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	<div class="container">

	<header>
	<div>
	<h1><span>Edge</span><span class="gold">First</span> · Model Zoo
	<span class="sub">/ models · platforms · benchmarks · validation</span>
	</h1>
	</div>
	<div class="header-meta">
	<span>Updated: <span id="snapshot-date">—</span></span>
	<span style="display:none">HF: <span id="hf-status" class="badge">loading…</span></span>
	<a href="https://huggingface.co/EdgeFirst" target="_blank" rel="noopener">huggingface.co/EdgeFirst →</a>
	</div>
	</header>

	<!-- KPI row -->
	<div class="kpi-row">
	<div class="kpi"><div class="kpi-label">Model repos</div><div class="kpi-value" id="kpi-repos">—</div><div class="kpi-sub">family × task</div></div>
	<div class="kpi"><div class="kpi-label">Model families</div><div class="kpi-value" id="kpi-families">—</div><div class="kpi-sub">YOLOv5 → YOLO26</div></div>
	<div class="kpi"><div class="kpi-label">Tasks</div><div class="kpi-value" id="kpi-tasks">—</div><div class="kpi-sub">detection · instance seg</div></div>
	<div class="kpi"><div class="kpi-label">Target platforms</div><div class="kpi-value" id="kpi-platforms">—</div><div class="kpi-sub">embedded + reference</div></div>
	<div class="kpi accent"><div class="kpi-label">Validation sessions</div><div class="kpi-value" id="kpi-validation">—</div><div class="kpi-sub">public on EdgeFirst Studio</div></div>
	</div>

	<!-- Tabs -->
	<nav class="tabs" id="tabs">
	<button data-tab="overview" class="active">Overview</button>
	<button data-tab="accuracy">Accuracy / Performance</button>
	<button data-tab="coverage">Platform Coverage</button>
	<button data-tab="studio">Validation Sessions</button>
	<button data-tab="roadmap">Progress</button>
	<button data-tab="leaderboard">Leaderboards</button>
	</nav>

	<!-- Overview -->
	<section class="tab-panel active" id="tab-overview">
	<div class="section">
	<div class="section-header"><h2>The EdgeFirst Model Zoo</h2>
	<span class="section-note">Multi-platform · multi-model · fully validated</span>
	</div>
	<p class="prose">
	The EdgeFirst Model Zoo is a <strong>multi-platform, multi-model</strong> collection of
	production-ready vision models with <strong>full profiling and validation results</strong> for
	every published artifact. Each model is trained, exported, quantized, and compiled in
	<a href="https://edgefirst.studio" target="_blank" rel="noopener">EdgeFirst Studio</a>, then
	validated <em>on the actual deployment hardware</em> — accuracy and timing are measured by the
	same pipeline that runs the deployed model, never a separate benchmark configuration.
	</p>
	<p class="prose">
	On-target measurement is performed by the
	<a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener">EdgeFirst Profiler</a>:
	it runs full inference on the device through each runtime's native delegate, captures per-image
	predictions as Parquet (inference results) and a Perfetto trace (<code>.pftrace</code>, detailed
	per-stage timing), and ships both to EdgeFirst Studio for processing and publishing. Every
	accuracy or timing number on this page links back to a public Studio validation session you can
	inspect yourself. The Profiler is built on our open-source stack, including the
	<a href="https://github.com/EdgeFirstAI/hal" target="_blank" rel="noopener">EdgeFirst HAL</a> —
	see <a href="https://github.com/EdgeFirstAI" target="_blank" rel="noopener">github.com/EdgeFirstAI</a>.
	</p>

	<h3>How the numbers are produced</h3>
	<div class="diagram-grid">
	<figure class="figure">
	<img src="01-ecosystem.png" alt="EdgeFirst ecosystem: Studio, Profiler, open-source stack and target hardware" loading="lazy" />
	<figcaption>The EdgeFirst ecosystem — Studio, Profiler, the open-source stack, and the target hardware they serve.</figcaption>
	</figure>
	<figure class="figure">
	<img src="02-model-lifecycle.png" alt="Model lifecycle: train, export, quantize, compile per platform, validate, publish" loading="lazy" />
	<figcaption>Model lifecycle — one training session fans out to ONNX, TFLite INT8, and platform-compiled artifacts, each validated before publishing.</figcaption>
	</figure>
	<figure class="figure">
	<img src="03-on-target-validation.png" alt="On-target validation: Profiler runs on device, ships Parquet results and pftrace to Studio" loading="lazy" />
	<figcaption>On-target validation — the Profiler runs on the device and ships Parquet inference results plus a detailed <code>.pftrace</code> to Studio for scoring and publication.</figcaption>
	</figure>
	</div>
	</div>

	<div class="section efpi-callout">
	<div class="section-header"><h2>EdgeFirst Perception Index Report</h2></div>
	<p class="prose">
	Want the full story behind these numbers? The <strong>EdgeFirst Perception Index (EFPI)</strong>
	is a comprehensive technical report covering how we collected these benchmarks and validation
	results, how the models were optimized for each platform, and our insights and analysis of the
	results across the embedded hardware landscape.
	</p>
	<p style="margin-top:0.75rem">
	<a class="btn-gold" href="https://www.au-zone.com/efpi" target="_blank" rel="noopener">Download the free report →</a>
	</p>
	</div>

	<div class="section">
	<div class="section-header"><h2>Model Matrix</h2>
	<span class="section-note">COCO val2017 · 640×640 · one repo per family × task</span>
	</div>
	<table class="dt" id="matrix-table">
	<thead>
	<tr>
	<th>Repo</th><th>Family</th><th>Task</th>
	<th>Sizes</th><th>Nano mAP<sub>50-95</sub> (ONNX)</th><th>Nano mAP<sub>50-95</sub> (INT8)</th>
	<th>Last modified</th>
	</tr>
	</thead>
	<tbody id="matrix-body"></tbody>
	</table>
	</div>

	<div class="section">
	<div class="section-header"><h2>Hardware Targets</h2>
	<span class="section-note">Platforms the zoo compiles models for</span>
	</div>
	<div id="platform-grid" style="display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:0.75rem"></div>
	</div>
	</section>

	<!-- Accuracy / Performance -->
	<section class="tab-panel" id="tab-accuracy">
	<div class="section">
	<div class="section-header"><h2>Nano Detection — mAP comparison</h2>
	<span class="section-note">COCO val2017, ONNX FP32 vs TFLite INT8</span>
	</div>
	<div class="chart-grid">
	<div class="chart-card">
	<h3>mAP@0.5-0.95 — ONNX FP32 vs TFLite INT8</h3>
	<div class="chart-host"><canvas id="chart-det-bar"></canvas></div>
	</div>
	<div class="chart-card">
	<h3>mAP@0.5-0.95 vs Compute (Nano GFLOPs)</h3>
	<div class="chart-host"><canvas id="chart-det-scatter"></canvas></div>
	</div>
	</div>
	</div>

	<div class="section">
	<div class="section-header"><h2>Nano Segmentation — Mask mAP</h2>
	<span class="section-note">YOLOv8 / YOLO11 / YOLO26, COCO val2017</span>
	</div>
	<div class="chart-grid">
	<div class="chart-card">
	<h3>Mask mAP@0.5-0.95 (ONNX vs INT8)</h3>
	<div class="chart-host"><canvas id="chart-seg-bar"></canvas></div>
	</div>
	<div class="chart-card">
	<h3>Detection mAP vs Mask mAP (ONNX FP32)</h3>
	<div class="chart-host"><canvas id="chart-seg-scatter"></canvas></div>
	</div>
	</div>
	</div>

	<div class="section">
	<div class="section-header"><h2>Accuracy vs On-target Throughput</h2>
	<span class="section-note">COCO val2017 · pipelined FPS measured on each platform by the <a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler</a></span>
	</div>
	<div class="chart-host" style="height:420px"><canvas id="chart-det-fps"></canvas></div>
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.82rem;margin-top:0.5rem">
	Each point is a public validation session: color = platform, shape = model family
	(● YOLOv5 · ■ YOLOv8 · ▲ YOLO11 · ◆ YOLO26). X axis is sustained pipelined throughput on a log
	scale — the full image load → decode → preprocess → inference → postprocess pipeline, not bare
	inference time. ⚠ flagged sessions are shown at their measured accuracy.
	</p>
	</div>

