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/autolabel.py

@@ -17,6 +17,26 @@ DEFAULT_THRESHOLD = 0.35
 DEFAULT_IOU = 0.0
 
 
+def _parse_class_params(raw) -> dict:
+    """Normalize `class_params` (REQ-181): lowercased class name → overrides.
+
+    Unknown keys and non-numeric values are dropped, so a malformed payload
+    degrades to the global values instead of failing the job."""
+    out: dict = {}
+    for name, values in (raw or {}).items():
+        if not isinstance(values, dict):
+            continue
+        entry = {}
+        for key in ("threshold", "iou_threshold", "min_box_frac"):
+            if key in values:
+                try:
+                    entry[key] = float(values[key])
+                except (TypeError, ValueError):
+                    pass
+        if entry:
+            out[str(name).strip().lower()] = entry
+    return out
+
 
 
 def start(batch_id: int, threshold: float = DEFAULT_THRESHOLD,
@@ -26,7 +46,8 @@ def start(batch_id: int, threshold: float = DEFAULT_THRESHOLD,
           class_ids: Optional[List[int]] = None,
           engine_classes: Optional[dict[str, List[str]]] = None,
           custom_model_path: Optional[str] = None,
-          target_class_names: Optional[List[str]] = None) -> dict:
+          target_class_names: Optional[List[str]] = None,
+          class_params: Optional[dict] = None) -> dict:
     batch = batches.get(batch_id)
     if batch is None:
         raise batches.BatchError("No such batch")
@@ -41,7 +62,8 @@ def start(batch_id: int, threshold: float = DEFAULT_THRESHOLD,
                 "iou_threshold": iou_threshold, "min_box_frac": min_box_frac,
                 "resume": resume, "append": append, "engine": active_engines[0], "engines": active_engines,
                 "class_ids": class_ids, "engine_classes": engine_classes,
-                "custom_model_path": custom_model_path, "target_class_names": target_class_names},
+                "custom_model_path": custom_model_path, "target_class_names": target_class_names,
+                "class_params": class_params},
         project_id=batch["project_id"],
         batch_id=batch_id,
         message=f"{batch['date_label']}/{batch['batch_label']} ({'+'.join(e.upper() for e in active_engines)})",
@@ -85,6 +107,10 @@ def _run_autolabel(job) -> None:
 
     conf = job.params.get("threshold", DEFAULT_THRESHOLD)
     iou_thresh = job.params.get("iou_threshold", DEFAULT_IOU)
+    per_class = _parse_class_params(job.params.get("class_params"))
+    # Predict at the LOWEST threshold in play so a class with a lower override
+    # can still see its boxes; each box then passes its own class gate below.
+    predict_conf = min([conf] + [v["threshold"] for v in per_class.values() if "threshold" in v])
 
     yolo_model = None
     sam3_target_classes = []
@@ -159,7 +185,7 @@ def _run_autolabel(job) -> None:
             all_raw_detections = []
 
             if yolo_model is not None:
-                results = yolo_model.predict(frame_file, conf=conf, 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:
@@ -189,6 +215,15 @@ def _run_autolabel(job) -> None:
 
                         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", conf):
+                                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,
@@ -199,9 +234,21 @@ 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
+                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", conf) 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_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]
                 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)
+                    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,
                 )
                 if not res.error and res.detections:
                     for det in res.detections:
@@ -213,7 +260,14 @@ def _run_autolabel(job) -> None:
                 elif res.error:
                     job.log(f"[SAM3 ERROR] {frame['filename']}: {res.error}")
 
-            kept = labeling.deduplicate(all_raw_detections, iou_threshold=iou_thresh)
+            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_thresh,
+                                        iou_by_class=iou_by_class_proj or None)
             items = []
             for det in kept:
                 if project["label_type"] == "bbox" or det.mask is None: