2 Commits

Author	SHA1	Message	Date
dtoro	fa9b21856f	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>	2026-04-10 13:49:02 +02:00
dtoro	ad007e4cd4	feat: add face detection, recognition, and clustering ... - Create face_embeddings table with pgvector Vector(128) + HNSW index - Implement extract_faces task (YuNet detection + SFace recognition) - Implement recluster_faces task (DBSCAN clustering → Tag(kind=face_cluster)) - Clusters are named "Person N" and get representative_photo_id - cluster_id FK → tags.id, SET NULL on delete for merge/rename support Migration 0005 creates the face_embeddings table. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-10 09:13:41 +02:00