	<div class="section">
	<div class="section-header"><h2>Accuracy vs On-target Latency</h2>
	<span class="section-note">End-to-end per-image latency measured by the <a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler</a> on deployment hardware</span>
	</div>
	<div id="timing-host">
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.92rem">
	On-target latency results are being collected across the platform matrix.
	See the <a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler documentation</a>
	for how profiling and validation are performed.
	</p>
	</div>
	</div>
	</section>

	<!-- Studio Sessions -->
	<section class="tab-panel" id="tab-studio">
	<div class="section">
	<div class="section-header"><h2>Public Validation Sessions</h2>
	<span class="section-note" id="studio-source">every row links to its public session on <a href="https://edgefirst.studio" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Studio</a></span>
	</div>
	<div id="studio-empty" style="display:none">
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.92rem">
	Validation results are being refreshed — browse the public sessions directly on
	<a href="https://edgefirst.studio" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Studio</a>.
	</p>
	</div>
	<table class="dt" id="studio-table" style="display:none">
	<thead><tr>
	<th data-sort="session">Session</th>
	<th data-sort="family">Family · Task</th>
	<th data-sort="target">Target</th>
	<th data-sort="precision">Precision</th>
	<th data-sort="det50">Det mAP<sub>50</sub></th>
	<th data-sort="det5095">Det mAP<sub>50-95</sub></th>
	<th data-sort="mask">Mask mAP<sub>50-95</sub></th>
	<th data-sort="inf">Inf (ms)</th>
	<th data-sort="e2e">E2E (ms)</th>
	<th data-sort="fps">FPS</th>
	<th>Studio</th>
	</tr></thead>
	<tbody id="studio-body"></tbody>
	</table>
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.8rem;margin-top:0.75rem">
	⚠ Model accuracy on this platform is below our expectations — we are investigating the
	results to make improvements.
	</p>
	</div>
	</section>

	<!-- Coverage -->
	<section class="tab-panel" id="tab-coverage">
	<div class="section">
	<div class="section-header"><h2>Detection — Platform Coverage</h2>
	<span class="section-note">✓ = validated on-target with a public Studio session</span>
	</div>
	<div class="coverage-wrap"><table class="matrix" id="coverage-det"></table></div>
	</div>
	<div class="section">
	<div class="section-header"><h2>Instance Segmentation — Platform Coverage</h2>
	<span class="section-note">✓ = validated on-target with a public Studio session</span>
	</div>
	<div class="coverage-wrap"><table class="matrix" id="coverage-seg"></table></div>
	</div>
	<div class="legend">
	<span><span class="swatch" style="background:var(--good-bg);border:1px solid var(--good)"></span>Validated — click ✓ to open the proving session on EdgeFirst Studio</span>
	<span><span class="swatch" style="background:transparent;border:1px solid var(--border)"></span>Validation pending</span>
	</div>
	</section>

	<!-- Progress -->
	<section class="tab-panel" id="tab-roadmap">
	<div class="section">
	<div class="section-header"><h2>Model Family Progress</h2>
	<span class="section-note">Ultralytics YOLO families — published repos & on-target validation coverage</span>
	</div>
	<div class="phase-grid" id="phase-grid"></div>
	<p class="prose" style="margin-top:1rem;color:var(--text-muted);font-size:0.85rem">
	Additional model families and platforms are added to the zoo as their on-target
	validation completes.
	</p>
	</div>
	</section>

	<!-- Leaderboard -->
	<section class="tab-panel" id="tab-leaderboard">
	<div class="section">
	<div class="section-header"><h2>Platform Throughput Leaderboard</h2>
	<div class="lb-filter" id="fps-filter">
	<button data-group="all" class="active">All platforms</button>
	<button data-group="embedded">Embedded (i.MX · ARA240 · Hailo · Jetson)</button>
	<button data-group="reference">Desktop reference</button>
	</div>
	</div>
	<p class="section-note" style="margin-bottom:0.75rem">Top 20 fastest validated model × platform results · full image load → decode → preprocess → inference → postprocess pipeline</p>
	<table class="dt">
	<thead><tr>
	<th style="width:3rem">#</th>
	<th>Platform</th>
	<th>Fastest model</th>
	<th style="text-align:right">FPS (median)</th>
	<th style="text-align:right">E2E latency (ms)</th>
	</tr></thead>
	<tbody id="platform-fps-body"></tbody>
	</table>
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.8rem;margin-top:0.75rem">
	Throughput is the measured pipelined rate — stages overlap across frames, so it exceeds
	1000 ÷ end-to-end latency. Measured by the
	<a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler</a>.
	</p>
	</div>

	<div class="section">
	<div class="section-header"><h2>Per-platform Leaderboards</h2>
	<div class="lb-filter" id="lb-filter">
	<button data-group="all" class="active">All platforms</button>
	<button data-group="embedded">Embedded (i.MX · ARA240 · Hailo · Jetson)</button>
	<button data-group="reference">Desktop reference</button>
	</div>
	<div class="lb-filter" id="lb-metric">
	<button data-metric="map" class="active">Accuracy — mAP@0.5-0.95</button>
	<button data-metric="fps">Throughput — FPS</button>
	</div>
	</div>
	<div class="leaderboard-grid" id="leaderboard-grid"></div>
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.85rem;margin-top:1rem">
	Ranked by COCO val2017 mAP@0.5-0.95 as measured on each platform by the
	<a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Profiler</a>.
	Each entry links to its public validation session on EdgeFirst Studio.
	</p>
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.85rem;margin-top:0.5rem">
	⚠ Model accuracy on this platform is below our expectations — we are investigating the results to make improvements.
	</p>
	</div>
	</section>

