio**](https://edgefirst.studio) 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 **full-dataset on-target validation** on real hardware — measuring both accuracy (mAP) and detailed timing breakdown per device. Results are published here on HuggingFace with per-platform performance tables.
+
+## Model Lifecycle
+
+<img src="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/02-model-lifecycle.png" alt="Model Lifecycle: Training to Publication"/>
+
+## On-Target Validation
+
+<img src="https://huggingface.co/spaces/EdgeFirst/README/resolve/main/03-on-target-validation.png" alt="On-Target Validation Pipeline"/>
+
+Unlike desktop-only benchmarks, EdgeFirst validates every model on **real target hardware** 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.
+
+---
+
+## Supported Hardware
+
+![NXP i.MX 8M Plus](https://img.shields.io/badge/NXP-i.MX_8M_Plus-3E3371?style=flat-square&logoColor=white)
+![NXP i.MX 95](https://img.shields.io/badge/NXP-i.MX_95-3E3371?style=flat-square&logoColor=white)
+![NXP Ara240](https://img.shields.io/badge/NXP-Ara240-3E3371?style=flat-square&logoColor=white)
+![RPi5 + Hailo-8/8L](https://img.shields.io/badge/RPi5-Hailo--8%2F8L-1FA0A8?style=flat-square&logoColor=white)
+![NVIDIA Jetson](https://img.shields.io/badge/NVIDIA-Jetson-76B900?style=flat-square&logoColor=white)
+
+---
+
+## Model Zoo
+
+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.
+
+### Detection
+
+| Model | Sizes | Nano mAP@0.5 | Link |
+|-------|-------|-------------|------|
+| **YOLO26** | n/s/m/l/x | 54.9% | [EdgeFirst/yolo26-det](https://huggingface.co/EdgeFirst/yolo26-det) |
+| **YOLO11** | n/s/m/l/x | 53.4% | [EdgeFirst/yolo11-det](https://huggingface.co/EdgeFirst/yolo11-det) |
+| **YOLOv8** | n/s/m/l/x | 50.2% | [EdgeFirst/yolov8-det](https://huggingface.co/EdgeFirst/yolov8-det) |
+| **YOLOv5** | n/s/m/l/x | 49.6% | [EdgeFirst/yolov5-det](https://huggingface.co/EdgeFirst/yolov5-det) |
+
+### Instance Segmentation
+
+| Model | Sizes | Nano Mask mAP | Link |
+|-------|-------|--------------|------|
+| **YOLO26** | n/s/m/l/x | 37.0% | [EdgeFirst/yolo26-seg](https://huggingface.co/EdgeFirst/yolo26-seg) |
+| **YOLO11** | n/s/m/l/x | 35.5% | [EdgeFirst/yolo11-seg](https://huggingface.co/EdgeFirst/yolo11-seg) |
+| **YOLOv8** | n/s/m/l/x | 34.1% | [EdgeFirst/yolov8-seg](https://huggingface.co/EdgeFirst/yolov8-seg) |
+
+---
+
+## Naming Convention
+
+| Component | Pattern | Example |
+|-----------|---------|---------|
+| HF Repo | `EdgeFirst/{version}-{task}` | `EdgeFirst/yolov8-det` |
+| ONNX Model | `{version}{size}-{task}-coco.onnx` | `yolov8n-det-coco.onnx` |
+| TFLite Model | `{version}{size}-{task}-coco.tflite` | `yolov8n-det-coco.tflite` |
+| i.MX 95 Model | `{version}{size}-{task}-coco.imx95.tflite` | `yolov8n-det-coco.imx95.tflite` |
+| Studio Project | `{Dataset} {Task}` | `COCO Detection` |
+| Studio Experiment | `{Version} {Task}` | `YOLOv8 Detection` |
+
+## Validation Pipeline
+
+| Stage | What | Where |
+|-------|------|-------|
+| **Reference** | ONNX FP32 and TFLite INT8 mAP on full COCO val2017 (5000 images) | EdgeFirst Studio (cloud) |
+| **On-Target** | Full dataset mAP + timing breakdown per device | Board farm (real hardware) |
+
+## Perception Architecture
+
+| Layer | Description |
+|-------|-------------|
+| **Foundation** | Hardware abstraction, video I/O, accelerated inference delegates |
+| **Zenoh** | Modular perception pipeline over Zenoh pub/sub |
+| **GStreamer** | Spatial perception elements for GStreamer / NNStreamer |
+| **ROS 2** | Native ROS 2 nodes extending Zenoh microservices *(Roadmap)* |
+
+## EdgeFirst Studio
+
+[**EdgeFirst Studio**](https://edgefirst.studio) is the MLOps platform that drives the entire model zoo pipeline. **Free tier available.**
+
+- Dataset management & AI-assisted annotation
+- Model training with automatic multi-format export and INT8 quantization
+- Reference and on-target validation with full metrics collection
+- CameraAdaptor integration for native sensor format training
+- Deploy trained models to edge devices via the [EdgeFirst Client](https://github.com/EdgeFirstAI/client) CLI
+
+---
+
+Apache 2.0 · [Au-Zone Technologies Inc.](https://www.au-zone.com)

index.html

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-<!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
+<!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>
+    <base target="_blank">
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <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">
+    <style>
+        :root {
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+            --border: #E9ECEF;
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+            --link: var(--teal-text);
+        }
+        @media (prefers-color-scheme: dark) {
+            :root {
+                --bg: #1a1a2e;
+                --bg-subtle: #16213e;
+                --bg-card: rgba(75, 0, 130, 0.2);
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+                --text-muted: #aaa;
+                --border: rgba(255,255,255,0.1);
+                --heading: #FFFFFF;
+                --link: #1FA0A8;
+            }
+        }
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body {
+            font-family: 'Crimson Text', Georgia, serif;
+            background: var(--bg);
+            color: var(--text);
+            line-height: 1.7;
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+        .link-badges img { height: 28px; }
+        /* Workflow diagram */
+        .diagram-container {
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+            text-align: center;
+        }
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+            max-width: 100%;
+            height: auto;
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+            width: 100%;
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+        }
+        .roadmap-tag {
+            font-family: 'Barlow', sans-serif;
+            font-size: 0.7rem; font-weight: 600;
+            padding: 0.1rem 0.45rem; border-radius: 3px;
+            background: var(--indigo); color: #fff;
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+        }
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+            display: grid;
+            grid-template-columns: repeat(auto-fill, minmax(240px, 1fr));
+            gap: 0.75rem; margin: 1rem 0;
+        }
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+        .studio-features { margin: 0.75rem 0 0 1.25rem; color: var(--text); font-size: 0.95rem; }
+        .studio-features li { margin-bottom: 0.25rem; }
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+        .footer a:hover { color: var(--link); }
+    </style>
+</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 &mdash; 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 &mdash; 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 &mdash; load, preprocessing, NPU inference, and decode &mdash; 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 &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>
+            <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 &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>
+
+        <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>