feat: per-class autolabel params + frame-scoped class clear (REQ-180, REQ-181)

- REQ-181: class_params {name: {threshold?, iou_threshold?, min_box_frac?}}
  on /preview and /autolabel, per-class override table in both modals;
  empty overrides take the unchanged global path
- REQ-180: x button on each review sidebar class row clears that class on
  the current frame only via bulk-delete, no confirmation
- includes REQ-178 empty date-folder cycle fix (archive_index.py)

backend/labeling.py

@@ -10,7 +10,7 @@ calls — see the domain invariants in `../AGENTS.md`.
 """
 
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import Dict, List, Optional
 
 from PIL import Image
 
@@ -41,9 +41,12 @@ def _iou(box_a: List[float], box_b: List[float]) -> float:
     return inter / union if union > 0 else 0.0
 
 
-def deduplicate(detections: List[Detection], iou_threshold: float = 0.8) -> List[Detection]:
-    """Greedy NMS per class: highest score wins within the SAME class."""
-    if iou_threshold <= 0.0:
+def deduplicate(detections: List[Detection], iou_threshold: float = 0.8,
+                iou_by_class: Optional[Dict[int, float]] = None) -> List[Detection]:
+    """Greedy NMS per class: highest score wins within the SAME class.
+
+    `iou_by_class` overrides the threshold per class id (REQ-181)."""
+    if not iou_by_class and iou_threshold <= 0.0:
         return detections
 
     by_class: dict[int, List[Detection]] = {}
@@ -52,10 +55,14 @@ def deduplicate(detections: List[Detection], iou_threshold: float = 0.8) -> List
         by_class.setdefault(det.class_id, []).append(det)
 
     kept: List[Detection] = []
-    for cls_dets in by_class.values():
+    for cls, cls_dets in by_class.items():
+        iou = (iou_by_class or {}).get(cls, iou_threshold)
+        if iou <= 0.0:
+            kept.extend(cls_dets)
+            continue
         cls_kept: List[Detection] = []
         for det in sorted(cls_dets, key=lambda d: d.score, reverse=True):
-            if all(_iou(det.box, k.box) < iou_threshold for k in cls_kept):
+            if all(_iou(det.box, k.box) < iou for k in cls_kept):
                 cls_kept.append(det)
         kept.extend(cls_kept)
     return kept
@@ -70,11 +77,16 @@ def label_image(
     min_box_frac: float = 0.0,
     exemplar_index: int = -1,
     exemplars: Optional[List[dict]] = None,
+    thresholds: Optional[List[float]] = None,
+    iou_by_class: Optional[Dict[int, float]] = None,
+    min_box_fracs: Optional[List[float]] = None,
 ) -> ImageResult:
     """Detect every prompt in one image and return the surviving instances.
 
     When `exemplars` are given, the prompt at `exemplar_index` also carries them
-    as drawn box exemplars (REQ-172); every other prompt runs on text alone."""
+    as drawn box exemplars (REQ-172); every other prompt runs on text alone.
+    `thresholds`, `iou_by_class` and `min_box_fracs` are per-prompt overrides
+    aligned with `prompts` (REQ-181); classes without one use the global values."""
     try:
         image = Image.open(image_path).convert("RGB")
     except Exception as exc:  # unreadable/corrupt frame: report, don't abort the job
@@ -84,14 +96,24 @@ def label_image(
     try:
         if exemplars and 0 <= exemplar_index < len(prompts):
             detections = get_engine().detect_with_exemplars(
-                image, prompts, threshold, exemplar_index, exemplars
+                image, prompts, threshold, exemplar_index, exemplars,
+                thresholds=thresholds
             )
         else:
-            detections = get_engine().detect(image, prompts, threshold)
+            detections = get_engine().detect(image, prompts, threshold,
+                                             thresholds=thresholds)
     except Exception as exc:
         return ImageResult(image_path, rel_path, width, height, error=str(exc))
 
-    if min_box_frac > 0:
+    if min_box_fracs is not None:
+        def _keep(det) -> bool:
+            frac = min_box_fracs[det.class_id] if det.class_id < len(min_box_fracs) else min_box_frac
+            if frac <= 0:
+                return True
+            floor = width * height * frac
+            return (det.box[2] - det.box[0]) * (det.box[3] - det.box[1]) >= floor
+        detections = [d for d in detections if _keep(d)]
+    elif min_box_frac > 0:
         floor = width * height * min_box_frac
         detections = [
             d for d in detections
@@ -99,4 +121,4 @@ def label_image(
         ]
 
     return ImageResult(image_path, rel_path, width, height,
-                       deduplicate(detections, iou_threshold))
+                       deduplicate(detections, iou_threshold, iou_by_class=iou_by_class))