	<footer>
	EdgeFirst Model Zoo · validated on deployment hardware with the
	<a href="https://doc.edgefirst.ai/latest/profiler/" target="_blank" rel="noopener">EdgeFirst Profiler</a> ·
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engines.","universal":false}],"sizes":[{"key":"n","label":"Nano"},{"key":"s","label":"Small"},{"key":"m","label":"Medium"},{"key":"l","label":"Large"},{"key":"x","label":"XLarge"}],"model_params":{"yolov5":{"n":{"params":"1.9M","gflops":4.5},"s":{"params":"7.2M","gflops":16.5},"m":{"params":"21.2M","gflops":49.0},"l":{"params":"46.5M","gflops":109.1},"x":{"params":"86.7M","gflops":205.7}},"yolov8":{"n":{"params":"3.2M","gflops":8.9},"s":{"params":"11.2M","gflops":28.8},"m":{"params":"25.9M","gflops":79.3},"l":{"params":"43.7M","gflops":165.7},"x":{"params":"68.2M","gflops":258.5}},"yolo11":{"n":{"params":"2.6M","gflops":6.5},"s":{"params":"9.4M","gflops":21.5},"m":{"params":"20.1M","gflops":68.0},"l":{"params":"25.3M","gflops":87.6},"x":{"params":"56.9M","gflops":195.0}},"yolo26":{"n":{"params":"2.7M","gflops":7.6},"s":{"params":"10.3M","gflops":27.0},"m":{"params":"24.5M","gflops":74.4},"l":{"params":"42.5M","gflops":155.0},"x":{"params":"67.5M","gflops":244.0}}},"det_bench":{"yolov5":{"n":{"onnx_fp32_map50":49.6,"onnx_fp32_map50_95":33.0,"tflite_int8_map50":46.2,"tflite_int8_map50_95":30.2},"s":{"onnx_fp32_map50":57.1,"onnx_fp32_map50_95":41.3,"tflite_int8_map50":46.4,"tflite_int8_map50_95":31.8},"m":{"onnx_fp32_map50":63.3,"onnx_fp32_map50_95":47.3,"tflite_int8_map50":50.9,"tflite_int8_map50_95":35.8}},"yolov8":{"n":{"onnx_fp32_map50":50.2,"onnx_fp32_map50_95":35.8,"tflite_int8_map50":47.3,"tflite_int8_map50_95":32.5}},"yolo11":{"n":{"onnx_fp32_map50":53.4,"onnx_fp32_map50_95":37.9,"tflite_int8_map50":50.1,"tflite_int8_map50_95":34.5}},"yolo26":{"n":{"onnx_fp32_map50":54.9,"onnx_fp32_map50_95":39.7,"tflite_int8_map50":51.5,"tflite_int8_map50_95":34.9}}},"seg_bench":{"yolov8":{"n":{"onnx_det_map50_95":35.3,"onnx_mask_map50_95":34.1},"m":{"onnx_det_map50_95":48.2},"l":{"onnx_det_map50_95":51.0},"x":{"onnx_det_map50_95":52.3}},"yolo11":{"n":{"onnx_det_map50_95":37.2,"onnx_mask_map50_95":35.5},"s":{"onnx_det_map50_95":44.8},"m":{"onnx_det_map50_95":50.0}},"yolo26":{"n":{"onnx_det_map50_95":39.5,"onnx_mask_map50_95":37.0},"s":{"onnx_det_map50_95":47.0},"m":{"onnx_det_map50_95":52.2},"l":{"onnx_det_map50_95":54.1},"x":{"onnx_det_map50_95":56.3}}},"validation":{},"studio_exp_urls":{"yolov5":{"det":"https://edgefirst.studio/public/projects/1684/experiment/training/list?exp_id=2113"},"yolov8":{"det":"https://edgefirst.studio/public/projects/1684/experiment/training/list?exp_id=2114","seg":"https://edgefirst.studio/public/projects/1685/experiment/training/list?exp_id=2181"},"yolo11":{"det":"https://edgefirst.studio/public/projects/1684/experiment/training/list?exp_id=2115","seg":"https://edgefirst.studio/public/projects/1685/experiment/training/list?exp_id=2182"},"yolo26":{"det":"https://edgefirst.studio/public/projects/1684/experiment/training/list?exp_id=2116","seg":"https://edgefirst.studio/public/projects/1685/experiment/training/list?exp_id=2183"}},"hardware_badges":["x86_64 \| Linux","aarch64 \| Linux","Apple \| macOS","NXP i.MX 8M Plus","NXP i.MX 93","NXP i.MX 95","NXP ARA240","RPi5 + Hailo-8/8L","NVIDIA Jetson"],"studio_server":"https://edgefirst.studio","unsupported_platforms":[{"version_key":"yolo26","task_key":"seg","platform_key":"ara240"},{"version_key":"yolo26","task_key":"seg","platform_key":"imx95"},{"version_key":"yolo11","task_key":"seg","platform_key":"imx95"},{"version_key":"yolo26","task_key":"det","platform_key":"ara240"},{"version_key":"yolo26","task_key":"det","platform_key":"imx95"},{"version_key":"yolo11","task_key":"det","platform_key":"ara240"},{"version_key":"yolo11","task_key":"det","platform_key":"imx95"},{"version_key":"yolo11","task_key":"seg","platform_key":"ara240"}]}</script>
	<script>
	"use strict";

	// ---------------------------------------------------------------------------
	// Constants
	// ---------------------------------------------------------------------------

	// Platforms as rows: hosts (reference) first, then embedded targets, then Jetson.
	// `match` is tested (in order) against the Studio target/footer string to map a
	// validation session onto a platform row.
	// The three CoreML compute units are separate accelerators on one host —
	// each gets its own coverage row, but only the ANE (the fastest) is ranked
	// in leaderboards; CPU/GPU numbers remain in the validation sessions table.
	const PLATFORM_ROWS = [
	{ key: "linux-x86", label: "Linux x86_64", detail: "ONNX Runtime + CUDA", group: "reference", color: "#6C757D", match: /linux\|x86\|cuda/ },
	{ key: "aarch64", label: "Linux aarch64", detail: "ONNX Runtime / TFLite", group: "reference", color: "#718096", match: /aarch64\|arm64(?!.*hailo)/ },
	{ key: "m2max-ane", label: "Apple M2 Max", detail: "ONNX + CoreML ANE", group: "reference", color: "#4B0082", match: /coreml-ane/ },
	{ key: "m2max-gpu", label: "Apple M2 Max", detail: "ONNX + CoreML GPU", group: "reference", color: "#7A4FB3", match: /coreml-gpu/, leaderboard: false },
	{ key: "m2max-cpu", label: "Apple M2 Max", detail: "ONNX + CoreML CPU", group: "reference", color: "#A98FD0", match: /coreml-cpu\|macos\|coreml/, leaderboard: false },
	{ key: "imx8mp", label: "NXP i.MX 8M Plus", detail: "VSI NPU · TFLite INT8", group: "embedded", color: "#E8B820", match: /imx8mp/ },
	{ key: "imx93", label: "NXP i.MX 93", detail: "Ethos-U65 · VELA TFLite", group: "embedded", color: "#8B7DC4", match: /imx93/ },
	{ key: "imx95", label: "NXP i.MX 95", detail: "eIQ Neutron · TFLite INT8", group: "embedded", color: "#1FA0A8", match: /imx95(?!-ara)/ },
	{ key: "ara240", label: "NXP ARA240", detail: "Kinara DVM", group: "embedded", color: "#5BB8F5", match: /ara240\|kinara\|\.dvm/ },
	{ key: "hailo10", label: "Hailo-10", detail: "HailoRT HEF · 40 TOPS", group: "embedded", color: "#8C2B28", match: /hailo10/ },
	{ key: "hailo8l", label: "Raspberry Pi 5 + Hailo-8L", detail: "HailoRT HEF · 13 TOPS", group: "embedded", color: "#D9534F", match: /hailo8l\|rpi5/ },
	{ key: "hailo8", label: "Hailo-8", detail: "HailoRT HEF · 26 TOPS", group: "embedded", color: "#B0413E", match: /hailo8/ },
	{ key: "orin", label: "NVIDIA Jetson Orin Nano", detail: "TensorRT FP16", group: "embedded", color: "#2E8B57", match: /orin\|tensorrt\|jetson\|\.engine/ },
	];

	// Point shapes per model family for the accuracy-vs-throughput chart
	// (color = platform, shape = family — mirrors the EFPI report figures).
	const FAMILY_SHAPES = { yolov5: "circle", yolov8: "rect", yolo11: "triangle", yolo26: "rectRot" };

	function platformRowForSession(s) {
	const t = `${s.target_raw \|\| ""} ${s.target_key \|\| ""} ${s.target_label \|\| ""}`.toLowerCase();
	for (const p of PLATFORM_ROWS) if (p.match.test(t)) return p;
	return null;
	}

	// Publication rule: a quantized/compiled result more than 10 percentage points
	// below the float reference (AP50) of the same model is published with its
	// measured numbers plus a ⚠ marker — never suppressed. References are the
	// host-platform ONNX runs (Linux x86_64 / Apple CoreML), not compiled engines.
	const FLAG_FOOTNOTE = "⚠ Model accuracy on this platform is below our expectations — we are investigating the results to make improvements.";

	let _refAp50 = null;
	function refAp50() {
	if (_refAp50) return _refAp50;
	_refAp50 = {};
	for (const s of (STUDIO?.sessions ?? [])) {
	const p = platformRowForSession(s);
	if (!p \|\| p.group !== "reference" \|\| p.key === "orin" \|\| s.det_map_50 == null) continue;
	const k = `${s.task_key}\|${s.version_key}\|${s.size}`;
	if (_refAp50[k] == null \|\| s.det_map_50 > _refAp50[k]) _refAp50[k] = s.det_map_50;
	}
	return _refAp50;
	}

	function isBelowExpectations(s) {
	// Prefer the officially graded flag from hf_publisher.status (10pp rule
	// against the ONNX reference of the same training session).
	if (s.flag !== undefined) return s.flag === "FLAG";
	const p = platformRowForSession(s);
	if (p && p.group === "reference") return false;
	const r = refAp50()[`${s.task_key}\|${s.version_key}\|${s.size}`];
	return r != null && s.det_map_50 != null && (r - s.det_map_50) > 10;
	}

	const COLORS = {
	yolov5: "#8B7DC4",
	yolov8: "#3E3371",
	yolo11: "#1FA0A8",
	yolo26: "#E8B820",
	};

	const VERSION_ORDER = ["yolov5", "yolov8", "yolo11", "yolo26"];

	const SNAPSHOT = JSON.parse(document.getElementById("snapshot-data").textContent);

