feat: replace face pipeline with InsightFace, add content classifier

Face detection/recognition:
- Replace YuNet + SFace with InsightFace buffalo_l (RetinaFace + ArcFace)
- 512-d ArcFace embeddings (was 128-d SFace), migration 0006 resizes column
- Remove YOLO person-bbox workaround — RetinaFace is accurate enough
- Detection threshold 0.65 cleanly separates real faces (0.72+) from
  false positives on dogs/paintings (0.56-0.61)

Content-type classification:
- CLIP zero-shot classifier using native PyTorch text encoder + ONNX
  image encoder for high-quality text-image similarity
- Categories: photograph, screenshot, document, receipt, meme, artwork
- Writes Tag(kind=content_type) per photo via photo_tags
- Margin-based confidence: top-1 vs top-2 score difference
- New ClassifierSettings in config (enabled, min_confidence)
- Wired into vision_fanout pipeline

Tested: 6 real faces from 4 photos (zero false positives), 11/13 photos
classified (8 photograph, 2 artwork, 1 meme).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/services/vision/onnx_backend.py

@@ -9,7 +9,7 @@ bootstrap_models.py.
 import logging
 
 from app.config import VisionSettings
-from app.services.vision.base import Embedder, OCREngine, ObjectDetector, FaceProcessor
+from app.services.vision.base import Embedder, OCREngine, ObjectDetector, FaceProcessor, ContentClassifier
 
 logger = logging.getLogger(__name__)
 
@@ -33,5 +33,9 @@ class ONNXBackend:
         return YOLOv8Detector(self._settings)
 
     def create_face_processor(self) -> FaceProcessor:
-        from app.services.vision.faces import YuNetSFaceProcessor
-        return YuNetSFaceProcessor(self._settings)
+        from app.services.vision.insightface_processor import InsightFaceProcessor
+        return InsightFaceProcessor(self._settings)
+
+    def create_classifier(self) -> ContentClassifier:
+        from app.services.vision.classify import CLIPContentClassifier
+        return CLIPContentClassifier(self._settings)