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/config.py

@@ -50,8 +50,13 @@ class FacesSettings(BaseModel):
     """YuNet + SFace face detection/recognition settings"""
     enabled: bool = True
     min_face_size: int = 40
-    recognition_threshold: float = 0.6
-    cluster_eps: float = 0.25
+    recognition_threshold: float = 0.65
+    cluster_eps: float = 0.5
+
+class ClassifierSettings(BaseModel):
+    """CLIP zero-shot content classification settings"""
+    enabled: bool = True
+    min_confidence: float = 0.3
 
 class VisionSettings(BaseModel):
     """AI vision pipeline settings. Disabled when running on SQLite
@@ -63,6 +68,7 @@ class VisionSettings(BaseModel):
     ocr: OCRSettings = OCRSettings()
     detector: DetectorSettings = DetectorSettings()
     faces: FacesSettings = FacesSettings()
+    classifier: ClassifierSettings = ClassifierSettings()
     worker_concurrency: int = 2
 
 class MulitaConfig(BaseModel):