	// Studio metrics snapshot — may be null if dashboard_snapshot.py hasn't been run.
	let STUDIO = null;
	try {
	const raw = document.getElementById("studio-data")?.textContent?.trim();
	if (raw && raw !== "null") STUDIO = JSON.parse(raw);
	} catch (e) { console.warn("Studio snapshot parse failed:", e); }

	// Platforms explicitly marked unsupported for specific model+task combinations.
	// Sessions for these combos are filtered from the Studio JSON at build time,
	// so they never reach any chart or leaderboard. The coverage table uses this
	// set to show "N/S" in place of the usual "—" for those cells.
	const UNSUPPORTED_CELLS = new Set(
	(SNAPSHOT.unsupported_platforms ?? []).map(u => `${u.task_key}\|${u.version_key}\|${u.platform_key}`)
	);

	// State: liveByRepo[repo_id] = { downloads, downloadsAllTime, likes, lastModified, createdAt, modelIndex }
	const live = {};

	// ---------------------------------------------------------------------------
	// Helpers
	// ---------------------------------------------------------------------------

	function el(tag, attrs = {}, children = []) {
	const e = document.createElement(tag);
	for (const [k, v] of Object.entries(attrs)) {
	if (k === "class") e.className = v;
	else if (k === "html") e.innerHTML = v;
	else if (k === "text") e.textContent = v;
	else if (k.startsWith("data-")) e.setAttribute(k, v);
	else e[k] = v;
	}
	for (const c of children) {
	if (c == null) continue;
	if (typeof c === "string") e.appendChild(document.createTextNode(c));
	else e.appendChild(c);
	}
	return e;
	}

	function fmt(n) {
	if (n == null) return "—";
	if (typeof n !== "number") return String(n);
	if (n >= 1e6) return (n / 1e6).toFixed(1) + "M";
	if (n >= 1e3) return (n / 1e3).toFixed(1) + "k";
	return String(n);
	}

	function fmtDate(d) {
	if (!d) return "—";
	try {
	const date = new Date(d);
	return date.toISOString().slice(0, 10);
	} catch (e) { return String(d); }
	}

	function tasksByVersion(matrix) {
	const out = {};
	for (const m of matrix) {
	out[m.version_key] \|\|= [];
	out[m.version_key].push(m);
	}
	return out;
	}

	// ---------------------------------------------------------------------------
	// Initial render from snapshot (so the page is meaningful before live data)
	// ---------------------------------------------------------------------------

	function renderSnapshotDate() {
	document.getElementById("snapshot-date").textContent = SNAPSHOT.generated_at;
	}

	function renderKpisFromSnapshot() {
	document.getElementById("kpi-repos").textContent = SNAPSHOT.matrix.length;
	document.getElementById("kpi-families").textContent =
	new Set(SNAPSHOT.matrix.map(m => m.version_key)).size;
	document.getElementById("kpi-tasks").textContent =
	new Set(SNAPSHOT.matrix.map(m => m.task_key)).size;
	document.getElementById("kpi-platforms").textContent = PLATFORM_ROWS.length;
	document.getElementById("kpi-validation").textContent =
	STUDIO?.counts?.sessions_ok ?? STUDIO?.sessions?.length ?? "—";
	}

	function renderMatrixTable() {
	const tbody = document.getElementById("matrix-body");
	tbody.innerHTML = "";
	for (const m of SNAPSHOT.matrix) {
	const bench = m.task_key === "det"
	? (SNAPSHOT.det_bench[m.version_key]?.n)
	: (SNAPSHOT.seg_bench[m.version_key]?.n);
	const onnxMap50 = m.task_key === "det"
	? (bench?.onnx_fp32_map50_95 ?? null)
	: (bench?.onnx_mask_map50_95 ?? null);
	const intMap5095 = m.task_key === "det"
	? bench?.tflite_int8_map50_95
	: bench?.int8_mask_map50_95;
	const exportNotes = m.export_notes ? ` · <span style="color:var(--text-muted);font-size:0.75rem">${m.export_notes}</span>` : "";
	const tr = el("tr", {}, []);
	tr.innerHTML = `
	<td><a href="https://huggingface.co/${m.repo_id}" target="_blank" rel="noopener"><span class="repo-name">${m.repo_id}</span></a>${exportNotes}</td>
	<td>${m.version_label}</td>
	<td>${m.task_label}</td>
	<td class="num">${m.sizes.length}</td>
	<td class="num">${onnxMap50 != null ? onnxMap50.toFixed(1) : "—"}</td>
	<td class="num">${intMap5095 != null ? intMap5095.toFixed(1) : "—"}</td>
	<td class="num" id="lm-${m.repo_id.replace(/[/]/g, "_")}"><span class="skeleton" style="width:60px"></span></td>
	`;
	tbody.appendChild(tr);
	}
	}

	function renderPlatformGrid() {
	const grid = document.getElementById("platform-grid");
	grid.innerHTML = "";
	for (const p of SNAPSHOT.platforms) {
	const card = el("div", { class: "phase-card" });
	card.innerHTML = `
	<div class="phase-title">${p.label}</div>
	<div style="font-family:var(--mono);font-size:0.72rem;color:var(--text-muted);margin-bottom:0.35rem">
	<span class="pill ${p.universal ? 'pill-onnx' : 'pill-target'}">${p.format_label}</span>
	</div>
	<div style="font-family:var(--serif);font-size:0.8rem;color:var(--text-muted)">${p.notes}</div>
	`;
	grid.appendChild(card);
	}
	}

	// Sizes shown as sub-columns per version supercolumn (kept to n/s/m so the
	// tables stay readable; larger sizes are browsable per-repo and in Studio).
	const COVERAGE_SIZES = ["n", "s", "m"];
	const SIZE_NAMES = { n: "Nano", s: "Small", m: "Medium" };

	// Build a lookup of validated (task, version, size, platform) cells from the
	// embedded Studio sessions. Each cell keeps the best proving session (smart
	// decoder preferred, then highest mAP) so the ✓ can link to it.
	function coverageLookup() {
	const seen = {};
	for (const s of (STUDIO?.sessions ?? [])) {
	const p = platformRowForSession(s);
	if (!p \|\| !s.has_metrics) continue;
	const key = `${s.task_key}\|${s.version_key}\|${s.size}\|${p.key}`;
	const metric = s.det_map_50_95 ?? s.mask_map_50_95 ?? 0;
	const isSmart = isSmartSession(s);
	const cur = seen[key];
	if (!cur \|\| (isSmart && !cur.isSmart) \|\| (isSmart === cur.isSmart && metric > cur.metric)) {
	seen[key] = { s, metric, isSmart };
	}
	}
	return seen;
	}

	function renderCoverageTable(taskKey, tableId, seen) {
	const table = document.getElementById(tableId);
	table.innerHTML = "";
	const versions = VERSION_ORDER.filter(v =>
	SNAPSHOT.matrix.some(m => m.version_key === v && m.task_key === taskKey));

	const head = el("thead");
	const top = el("tr");
	top.appendChild(el("th", { class: "row-h", text: "Platform" }));
	for (const v of versions) {
	const th = el("th", { class: "supercol", text: v.toUpperCase() });
	th.colSpan = COVERAGE_SIZES.length;
	top.appendChild(th);
	}
	const sub = el("tr");
	sub.appendChild(el("th", { class: "row-h", text: "" }));
	for (const v of versions) {
	for (const sz of COVERAGE_SIZES) {
	sub.appendChild(el("th", { text: SIZE_NAMES[sz] }));
	}
	}
	head.appendChild(top);
	head.appendChild(sub);
	table.appendChild(head);

