feat: per-class max box fraction (REQ-188)

- labeling/preview/autolabel: max_box_frac + per-class max_box_fracs
  ceiling filter (0=none, 1=off) before NMS, mirrors min_box_frac
- exemplar review-assist: ceiling before max_detections truncation;
  ExemplarLabelRequest + filter panel 'Max box size' slider
- ClassParamsTable: MaxBox column; copy line now REQ-186 amended
  order: class <name> conf <v> iou <v> minbox <v> maxbox <v> container
- docs: design/ui-spec/tasks updated (incl. stale 4-slider narrative)

backend/autolabel.py

@@ -27,7 +27,7 @@ def _parse_class_params(raw) -> dict:
         if not isinstance(values, dict):
             continue
         entry = {}
-        for key in ("threshold", "iou_threshold", "min_box_frac"):
+        for key in ("threshold", "iou_threshold", "min_box_frac", "max_box_frac"):
             if key in values:
                 try:
                     entry[key] = float(values[key])
@@ -229,6 +229,9 @@ def _run_autolabel(job) -> None:
                             mb = cp.get("min_box_frac")
                             if mb and mb > 0 and (xyxyn[2]-xyxyn[0])*(xyxyn[3]-xyxyn[1]) < mb:
                                 continue
+                            xb = cp.get("max_box_frac")
+                            if xb and xb < 1 and (xyxyn[2]-xyxyn[0])*(xyxyn[3]-xyxyn[1]) > xb:
+                                continue
 
                         all_raw_detections.append(labeling.Detection(
                             class_id=target_class_id,
@@ -240,7 +243,7 @@ def _run_autolabel(job) -> None:
 
             if selected_engine == "sam3" and sam3_target_classes:
                 prompts = [(c.get("prompt") or c["name"]).strip() for c in sam3_target_classes]
-                thr_list = iou_list = mb_list = None
+                thr_list = iou_list = mb_list = mx_list = None
                 if per_class:
                     names = [c["name"].strip().lower() for c in sam3_target_classes]
                     if any("threshold" in v for v in per_class.values()):
@@ -249,12 +252,15 @@ def _run_autolabel(job) -> None:
                         iou_list = [per_class.get(n, {}).get("iou_threshold", iou_thresh) for n in names]
                     if any("min_box_frac" in v for v in per_class.values()):
                         mb_list = [per_class.get(n, {}).get("min_box_frac", job.params.get("min_box_frac", 0.0)) for n in names]
+                    if any("max_box_frac" in v for v in per_class.values()):
+                        mx_list = [per_class.get(n, {}).get("max_box_frac", 1.0) for n in names]
                 res = labeling.label_image(
                     frame_file, frame["filename"], prompts, conf,
                     iou_threshold=iou_thresh, min_box_frac=job.params.get("min_box_frac", 0.0),
                     thresholds=thr_list,
                     iou_by_class=dict(enumerate(iou_list)) if iou_list else None,
                     min_box_fracs=mb_list,
+                    max_box_fracs=mx_list,
                     container_ids=prompt_container_ids,
                 )
                 if not res.error and res.detections: