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/registry.py

@@ -15,7 +15,7 @@ import logging
 from functools import lru_cache
 
 from app.config import settings
-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__)
 
@@ -46,6 +46,11 @@ class ModelRegistry:
         logger.info("Loading face processor (backend=%s)", self._vision.backend)
         return self._load_backend().create_face_processor()
 
+    @lru_cache(maxsize=1)
+    def get_classifier(self) -> ContentClassifier:
+        logger.info("Loading content classifier (backend=%s)", self._vision.backend)
+        return self._load_backend().create_classifier()
+
     @lru_cache(maxsize=1)
     def _load_backend(self):
         """Import and instantiate the configured backend."""
@@ -70,6 +75,8 @@ class ModelRegistry:
             self.get_detector()
         if self._vision.faces.enabled:
             self.get_face_processor()
+        if self._vision.classifier.enabled:
+            self.get_classifier()
         logger.info("Vision model warmup complete")