	const body = el("tbody");
	for (const p of PLATFORM_ROWS) {
	const row = el("tr");
	row.appendChild(el("td", {
	class: "row-h",
	html: `<div>${p.label}</div><div style="color:var(--text-muted);font-size:0.62rem">${p.detail}</div>`,
	}));
	for (const v of versions) {
	for (const sz of COVERAGE_SIZES) {
	const td = el("td");
	const hit = seen[`${taskKey}\|${v}\|${sz}\|${p.key}`];
	if (hit) {
	// Build via DOM APIs with scheme validation — snapshot data is
	// trusted at build time, but this page is public; stay defensive.
	const a = el("a", { class: "cell cell-val", target: "_blank", rel: "noopener" });
	let safeUrl = "#";
	try {
	const u = new URL(hit.s.url);
	if (u.protocol === "https:" \|\| u.protocol === "http:") safeUrl = u.href;
	} catch (e) { /* keep '#' */ }
	a.href = safeUrl;
	a.setAttribute("title", `Open validation session ${hit.s.session_id} on EdgeFirst Studio`);
	a.textContent = "✓";
	td.appendChild(a);
	} else if (UNSUPPORTED_CELLS.has(`${taskKey}\|${v}\|${p.key}`)) {
	td.innerHTML = '<span class="cell cell-unsupported" title="Not supported on this platform">N/S</span>';
	} else {
	td.innerHTML = '<span class="cell cell-none">—</span>';
	}
	row.appendChild(td);
	}
	}
	body.appendChild(row);
	}
	table.appendChild(body);
	}

	function renderCoverageMatrix() {
	const seen = coverageLookup();
	renderCoverageTable("det", "coverage-det", seen);
	renderCoverageTable("seg", "coverage-seg", seen);
	}

	// Progress is published per Ultralytics YOLO family: which task repos are
	// live, and how much of the platform matrix each family has validated.
	function renderRoadmap() {
	const grid = document.getElementById("phase-grid");
	grid.innerHTML = "";
	const seen = coverageLookup();

	for (const v of VERSION_ORDER) {
	const entries = SNAPSHOT.matrix.filter(m => m.version_key === v);
	if (entries.length === 0) continue;
	const label = entries[0].version_label;

	// Validated platform coverage for this family across its tasks.
	let covered = 0, possible = 0;
	for (const m of entries) {
	for (const p of PLATFORM_ROWS) {
	for (const sz of COVERAGE_SIZES) {
	possible += 1;
	if (seen[`${m.task_key}\|${v}\|${sz}\|${p.key}`]) covered += 1;
	}
	}
	}
	const pct = possible > 0 ? Math.round(100 * covered / possible) : 0;

	const items = entries.map(m =>
	`<li><span class="dot published"></span><a href="https://huggingface.co/${m.repo_id}" target="_blank" rel="noopener">${m.task_label} — <span style="font-family:var(--mono);font-size:0.92em">${m.repo_id}</span></a></li>`
	).join("");

	const card = el("div", { class: "phase-card" });
	card.innerHTML = `
	<div class="phase-title">${label}</div>
	<div class="phase-summary">Published · on-target validation rolling out across the platform matrix.</div>
	<div class="phase-progress"><div class="phase-progress-bar" style="width:${pct}%"></div></div>
	<div style="font-family:var(--mono);font-size:0.72rem;color:var(--text-muted);margin-bottom:0.5rem">${covered} / ${possible} platform × size cells validated · ${pct}%</div>
	<ul class="phase-items">${items}</ul>
	`;
	grid.appendChild(card);
	}
	}

	// Per-platform leaderboards built from measured Studio sessions, with a
	// power-class filter (embedded accelerators vs desktop reference) and a
	// metric toggle (accuracy mAP vs pipelined throughput FPS).
	let lbGroup = "all";
	let lbMetric = "map";

	function sessionModelName(s) {
	return `${s.version_key.toUpperCase()}${(s.size \|\| "").toUpperCase()}${s.task_key === "seg" ? "-Seg" : ""}`;
	}

	function isSmartSession(s) {
	return s.decoder_variant === "smart" \|\| /_smart/.test(s.target_raw \|\| "");
	}

	function leaderboardEntries(platformKey, metricKey) {
	// Best session per (task, version, size); smart decoder preferred over logical.
	const best = {};
	for (const s of (STUDIO?.sessions ?? [])) {
	const p = platformRowForSession(s);
	if (!p \|\| p.key !== platformKey \|\| !s.has_metrics) continue;
	const metric = metricKey === "fps"
	? s.fps_median
	: (s.det_map_50_95 ?? s.mask_map_50_95);
	if (metric == null) continue;
	const key = `${s.task_key}\|${s.version_key}\|${s.size}`;
	const isSmart = isSmartSession(s);
	const cur = best[key];
	if (!cur \|\| (isSmart && !cur.isSmart) \|\| (isSmart === cur.isSmart && metric > cur.metric)) {
	best[key] = { s, metric, isSmart };
	}
	}
	return Object.values(best)
	.sort((a, b) => b.metric - a.metric)
	.map(({ s, metric }) => ({
	name: sessionModelName(s),
	metric,
	url: s.url,
	sub: metricKey === "fps"
	? (s.det_map_50_95 != null ? `${s.det_map_50_95.toFixed(1)} mAP` : null)
	: (s.fps_median != null ? `${s.fps_median.toFixed(0)} FPS` : null),
	flagged: isBelowExpectations(s),
	}));
	}

	// Cross-platform throughput board: the top 20 fastest validated
	// model × platform results by pipelined FPS (accuracy intentionally not
	// ranked here; smart decoder preferred over logical duplicates).
	let fpsBoardGroup = "all";

	function renderPlatformThroughput() {
	const host = document.getElementById("platform-fps-body");
	if (!host) return;
	host.innerHTML = "";
	const best = {};
	for (const s of (STUDIO?.sessions ?? [])) {
	const p = platformRowForSession(s);
	if (!p \|\| p.leaderboard === false \|\| s.fps_median == null) continue;
	if (fpsBoardGroup !== "all" && p.group !== fpsBoardGroup) continue;
	const key = `${p.key}\|${s.task_key}\|${s.version_key}\|${s.size}`;
	const isSmart = isSmartSession(s);
	const cur = best[key];
	if (!cur \|\| (isSmart && !cur.isSmart) \|\| (isSmart === cur.isSmart && s.fps_median > cur.s.fps_median)) {
	best[key] = { p, s, isSmart };
	}
	}
	const rows = Object.values(best)
	.sort((a, b) => b.s.fps_median - a.s.fps_median)
	.slice(0, 20);
	for (let i = 0; i < rows.length; i++) {
	const { p, s } = rows[i];
	const tr = el("tr");
	tr.innerHTML = `
	<td class="num">${i + 1}</td>
	<td><strong>${p.label}</strong> <span class="lb-sub">${p.detail}</span></td>
	<td><a href="${s.url}" target="_blank" rel="noopener"><span class="repo-name">${sessionModelName(s)}</span></a></td>
	<td class="num">${s.fps_median.toFixed(0)}</td>
	<td class="num">${s.e2e_latency_ms != null ? s.e2e_latency_ms.toFixed(1) : "—"}</td>
	`;
	host.appendChild(tr);
	}
	}

	function renderLeaderboard() {
	const grid = document.getElementById("leaderboard-grid");
	grid.innerHTML = "";
	const platforms = PLATFORM_ROWS.filter(p =>
	p.leaderboard !== false && (lbGroup === "all" \|\| p.group === lbGroup));
	const metricLabel = lbMetric === "fps"
	? "Pipelined throughput FPS (higher is better)"
	: "mAP@0.5-0.95 (higher is better)";

