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/bootstrap_models.py

@@ -21,20 +21,9 @@ logger = logging.getLogger(__name__)
 
 # (relative_path, url, description)
 # Models with url=None must be pre-exported via export_models.py.
-DOWNLOADS = [
-    # YuNet — face detection (Apache 2.0, opencv_zoo, ~233 KB)
-    (
-        "face/yunet.onnx",
-        "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx",
-        "YuNet face detector",
-    ),
-    # SFace — face recognition (Apache 2.0, opencv_zoo, ~37 MB)
-    (
-        "face/sface.onnx",
-        "https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx",
-        "SFace face recognizer",
-    ),
-]
+# InsightFace (RetinaFace + ArcFace) auto-downloads via the insightface
+# package on first use — no manual download entries needed.
+DOWNLOADS = []
 
 # Models that need manual export via export_models.py
 EXPORTS = [