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/preview.py

@@ -12,7 +12,7 @@ import os
 from typing import List, Optional
 
 from backend import batches, db, labeling, projects, review
-from backend.autolabel import DEFAULT_IOU, DEFAULT_THRESHOLD, _geometries
+from backend.autolabel import DEFAULT_IOU, DEFAULT_THRESHOLD, _geometries, _parse_class_params
 
 def preview_frame(
     batch_id: int,
@@ -25,6 +25,7 @@ def preview_frame(
     custom_model_path: Optional[str] = None,
     exemplars: Optional[List[dict]] = None,
     exemplar_class_name: Optional[str] = None,
+    class_params: Optional[dict] = None,
 ) -> List[dict]:
     batch = batches.get(batch_id)
     if not batch:
@@ -69,11 +70,13 @@ def preview_frame(
 
     name_to_class_id = {item["name"].strip().lower(): item["class_id"] for item in project["classes"]}
     allowed_classes_set = {c.strip().lower() for c in target_class_names} if target_class_names else None
+    per_class = _parse_class_params(class_params)
+    predict_conf = min([threshold] + [v["threshold"] for v in per_class.values() if "threshold" in v])
 
     all_raw_detections = []
 
     if yolo_model is not None:
-        results = yolo_model.predict(frame_file, conf=threshold, verbose=False)
+        results = yolo_model.predict(frame_file, conf=predict_conf, verbose=False)
         if results and len(results) > 0:
             model_names = results[0].names
             for box in results[0].boxes:
@@ -103,6 +106,15 @@ def preview_frame(
 
                 score = float(box.conf[0].item())
                 xyxyn = box.xyxyn[0].tolist()
+
+                cp = per_class.get(proj_cls_name) or per_class.get(raw_cls_name)
+                if cp:
+                    if score < cp.get("threshold", threshold):
+                        continue
+                    mb = cp.get("min_box_frac")
+                    if mb and mb > 0 and (xyxyn[2]-xyxyn[0])*(xyxyn[3]-xyxyn[1]) < mb:
+                        continue
+
                 all_raw_detections.append(labeling.Detection(
                     class_id=target_class_id,
                     class_name=proj_cls_name or raw_cls_name,
@@ -125,10 +137,22 @@ def preview_frame(
                  if c["name"].strip().lower() == wanted),
                 -1,
             )
+        thr_list = iou_list = mb_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()):
+                thr_list = [per_class.get(n, {}).get("threshold", threshold) for n in names]
+            if any("iou_threshold" in v for v in per_class.values()):
+                iou_list = [per_class.get(n, {}).get("iou_threshold", iou_threshold) 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", min_box_frac) for n in names]
         res = labeling.label_image(
             frame_file, frame["filename"], prompts, threshold,
             iou_threshold=iou_threshold, min_box_frac=min_box_frac,
-            exemplar_index=exemplar_index, exemplars=exemplars
+            exemplar_index=exemplar_index, exemplars=exemplars,
+            thresholds=thr_list,
+            iou_by_class=dict(enumerate(iou_list)) if iou_list else None,
+            min_box_fracs=mb_list,
         )
         if not res.error and res.detections:
             for det in res.detections:
@@ -138,7 +162,14 @@ def preview_frame(
                     det.class_name = real_cls["name"]
                     all_raw_detections.append(det)
 
-    kept = labeling.deduplicate(all_raw_detections, iou_threshold=iou_threshold)
+    iou_by_class_proj = {
+        c["class_id"]: per_class[c["name"].strip().lower()]["iou_threshold"]
+        for c in project["classes"]
+        if c["name"].strip().lower() in per_class
+        and "iou_threshold" in per_class[c["name"].strip().lower()]
+    }
+    kept = labeling.deduplicate(all_raw_detections, iou_threshold=iou_threshold,
+                                iou_by_class=iou_by_class_proj or None)
     items = []
     for det in kept:
         if project["label_type"] == "bbox" or det.mask is None: