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Loads only on the Dragonwing IQ-9075 (Hexagon v73).","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":48.76,"onnx_fp32_map50_95":33.73,"tflite_int8_map50":47.71,"tflite_int8_map50_95":32.94}},"yolov8":{"n":{"onnx_fp32_map50":51.64,"onnx_fp32_map50_95":36.73,"tflite_int8_map50":50.92,"tflite_int8_map50_95":36.0}},"yolo11":{"n":{"onnx_fp32_map50":54.19,"onnx_fp32_map50_95":38.69,"tflite_int8_map50":52.72,"tflite_int8_map50_95":37.35}},"yolo26":{"n":{"onnx_fp32_map50":55.91,"onnx_fp32_map50_95":40.25,"tflite_int8_map50":54.08,"tflite_int8_map50_95":38.08}}},"seg_bench":{"yolov8":{"n":{"onnx_det_map50_95":36.23,"onnx_mask_map50_95":29.24,"int8_det_map50_95":35.51,"int8_mask_map50_95":28.7}},"yolo11":{"n":{"onnx_det_map50_95":38.48,"onnx_mask_map50_95":31.03,"int8_det_map50_95":37.21,"int8_mask_map50_95":29.82}},"yolo26":{"n":{"onnx_det_map50_95":40.13,"onnx_mask_map50_95":33.25,"int8_det_map50_95":37.83,"int8_mask_map50_95":31.55}}},"validation":{},"studio_exp_urls":{"yolov5":{"det":"https://edgefirst.studio/public/p-694/exp-be2/trainings"},"yolov8":{"det":"https://edgefirst.studio/public/p-694/exp-be3/trainings","seg":"https://edgefirst.studio/public/p-695/exp-c1f/trainings"},"yolo11":{"det":"https://edgefirst.studio/public/p-694/exp-be4/trainings","seg":"https://edgefirst.studio/public/p-695/exp-c20/trainings"},"yolo26":{"det":"https://edgefirst.studio/public/p-694/exp-be5/trainings","seg":"https://edgefirst.studio/public/p-695/exp-c21/trainings"}},"hardware_badges":["Linux x86_64","Apple macOS","NXP i.MX 8M Plus","NXP i.MX 95","NXP Ara240","RPi5 + Hailo-8L","NVIDIA Jetson"],"studio_server":"https://edgefirst.studio","unsupported_platforms":[],"visdrone_matrix":[{"repo_id":"EdgeFirst/visdrone-yolov8-det","dataset":"visdrone","version_key":"yolov8","version_label":"YOLOv8","task_key":"det","task_label":"Detection","pipeline_tag":"object-detection","sizes":["n","s","m"],"variants":["640","1280","1344x768","tiled"],"export_notes":""},{"repo_id":"EdgeFirst/visdrone-yolo11-det","dataset":"visdrone","version_key":"yolo11","version_label":"YOLO11","task_key":"det","task_label":"Detection","pipeline_tag":"object-detection","sizes":["n","s","m"],"variants":["640","1280","1344x768","tiled"],"export_notes":""},{"repo_id":"EdgeFirst/visdrone-yolo26-det","dataset":"visdrone","version_key":"yolo26","version_label":"YOLO26","task_key":"det","task_label":"Detection","pipeline_tag":"object-detection","sizes":["n","s","m"],"variants":["640","1280","1344x768","tiled"],"export_notes":""}],"visdrone_bench":{"yolov8":{"640":{"onnx_aps":10.14,"onnx_ap":18.6,"int8_aps":9.34,"int8_ap":17.67},"1280":{"onnx_aps":20.02,"onnx_ap":29.41,"int8_aps":19.58,"int8_ap":28.89},"1344x768":{"onnx_aps":21.6,"onnx_ap":30.29,"int8_aps":20.47,"int8_ap":29.04},"tiled":{"onnx_aps":19.98,"onnx_ap":28.48,"int8_aps":18.94,"int8_ap":27.41}},"yolo11":{"640":{"onnx_aps":9.99,"onnx_ap":18.46,"int8_aps":9.32,"int8_ap":17.84},"1280":{"onnx_aps":19.75,"onnx_ap":28.44,"int8_aps":18.61,"int8_ap":27.19},"1344x768":{"onnx_aps":20.7,"onnx_ap":29.95,"int8_aps":19.75,"int8_ap":28.82},"tiled":{"onnx_aps":19.46,"onnx_ap":28.1,"int8_aps":18.77,"int8_ap":27.3}},"yolo26":{"640":{"onnx_aps":10.32,"onnx_ap":18.38,"int8_aps":9.75,"int8_ap":17.52},"1280":{"onnx_aps":20.68,"onnx_ap":29.14,"int8_aps":19.91,"int8_ap":28.39},"1344x768":{"onnx_aps":21.57,"onnx_ap":30.49,"int8_aps":20.87,"int8_ap":29.53},"tiled":{"onnx_aps":20.35,"onnx_ap":28.77,"int8_aps":19.39,"int8_ap":27.56}}}}</script>
 <script>
 "use strict";
 
@@ -1425,8 +1425,9 @@ const RUNTIME_LABELS = {
 const DECODE_LABELS = { smart: "Smart", logical: "Logical", combined: "Combined" };
 
 // Everything that distinguishes one competing configuration from another on the
-// same device: runtime/format, quantization precision, decode variant, and the
-// pipeline mode where a platform runs both. Rendered on every leaderboard row so
+// same device: runtime/format, quantization precision and calibration, decode
+// variant, the pipeline mode where a platform runs both, and the converter
+// preset that points at a Qualcomm grid cell. Rendered on every leaderboard row so
 // two rows of the same model on the same device are never ambiguous.
 function sessionConfigLabel(s) {
   const parts = [];
@@ -1434,10 +1435,12 @@ function sessionConfigLabel(s) {
     ?? ((s.target_key || "").startsWith("qnn_") ? RUNTIME_LABELS.qnn : undefined);
   if (rt) parts.push(rt);
   if (s.precision) parts.push(s.precision);
+  if (s.quant_label) parts.push(s.quant_label);
   if (s.gpu_model) parts.push(s.gpu_model);
   const dv = DECODE_LABELS[s.decoder_variant];
   if (dv) parts.push(dv);
   if (s.pipeline_mode) parts.push(s.pipeline_mode);
+  if (s.qnn_preset) parts.push(`${s.qnn_preset} preset`);
   return parts.join(" · ");
 }
 
@@ -2318,9 +2321,11 @@ function renderStudioBody() {
     const fam = `${esc(s.version_key?.toUpperCase())} · ${s.task_key === "det" ? "Det" : "Seg"} ${s.size ? "· " + esc(s.size.toUpperCase()) : ""}${variant}${editionBadge(s)}`;
     const tgt = esc(s.target_label || s.target_raw || "—");
     // Fleet NXP i.MX 95 Neutron runs carry a pipeline mode (latency/throughput) and
-    // a board tier (FRDM/Phytec); surface them as small pills so the otherwise
-    // identical session name + description rows read as distinct configs.
-    const tgtPills = [s.gpu_model, s.pipeline_mode, s.board]
+    // a board tier (FRDM/Phytec), and Qualcomm grid runs a calibration and the
+    // converter preset pointing at them; surface them as small pills so the
+    // otherwise identical session name + description rows read as distinct configs.
+    const tgtPills = [s.gpu_model, s.pipeline_mode, s.board,
+                      s.quant_label, s.qnn_preset && `${s.qnn_preset} preset`]
       .filter(Boolean)
       .map(b => ` <span class="pill pill-target">${esc(b)}</span>`)
       .join("");