	for (const p of platforms) {
	const rows = leaderboardEntries(p.key, lbMetric);
	const card = el("div", { class: "leaderboard-card" });
	card.innerHTML = `<h3>${p.label} <span class="meta">${p.detail}</span></h3>`;
	if (rows.length === 0) {
	card.appendChild(el("div", { class: "lb-empty", text: "On-target validation pending." }));
	} else {
	for (let i = 0; i < rows.length; i++) {
	const r = rows[i];
	const row = el("div", { class: "lb-row" });
	const sub = r.sub ? ` <span class="lb-sub">· ${r.sub}</span>` : "";
	const flag = r.flagged ? ` <span title="${FLAG_FOOTNOTE}">⚠</span>` : "";
	row.innerHTML = `
	<span class="lb-rank">${i + 1}.</span>
	<span class="lb-name"><a href="${r.url}" target="_blank" rel="noopener" style="color:inherit">${r.name}</a>${sub}</span>
	<span class="lb-metric">${lbMetric === "fps" ? r.metric.toFixed(0) : r.metric.toFixed(1)}${flag}</span>
	`;
	card.appendChild(row);
	}
	card.appendChild(el("div", { class: "lb-empty", text: metricLabel }));
	}
	grid.appendChild(card);
	}
	}

	function initLeaderboardFilter() {
	const groupHost = document.getElementById("lb-filter");
	if (groupHost) {
	groupHost.querySelectorAll("button").forEach(b => b.addEventListener("click", () => {
	groupHost.querySelectorAll("button").forEach(x => x.classList.remove("active"));
	b.classList.add("active");
	lbGroup = b.dataset.group;
	renderLeaderboard();
	}));
	}
	const metricHost = document.getElementById("lb-metric");
	if (metricHost) {
	metricHost.querySelectorAll("button").forEach(b => b.addEventListener("click", () => {
	metricHost.querySelectorAll("button").forEach(x => x.classList.remove("active"));
	b.classList.add("active");
	lbMetric = b.dataset.metric;
	renderLeaderboard();
	}));
	}
	const fpsHost = document.getElementById("fps-filter");
	if (fpsHost) {
	fpsHost.querySelectorAll("button").forEach(b => b.addEventListener("click", () => {
	fpsHost.querySelectorAll("button").forEach(x => x.classList.remove("active"));
	b.classList.add("active");
	fpsBoardGroup = b.dataset.group;
	renderPlatformThroughput();
	}));
	}
	}

	// ---------------------------------------------------------------------------
	// Charts (Chart.js)
	// ---------------------------------------------------------------------------

	let charts = {};

	function destroyChart(name) {
	if (charts[name]) { charts[name].destroy(); delete charts[name]; }
	}

	function chartOpts(scaleOverrides) {
	return {
	responsive: true, maintainAspectRatio: false,
	plugins: {
	legend: { display: false },
	tooltip: { titleFont: { family: "var(--mono)" }, bodyFont: { family: "var(--mono)" } },
	},
	scales: {
	x: { ticks: { font: { family: "monospace", size: 10 } }, grid: { display: false } },
	y: { beginAtZero: true, ticks: { font: { family: "monospace", size: 10 } },
	...(scaleOverrides?.y ?? {}) },
	},
	};
	}

	function chartDetectionBar() {
	const versions = VERSION_ORDER.filter(v => SNAPSHOT.det_bench[v]?.n);
	const labels = versions.map(v => v.toUpperCase());
	const onnx = versions.map(v => SNAPSHOT.det_bench[v].n.onnx_fp32_map50_95);
	const int8 = versions.map(v => SNAPSHOT.det_bench[v].n.tflite_int8_map50_95);

	destroyChart("detBar");
	charts.detBar = new Chart(document.getElementById("chart-det-bar"), {
	type: "bar",
	data: { labels, datasets: [
	{ label: "ONNX FP32", data: onnx, backgroundColor: "rgba(31,160,168,0.85)", borderRadius: 3 },
	{ label: "TFLite INT8", data: int8, backgroundColor: "rgba(232,184,32,0.85)", borderRadius: 3 },
	]},
	options: {
	...chartOpts({ y: { title: { display: true, text: "mAP@0.5-0.95 (%)" }, min: 25, max: 45 } }),
	plugins: { legend: { display: true, position: "bottom" } },
	},
	});
	}

	// Accuracy vs measured pipelined throughput — color by platform, shape by
	// family, log-scale X (mirrors the EFPI report's throughput figure).
	function chartDetFps() {
	const canvas = document.getElementById("chart-det-fps");
	if (!canvas) return;
	const usable = (STUDIO?.sessions ?? []).filter(s =>
	s.task_key === "det" && s.fps_median > 0 && s.det_map_50_95 != null);
	if (usable.length === 0) return;

	const byPlatform = {};
	for (const s of usable) {
	const p = platformRowForSession(s);
	if (!p) continue;
	(byPlatform[p.key] \|\|= []).push(s);
	}

	const datasets = PLATFORM_ROWS.filter(p => byPlatform[p.key]).map(p => {
	const pts = byPlatform[p.key];
	return {
	label: `${p.label} ${p.detail.includes("CoreML") ? p.detail.replace("ONNX + ", "") : ""}`.trim(),
	data: pts.map(s => ({
	x: s.fps_median,
	y: s.det_map_50_95,
	name: sessionModelName(s),
	flagged: isBelowExpectations(s),
	})),
	backgroundColor: p.color,
	borderColor: p.color,
	pointStyle: pts.map(s => FAMILY_SHAPES[s.version_key] ?? "circle"),
	pointRadius: 7, pointHoverRadius: 10,
	};
	});

	destroyChart("detFps");
	charts.detFps = new Chart(canvas, {
	type: "scatter",
	data: { datasets },
	options: {
	responsive: true, maintainAspectRatio: false,
	plugins: {
	legend: { display: true, position: "bottom" },
	tooltip: {
	callbacks: {
	label: (ctx) => `${ctx.raw.name} on ${ctx.dataset.label}: ${ctx.parsed.y.toFixed(1)} mAP @ ${ctx.parsed.x.toFixed(0)} FPS${ctx.raw.flagged ? " ⚠" : ""}`,
	},
	},
	},
	scales: {
	x: { type: "logarithmic",
	title: { display: true, text: "Sustained pipelined throughput (FPS, log scale)" },
	ticks: { font: { family: "monospace", size: 10 },
	callback: (v) => ([1,2,5,10,20,50,100,200,500,1000].includes(v) ? String(v) : null) } },
	y: { title: { display: true, text: "mAP@0.5-0.95 (%)" }, beginAtZero: false,
	ticks: { font: { family: "monospace", size: 10 } } },
	},
	},
	});
	}

	function chartDetectionScatter() {
	const data = VERSION_ORDER.filter(v => SNAPSHOT.det_bench[v]?.n).map(v => ({
	label: v.toUpperCase(),
	data: [{
	x: SNAPSHOT.model_params[v]?.n?.gflops,
	y: SNAPSHOT.det_bench[v].n.onnx_fp32_map50_95,
	}],
	backgroundColor: COLORS[v],
	borderColor: COLORS[v],
	pointRadius: 9, pointHoverRadius: 11,
	}));

	destroyChart("detScatter");
	charts.detScatter = new Chart(document.getElementById("chart-det-scatter"), {
	type: "scatter",
	data: { datasets: data },
	options: {
	responsive: true, maintainAspectRatio: false,
	plugins: {
	legend: { display: true, position: "bottom" },
	tooltip: {
	callbacks: {
	label: (ctx) => `${ctx.dataset.label}: ${ctx.parsed.y}% mAP @ ${ctx.parsed.x} GFLOPs`,
	},
	},
	},
	scales: {
	x: { title: { display: true, text: "Nano GFLOPs (lower = faster)" }, beginAtZero: true,
	ticks: { font: { family: "monospace", size: 10 } } },
	y: { title: { display: true, text: "mAP@0.5-0.95 (ONNX FP32)" }, beginAtZero: false, min: 30, max: 45,
	ticks: { font: { family: "monospace", size: 10 } } },
	},
	},
	});
	}

	function chartSegBar() {
	const versions = ["yolov8", "yolo11", "yolo26"].filter(v => SNAPSHOT.seg_bench[v]?.n);
	const labels = versions.map(v => v.toUpperCase());
	const onnx = versions.map(v => SNAPSHOT.seg_bench[v].n.onnx_mask_map50_95);
	const int8 = versions.map(v => SNAPSHOT.seg_bench[v].n.int8_mask_map50_95);

	destroyChart("segBar");
	charts.segBar = new Chart(document.getElementById("chart-seg-bar"), {
	type: "bar",
	data: { labels, datasets: [
	{ label: "ONNX FP32", data: onnx, backgroundColor: "rgba(31,160,168,0.85)", borderRadius: 3 },
	{ label: "TFLite INT8", data: int8, backgroundColor: "rgba(232,184,32,0.85)", borderRadius: 3 },
	]},
	options: {
	...chartOpts({ y: { title: { display: true, text: "Mask mAP@0.5-0.95 (%)" }, min: 25, max: 40 } }),
	plugins: { legend: { display: true, position: "bottom" } },
	},
	});
	}

	function chartSegScatter() {
	const data = ["yolov8", "yolo11", "yolo26"].filter(v => SNAPSHOT.seg_bench[v]?.n).map(v => ({
	label: v.toUpperCase(),
	data: [{
	x: SNAPSHOT.seg_bench[v].n.onnx_det_map50_95,
	y: SNAPSHOT.seg_bench[v].n.onnx_mask_map50_95,
	}],
	backgroundColor: COLORS[v],
	borderColor: COLORS[v],
	pointRadius: 9, pointHoverRadius: 11,
	}));

	destroyChart("segScatter");
	charts.segScatter = new Chart(document.getElementById("chart-seg-scatter"), {
	type: "scatter",
	data: { datasets: data },
	options: {
	responsive: true, maintainAspectRatio: false,
	plugins: {
	legend: { display: true, position: "bottom" },
	tooltip: {
	callbacks: {
	label: (ctx) => `${ctx.dataset.label}: det ${ctx.parsed.x}% / mask ${ctx.parsed.y}%`,
	},
	},
	},
	scales: {
	x: { title: { display: true, text: "Detection mAP@0.5-0.95 (%)" }, min: 25, max: 32,
	ticks: { font: { family: "monospace", size: 10 } } },
	y: { title: { display: true, text: "Mask mAP@0.5-0.95 (%)" }, min: 33, max: 40,
	ticks: { font: { family: "monospace", size: 10 } } },
	},
	},
	});
	}

	// ---------------------------------------------------------------------------
	// Live data
	// ---------------------------------------------------------------------------

	async function fetchLive() {
	const allRepos = SNAPSHOT.matrix.map(m => ({ id: m.repo_id, type: "model" }));
	try {
	await fetchLiveViaHttp(allRepos);
	} catch (httpErr) {
	console.warn("HF API fetch failed (snapshot-only render):", httpErr);
	}
	afterLive();
	}

	// ----- Path 1: direct HF JSON API (works in any normal browser) -----
	async function fetchLiveViaHttp(repos) {
	const fetches = repos.map(async (r) => {
	const url = r.type === "space"
	? `https://huggingface.co/api/spaces/${r.id}`
	: `https://huggingface.co/api/models/${r.id}`;
	const resp = await fetch(url, { mode: "cors", credentials: "omit" });
	if (!resp.ok) throw new Error(`HTTP ${resp.status} for ${r.id}`);
	const d = await resp.json();
	live[r.id] = {
	downloads: typeof d.downloads === "number" ? d.downloads : null,
	downloadsAllTime: typeof d.downloadsAllTime === "number" ? d.downloadsAllTime : null,
	likes: typeof d.likes === "number" ? d.likes : null,
	lastModified: d.lastModified,
	createdAt: d.createdAt,
	modelIndex: d["model-index"] ?? null,
	siblings: Array.isArray(d.siblings) ? d.siblings.length : null,
	type: r.type,
	};
	});
	await Promise.all(fetches);
	}

	function afterLive() {
	// Update matrix last-modified column
	for (const m of SNAPSHOT.matrix) {
	const cell = document.getElementById("lm-" + m.repo_id.replace(/[/]/g, "_"));
	if (cell) cell.textContent = fmtDate(live[m.repo_id]?.lastModified);
	}
	}

	// ---------------------------------------------------------------------------
	// Studio sessions snapshot
	// ---------------------------------------------------------------------------

	function totalMs(s) {
	// Prefer the computed end-to-end latency; fall back to summing stage means.
	if (s.e2e_latency_ms != null) return s.e2e_latency_ms;
	const a = s.avg_input_ms ?? 0, b = s.avg_inference_ms ?? 0, c = s.avg_output_ms ?? 0;
	const v = a + b + c;
	return v > 0 ? v : null;
	}

	function renderStudio() {
	const table = document.getElementById("studio-table");
	const empty = document.getElementById("studio-empty");
	const source = document.getElementById("studio-source");
	if (!STUDIO \|\| !Array.isArray(STUDIO.sessions) \|\| STUDIO.sessions.length === 0) {
	empty.style.display = "block";
	table.style.display = "none";
	source.textContent = "results refreshing — browse public sessions on EdgeFirst Studio";
	return;
	}
	empty.style.display = "none";
	table.style.display = "";
	const okCount = STUDIO.counts?.sessions_ok ?? STUDIO.counts?.ok ?? STUDIO.sessions.length;
	source.innerHTML = `updated <code>${STUDIO.generated_at}</code> · ${okCount} public sessions · every row links to <a href="https://edgefirst.studio" target="_blank" rel="noopener" style="color:var(--teal-text)">EdgeFirst Studio</a> · click a column header to sort`;

	// Attach sort handlers once
	table.querySelectorAll("th[data-sort]").forEach(th => {
	if (th.dataset.bound) return;
	th.dataset.bound = "1";
	th.addEventListener("click", () => {
	const key = th.dataset.sort;
	if (studioSortKey === key) {
	studioSortDir = -studioSortDir;
	} else {
	studioSortKey = key;
	// Metrics default to descending (best first), text to ascending.
	studioSortDir = ["session", "family", "target", "precision"].includes(key) ? 1 : -1;
	}
	renderStudioBody();
	});
	});

	renderStudioBody();
	}

	const SIZE_ORDER = ["n", "s", "m", "l", "x"];

	const STUDIO_SORT_ACCESSORS = {
	session: s => s.session_name \|\| s.session_id \|\| "",
	family: s => `${String(VERSION_ORDER.indexOf(s.version_key)).padStart(2, "0")}\|${s.task_key}\|${SIZE_ORDER.indexOf(s.size)}`,
	target: s => s.target_label \|\| s.target_raw \|\| "",
	precision: s => s.precision \|\| "",
	det50: s => s.det_map_50,
	det5095: s => s.det_map_50_95,
	mask: s => s.mask_map_50_95,
	inf: s => s.avg_inference_ms,
	e2e: s => totalMs(s),
	fps: s => s.fps_median,
	};

	let studioSortKey = null;
	let studioSortDir = 1;

	function sortedStudioSessions() {
	const sessions = STUDIO.sessions.slice();
	if (studioSortKey && STUDIO_SORT_ACCESSORS[studioSortKey]) {
	const acc = STUDIO_SORT_ACCESSORS[studioSortKey];
	sessions.sort((a, b) => {
	const va = acc(a), vb = acc(b);
	if (va == null && vb == null) return 0;
	if (va == null) return 1; // nulls always last
	if (vb == null) return -1;
	const c = (typeof va === "number" && typeof vb === "number")
	? va - vb
	: String(va).localeCompare(String(vb));
	return c * studioSortDir;
	});
	return sessions;
	}
	// Default: detection first, then by version, size, target, precision
	const targetOrder = ["onnx", "tflite", "imx93", "imx95", "imx8mp", "ara240", "hailo8l", "hailo8", "hailo10", "engine"];
	return sessions.sort((a, b) => {
	if (a.task_key !== b.task_key) return a.task_key.localeCompare(b.task_key);
	const ai = VERSION_ORDER.indexOf(a.version_key);
	const bi = VERSION_ORDER.indexOf(b.version_key);
	if (ai !== bi) return ai - bi;
	const si = SIZE_ORDER.indexOf(a.size), sj = SIZE_ORDER.indexOf(b.size);
	if (si !== sj) return si - sj;
	const ti = targetOrder.indexOf(a.target_key) === -1 ? 99 : targetOrder.indexOf(a.target_key);
	const tj = targetOrder.indexOf(b.target_key) === -1 ? 99 : targetOrder.indexOf(b.target_key);
	if (ti !== tj) return ti - tj;
	return (a.precision \|\| "").localeCompare(b.precision \|\| "");
	});
	}

	function renderStudioBody() {
	const table = document.getElementById("studio-table");
	const body = document.getElementById("studio-body");
	body.innerHTML = "";

