diff --git "a/index.html" "b/index.html"
--- "a/index.html"
+++ "b/index.html"
@@ -418,7 +418,7 @@
         <thead>
           <tr>
             <th>Repo</th><th>Family</th><th>Task</th>
-            <th>Sizes</th><th>Nano mAP<sub>50</sub> (ONNX)</th><th>Nano mAP<sub>50-95</sub> (INT8)</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>
@@ -442,7 +442,7 @@
       </div>
       <div class="chart-grid">
         <div class="chart-card">
-          <h3>mAP@0.5 — ONNX FP32 vs TFLite INT8</h3>
+          <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">
@@ -452,6 +452,19 @@
       </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>Nano Segmentation — Mask mAP</h2>
         <span class="section-note">YOLOv8 / YOLO11 / YOLO26, COCO val2017</span>
@@ -504,7 +517,8 @@
           <th>Det mAP<sub>50-95</sub></th>
           <th>Mask mAP<sub>50-95</sub></th>
           <th>Inf (ms)</th>
-          <th>Total (ms)</th>
+          <th>E2E (ms)</th>
+          <th>FPS</th>
           <th>Studio</th>
         </tr></thead>
         <tbody id="studio-body"></tbody>
@@ -552,12 +566,37 @@
 
   <!-- Leaderboard -->
   <section class="tab-panel" id="tab-leaderboard">
+    <div class="section">
+      <div class="section-header"><h2>Platform Throughput Leaderboard</h2>
+        <span class="section-note">Best sustained pipelined throughput measured per platform · full image load → decode → preprocess → inference → postprocess pipeline</span>
+      </div>
+      <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)</button>
-          <button data-group="reference">Desktop &amp; Jetson reference</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>
@@ -583,7 +622,7 @@
   </footer>
 </div>
 
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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":["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"}</script>
 <script>
 "use strict";
@@ -596,18 +635,22 @@
 // `match` is tested (in order) against the Studio target/footer string to map a
 // validation session onto a platform row.
 const PLATFORM_ROWS = [
-  { key: "linux-x86", label: "Linux x86_64",            detail: "ONNX Runtime + CUDA",            group: "reference", match: /linux|x86|cuda/ },
-  { key: "m2max",     label: "Apple M2 Max",            detail: "ONNX + CoreML (CPU, GPU, ANE)",  group: "reference", match: /macos|coreml/ },
-  { key: "imx8mp",    label: "NXP i.MX 8M Plus",        detail: "VSI NPU · TFLite INT8",          group: "embedded",  match: /imx8mp/ },
-  { key: "imx93",     label: "NXP i.MX 93",             detail: "Ethos-U65 · VELA TFLite",        group: "embedded",  match: /imx93/ },
-  { key: "imx95",     label: "NXP i.MX 95",             detail: "eIQ Neutron · TFLite INT8",      group: "embedded",  match: /imx95/ },
-  { key: "ara240",    label: "NXP ARA240",              detail: "Kinara DVM",                     group: "embedded",  match: /ara240|kinara|\.dvm/ },
-  { key: "hailo10",   label: "Hailo-10",                detail: "HailoRT HEF · 40 TOPS",          group: "embedded",  match: /hailo10/ },
-  { key: "hailo8l",   label: "Raspberry Pi 5 + Hailo-8L", detail: "HailoRT HEF · 13 TOPS",        group: "embedded",  match: /hailo8l|rpi5/ },
-  { key: "hailo8",    label: "Hailo-8",                 detail: "HailoRT HEF · 26 TOPS",          group: "embedded",  match: /hailo8/ },
-  { key: "orin",      label: "NVIDIA Jetson Orin Nano", detail: "TensorRT FP16",                  group: "reference", match: /orin|tensorrt|jetson|\.engine/ },
+  { key: "linux-x86", label: "Linux x86_64",            detail: "ONNX Runtime + CUDA",            group: "reference", color: "#6C757D", match: /linux|x86|cuda/ },
+  { key: "m2max",     label: "Apple M2 Max",            detail: "ONNX + CoreML (CPU, GPU, ANE)",  group: "reference", color: "#4B0082", match: /macos|coreml/ },
+  { 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/ },
+  { key: "ara240",    label: "NXP ARA240",              detail: "Kinara DVM",                     group: "embedded",  color: "#3E3371", 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;
@@ -634,6 +677,9 @@ function 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}`];
@@ -733,7 +779,9 @@ function renderMatrixTable() {
     const bench = m.task_key === "det"
       ? (SNAPSHOT.det_bench[m.version_key]?.n)
       : (SNAPSHOT.seg_bench[m.version_key]?.n);
-    const onnxMap50 = bench?.onnx_fp32_map50 ?? null;
+    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;
@@ -880,20 +928,31 @@ function renderRoadmap() {
 }
 
 // Per-platform leaderboards built from measured Studio sessions, with a
-// power-class filter that splits embedded accelerators from the desktop /
-// Jetson reference platforms.
+// 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 leaderboardEntries(platformKey) {
-  // Best session per (version, size); smart decoder preferred over logical.
+function sessionModelName(s) {
+  return `${s.version_key.toUpperCase()}${(s.size || "").toUpperCase()}${s.task_key === "seg" ? "-Seg" : ""}`;
+}
+
+function isSmartSession(s) {
+  return s.decoder_variant === "smart" || /_smart/.test(s.target_raw || "");
+}
+
+function leaderboardEntries(platformKey, metricKey) {
+  // Best session per (task, version, size); smart decoder preferred over logical.
   const best = {};
   for (const s of (STUDIO?.sessions ?? [])) {
     const p = platformRowForSession(s);
     if (!p || p.key !== platformKey || !s.has_metrics) continue;
-    const metric = s.det_map_50_95 ?? s.mask_map_50_95;
+    const metric = metricKey === "fps"
+      ? s.fps_median
+      : (s.det_map_50_95 ?? s.mask_map_50_95);
     if (metric == null) continue;
     const key = `${s.task_key}|${s.version_key}|${s.size}`;
-    const isSmart = /_smart/.test(s.target_raw || "");
+    const isSmart = isSmartSession(s);
     const cur = best[key];
     if (!cur || (isSmart && !cur.isSmart) || (isSmart === cur.isSmart && metric > cur.metric)) {
       best[key] = { s, metric, isSmart };
@@ -902,59 +961,99 @@ function leaderboardEntries(platformKey) {
   return Object.values(best)
     .sort((a, b) => b.metric - a.metric)
     .map(({ s, metric }) => ({
-      name: `${s.version_key.toUpperCase()}${(s.size || "").toUpperCase()}${s.task_key === "seg" ? "-Seg" : ""}`,
+      name: sessionModelName(s),
       metric,
       url: s.url,
-      inf: s.avg_inference_ms,
+      sub: metricKey === "fps"
+        ? (s.det_map_50_95 != null ? `${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: ranks platforms by their best measured
+// pipelined FPS (accuracy intentionally not ranked here).
+function renderPlatformThroughput() {
+  const host = document.getElementById("platform-fps-body");
+  if (!host) return;
+  host.innerHTML = "";
+  const rows = [];
+  for (const p of PLATFORM_ROWS) {
+    let top = null;
+    for (const s of (STUDIO?.sessions ?? [])) {
+      const pr = platformRowForSession(s);
+      if (!pr || pr.key !== p.key || s.fps_median == null) continue;
+      if (!top || s.fps_median > top.fps_median) top = s;
+    }
+    if (top) rows.push({ p, s: top });
+  }
+  rows.sort((a, b) => b.s.fps_median - a.s.fps_median);
+  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 => lbGroup === "all" || p.group === lbGroup);
+  const metricLabel = lbMetric === "fps"
+    ? "Pipelined throughput FPS (higher is better)"
+    : "mAP@0.5-0.95 (higher is better)";
 
-  let anyRows = false;
   for (const p of platforms) {
-    const rows = leaderboardEntries(p.key);
+    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 {
-      anyRows = true;
       for (let i = 0; i < rows.length; i++) {
         const r = rows[i];
         const row = el("div", { class: "lb-row" });
-        const inf = r.inf != null ? ` <span class="lb-sub">· ${r.inf.toFixed(0)} ms</span>` : "";
+        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">${r.name}</a>${inf}</span>
-          <span class="lb-metric">${r.metric.toFixed(1)}${flag}</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: "mAP@0.5-0.95 (higher is better)" }));
+      card.appendChild(el("div", { class: "lb-empty", text: metricLabel }));
     }
     grid.appendChild(card);
   }
-
-  if (!anyRows && platforms.length === 0) {
-    grid.appendChild(el("div", { class: "lb-empty", text: "No platforms in this class yet." }));
-  }
 }
 
 function initLeaderboardFilter() {
-  const host = document.getElementById("lb-filter");
-  if (!host) return;
-  host.querySelectorAll("button").forEach(b => b.addEventListener("click", () => {
-    host.querySelectorAll("button").forEach(x => x.classList.remove("active"));
-    b.classList.add("active");
-    lbGroup = b.dataset.group;
-    renderLeaderboard();
-  }));
+  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();
+    }));
+  }
 }
 
 // ---------------------------------------------------------------------------
@@ -985,8 +1084,8 @@ function chartOpts(scaleOverrides) {
 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);
-  const int8 = versions.map(v => SNAPSHOT.det_bench[v].n.tflite_int8_map50);
+  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"), {
@@ -996,12 +1095,71 @@ function chartDetectionBar() {
       { label: "TFLite INT8", data: int8, backgroundColor: "rgba(232,184,32,0.85)", borderRadius: 3 },
     ]},
     options: {
-      ...chartOpts({ y: { title: { display: true, text: "mAP@0.5 (%)" }, min: 40, max: 60 } }),
+      ...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,
+      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(),
@@ -1144,6 +1302,8 @@ function afterLive() {
 // ---------------------------------------------------------------------------
 
 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;
@@ -1209,7 +1369,8 @@ function renderStudio() {
       <td class="num">${fmtPct(s.det_map_50_95)}</td>
       <td class="num">${fmtPct(s.mask_map_50_95)}</td>
       <td class="num">${fmtMs(s.avg_inference_ms)}</td>
-      <td class="num">${fmtMs(total)}</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);
@@ -1325,8 +1486,10 @@ window.addEventListener("DOMContentLoaded", () => {
   // Static charts available immediately
   chartDetectionBar();
   chartDetectionScatter();
+  chartDetFps();
   chartSegBar();
   chartSegScatter();
+  renderPlatformThroughput();
 
   initTabs();
   initLeaderboardFilter();