Implement multi-model detection in HVideoTool: updated the detection system to support multiple YOLO models simultaneously, enhancing detection capabilities. Reflected changes in the UI with a new model selection menu and updated documentation in README and CLAUDE.md to guide users on model management and configuration.

hvideotool/core/detection/factory.py

@@ -4,26 +4,33 @@ Kept separate from ``app.py`` so both the app bootstrap and the UI can build
 detectors without an import cycle. Raises ``ValueError`` (not ``SystemExit``) on
 bad config so the GUI can show the message instead of exiting.
 
-Only the YOLO detector is supported — the classic-CV heuristic (and the composite
-mode that combined them) were removed: they were noisy/approximate on real footage.
+YOLO-only, but multi-model: every path in ``config.detector_models`` (ticked under
+models/yolo/<category>/) becomes a YoloDetector tagged with its category, and they
+run together via :class:`~.multi.MultiYoloDetector`. The classic-CV detector and the
+``combined`` composite were removed (noisy/approximate on real footage).
 """
 
 from __future__ import annotations
 
+from pathlib import Path
+
 from ...config import AppConfig
 from .base import Detector
-
-
-def _require_model(config: AppConfig) -> str:
-    if not config.model_path:
-        raise ValueError(
-            "Для детектора YOLO укажите путь к весам (.pt) в Параметрах "
-            "или скачайте модель LADA — см. README."
-        )
-    return config.model_path
+from .registry import category_of
 
 
 def build_detector(config: AppConfig) -> Detector:
-    from .yolo import YoloDetector  # lazy: pulls torch/ultralytics
+    models = [m for m in config.detector_models if Path(m).is_file()]
+    if not models:
+        raise ValueError(
+            "Не выбрана ни одна модель детекции.\n"
+            "Положите веса YOLO в models/yolo/<категория>/ (например models/yolo/mosaic/) "
+            "и отметьте их галочкой в меню «Модели». См. README."
+        )
+    # lazy imports: YoloDetector pulls in torch/ultralytics only when a detect runs
+    from .multi import MultiYoloDetector
+    from .yolo import YoloDetector
 
-    return YoloDetector(_require_model(config), config.detection)
+    return MultiYoloDetector([
+        YoloDetector(m, config.detection, label=category_of(m)) for m in models
+    ])