	// Header sort indicators
	table.querySelectorAll("th[data-sort]").forEach(th => {
	th.querySelector(".sort-ind")?.remove();
	if (th.dataset.sort === studioSortKey) {
	th.insertAdjacentHTML("beforeend",
	`<span class="sort-ind">${studioSortDir === 1 ? "▲" : "▼"}</span>`);
	}
	});

	const sessions = sortedStudioSessions();
	for (const s of sessions) {
	const total = totalMs(s);
	const tr = el("tr");
	const dashMuted = '<span style="color:var(--text-muted)">—</span>';
	const fmtPct = v => v == null ? dashMuted : v.toFixed(2);
	const fmtMs = v => v == null ? dashMuted : v.toFixed(0);
	const fam = `${s.version_key?.toUpperCase()} · ${s.task_key === "det" ? "Det" : "Seg"} ${s.size ? "· " + s.size.toUpperCase() : ""}`;
	const tgt = s.target_label \|\| s.target_raw \|\| "—";
	const prec = s.precision ? `<span class="pill pill-onnx">${s.precision}</span>` : dashMuted;
	const flag = isBelowExpectations(s) ? ` <span title="${FLAG_FOOTNOTE}">⚠</span>` : "";
	tr.innerHTML = `
	<td><span class="repo-name">${s.session_name \|\| s.session_id}</span><div style="color:var(--text-muted);font-size:0.7rem;font-family:var(--mono)">${s.session_id}</div></td>
	<td>${fam}</td>
	<td>${tgt}</td>
	<td>${prec}</td>
	<td class="num">${fmtPct(s.det_map_50)}${flag}</td>
	<td class="num">${fmtPct(s.det_map_50_95)}</td>
	<td class="num">${fmtPct(s.mask_map_50_95)}</td>
	<td class="num">${fmtMs(s.avg_inference_ms)}</td>
	<td class="num">${total != null ? total.toFixed(1) : dashMuted}</td>
	<td class="num">${s.fps_median != null ? s.fps_median.toFixed(0) : dashMuted}</td>
	<td><a href="${s.url}" target="_blank" rel="noopener">↗</a></td>
	`;
	body.appendChild(tr);
	}
	}

	// Accuracy vs on-target latency — end-to-end per-image wall time, encoded
	// like the throughput chart: color = platform, shape = model family.
	function renderTiming() {
	const host = document.getElementById("timing-host");
	if (!host) return;
	const usable = (STUDIO?.sessions ?? []).filter(s =>
	s.task_key === "det" && totalMs(s) != null && typeof s.det_map_50_95 === "number"
	);
	if (usable.length === 0) {
	// leave the default placeholder copy
	return;
	}

	host.innerHTML = `
	<div class="chart-host" style="height:420px"><canvas id="chart-timing"></canvas></div>
	<p style="font-family:var(--serif);color:var(--text-muted);font-size:0.82rem;margin-top:0.5rem">
	Each point is a public validation session: color = platform, shape = model family
	(● YOLOv5 · ■ YOLOv8 · ▲ YOLO11 · ◆ YOLO26). X axis is end-to-end per-image latency on a log
	scale — the sequential image load → decode → preprocess → inference → postprocess wall time, not
	bare inference. Throughput exceeds 1000 ÷ latency because stages pipeline across frames.
	</p>
	`;

	const byPlatform = {};
	for (const s of usable) {
	const p = platformRowForSession(s);
	if (!p) continue;
	(byPlatform[p.key] \|\|= []).push(s);
	}

	const datasets = PLATFORM_ROWS.filter(p => byPlatform[p.key]).map(p => {
	const pts = byPlatform[p.key];
	return {
	label: `${p.label} ${p.detail.includes("CoreML") ? p.detail.replace("ONNX + ", "") : ""}`.trim(),
	data: pts.map(s => ({
	x: totalMs(s),
	y: s.det_map_50_95,
	name: sessionModelName(s),
	flagged: isBelowExpectations(s),
	})),
	backgroundColor: p.color,
	borderColor: p.color,
	pointStyle: pts.map(s => FAMILY_SHAPES[s.version_key] ?? "circle"),
	pointRadius: 7, pointHoverRadius: 10,
	};
	});

	destroyChart("timing");
	charts.timing = new Chart(document.getElementById("chart-timing"), {
	type: "scatter",
	data: { datasets },
	options: {
	responsive: true, maintainAspectRatio: false,
	plugins: {
	legend: { display: true, position: "bottom" },
	tooltip: {
	callbacks: {
	label: (ctx) => `${ctx.raw.name} on ${ctx.dataset.label}: ${ctx.parsed.y.toFixed(1)} mAP @ ${ctx.parsed.x.toFixed(1)} ms E2E${ctx.raw.flagged ? " ⚠" : ""}`,
	},
	},
	},
	scales: {
	x: { type: "logarithmic",
	title: { display: true, text: "End-to-end latency (ms, log scale — lower is better)" },
	ticks: { font: { family: "monospace", size: 10 },
	callback: (v) => ([1,2,5,10,20,50,100,200,500,1000,2000].includes(v) ? String(v) : null) } },
	y: { title: { display: true, text: "mAP@0.5-0.95 (%)" }, beginAtZero: false,
	ticks: { font: { family: "monospace", size: 10 } } },
	},
	},
	});
	}

	// Update the "Validated sessions" KPI to reflect the snapshot if present.
	function refreshValidationKpi() {
	const n = STUDIO?.counts?.sessions_ok ?? STUDIO?.counts?.ok;
	if (n != null) {
	document.getElementById("kpi-validation").textContent = n;
	}
	}

	// ---------------------------------------------------------------------------
	// Tab switching
	// ---------------------------------------------------------------------------

	function initTabs() {
	const buttons = document.querySelectorAll("nav.tabs button");
	const panels = document.querySelectorAll(".tab-panel");
	buttons.forEach(b => b.addEventListener("click", () => {
	buttons.forEach(x => x.classList.remove("active"));
	panels.forEach(x => x.classList.remove("active"));
	b.classList.add("active");
	const t = b.dataset.tab;
	document.getElementById("tab-" + t).classList.add("active");
	// Ensure charts inside the panel resize properly now that they're visible
	for (const c of Object.values(charts)) {
	try { c.resize(); } catch (e) { /* ignore */ }
	}
	}));
	}

	// ---------------------------------------------------------------------------
	// Boot
	// ---------------------------------------------------------------------------

	window.addEventListener("DOMContentLoaded", () => {
	renderSnapshotDate();
	renderKpisFromSnapshot();
	renderMatrixTable();
	renderPlatformGrid();
	renderCoverageMatrix();
	renderRoadmap();
	renderLeaderboard();
	renderStudio();
	renderTiming();
	refreshValidationKpi();

	// Static charts available immediately
	chartDetectionBar();
	chartDetectionScatter();
	chartDetFps();
	chartSegBar();
	chartSegScatter();
	renderPlatformThroughput();

	initTabs();
	initLeaderboardFilter();

	// Live refresh — fills the "last modified" column from the HF Hub API.
	fetchLive();
	});
	</script>
	</body>
	</html>