Revert "约束预测框互不重叠"（eba533a、9439cbb）

去掉强制非重叠：detect.py 不再给 DetectionValidator.postprocess 打补丁，
配置里移除 train.max_overlap 与 agnostic_nms，可视化脚本恢复为直接使用模型原始输出，
engine/detection 里的 suppress_overlaps / filter_overlapping_outputs 一并移除。

约束会把 mAP50 从 0.555 压到 0.483（precision 0.536->0.612），代价是按置信度抑制时
高分错框会压掉本可命中的低分框。需要时用 git revert 这次提交即可取回。

按类别分目录的可视化产出结构（36fff98）保留。

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

scripts/visualize_detections.py

@@ -25,10 +25,6 @@ train / val 不分目录，但 `index.csv` 里有 `split` 列——训练集的
 同样地，"命中 / 漏检 / 误检"记在 `index.csv` 的 `outcome` 列里，按它排序筛选即可。
 
 `--only errors` 只输出有漏检或误检的图，扫起来快得多。
-
-**预测框默认约束为互不重叠**（`--max-overlap 0`）。人工标注的真值里一处缺陷只有一个框、
-零重叠，而 NMS 只在同类内按固定阈值抑制，留得下跨类重复和 IoU 低于阈值的重叠框。
-`--max-overlap -1` 可关掉这层约束，看模型的原始输出。
 """
 
 from __future__ import annotations
@@ -46,13 +42,7 @@ sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
 import yaml  # noqa: E402
 
 from datasets.yolo import IMAGE_SUFFIXES  # noqa: E402
-from engine.detection import (  # noqa: E402
-    Box,
-    draw_boxes,
-    match_boxes,
-    parse_label,
-    suppress_overlaps,
-)
+from engine.detection import Box, draw_boxes, match_boxes, parse_label  # noqa: E402
 from utils import setup_logging  # noqa: E402
 from utils.device import DeviceNotAvailableError, describe_device, resolve_device  # noqa: E402
 
@@ -133,16 +123,6 @@ def main() -> int:
     parser.add_argument("--imgsz", type=int, default=640)
     parser.add_argument("--conf", type=float, default=0.25, help="置信度阈值")
     parser.add_argument("--iou-threshold", type=float, default=0.5, help="判定命中的 IoU")
-    parser.add_argument(
-        "--max-overlap", type=float, default=0.0,
-        help="预测框之间允许的最大 IoU，超出的丢低分框。0=完全不允许重叠（默认，与真值一致），"
-             "-1=不约束，看模型原始输出",
-    )
-    parser.add_argument("--nms-iou", type=float, default=0.7, help="NMS 的 IoU 阈值")
-    parser.add_argument(
-        "--no-agnostic-nms", action="store_true",
-        help="关掉类别无关 NMS。默认开启，用来压掉同一处缺陷被两个类别各框一次的重复",
-    )
     parser.add_argument("--batch", type=int, default=16)
     parser.add_argument(
         "--only", choices=("all", "errors"), default="all",
@@ -170,12 +150,7 @@ def main() -> int:
         "数据集: %s（%d 类，%s）", args.data, len(classes),
         "，".join(f"{name} {len(images)} 张" for name, images in splits.items()),
     )
-    logger.info(
-        "conf=%.2f  命中 IoU=%.2f  imgsz=%d  NMS IoU=%.2f  类别无关 NMS=%s  框间最大重叠=%s",
-        args.conf, args.iou_threshold, args.imgsz, args.nms_iou,
-        not args.no_agnostic_nms,
-        "不约束" if args.max_overlap < 0 else f"{args.max_overlap:.2f}",
-    )
+    logger.info("conf=%.2f  IoU=%.2f  imgsz=%d", args.conf, args.iou_threshold, args.imgsz)
 
     if out_dir.exists() and any(out_dir.iterdir()):
         if not args.overwrite:
@@ -196,7 +171,6 @@ def main() -> int:
     scores: dict[str, list[float]] = defaultdict(list)
     rows = []
     processed = 0
-    suppressed = 0
 
     def predict_in_batches(images):
         """自己按批切分：ultralytics 的 predict 不理会 batch 参数，
@@ -205,8 +179,7 @@ def main() -> int:
             chunk = images[start:start + args.batch]
             results = model.predict(
                 source=[str(p) for p in chunk], imgsz=args.imgsz, conf=args.conf,
-                device=device_arg, verbose=False, iou=args.nms_iou,
-                agnostic_nms=not args.no_agnostic_nms,
+                device=device_arg, verbose=False,
             )
             yield from zip(chunk, results, strict=True)
 
@@ -219,11 +192,6 @@ def main() -> int:
                     result.boxes.conf.tolist(), strict=True,
                 )
             ]
-            if args.max_overlap >= 0:
-                kept = suppress_overlaps(predictions, max_iou=args.max_overlap)
-                suppressed += len(predictions) - len(kept)
-                predictions = [predictions[i] for i in kept]
-
             label_file = label_path_for(image_path)
             truth = parse_label(label_file.read_text(encoding="utf-8")) if label_file.exists() else []
             match = match_boxes(predictions, truth, args.iou_threshold)
@@ -273,10 +241,6 @@ def main() -> int:
         "conf": args.conf,
         "iou_threshold": args.iou_threshold,
         "imgsz": args.imgsz,
-        "nms_iou": args.nms_iou,
-        "agnostic_nms": not args.no_agnostic_nms,
-        "max_overlap": args.max_overlap,
-        "suppressed_overlapping_boxes": suppressed,
         "outcomes": {split: dict(counter) for split, counter in outcomes.items()},
         "per_class": {
             split: {
@@ -293,8 +257,6 @@ def main() -> int:
         json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8"
     )
 
-    if suppressed:
-        logger.info("因与更高分的框重叠而丢弃: %d 个", suppressed)
     for split, counter in outcomes.items():
         shown = sum(counter[k] for k in ("missed", "extra"))
         logger.info(