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/base.py

@@ -36,6 +36,13 @@ class FaceDetection:
     quality: float
 
 
+@dataclass
+class ClassificationResult:
+    """A content-type classification."""
+    label: str
+    confidence: float
+
+
 class Embedder(ABC):
     """Generates image and text embeddings (e.g. OpenCLIP ViT-B/32)."""
 
@@ -74,6 +81,15 @@ class ObjectDetector(ABC):
         ...
 
 
+class ContentClassifier(ABC):
+    """Classifies images into content types (screenshot, document, etc.)."""
+
+    @abstractmethod
+    def classify(self, image: np.ndarray) -> list[ClassificationResult]:
+        """Return content type classifications for an RGB image."""
+        ...
+
+
 class FaceProcessor(ABC):
     """Detects faces and extracts recognition embeddings (e.g. YuNet + SFace)."""