3 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	649437dc85	feat: add embeddings pipeline and semantic search endpoint ... Wire the full embedding flow: - Rewrite Embedding model to use pgvector Vector(512) with HNSW index - Add embed_photo, vision_fanout, backfill_vision Celery tasks on dedicated `vision` queue - Hook vision_fanout into generate_thumbnails completion - Add POST /api/v1/photos/search with hybrid RRF ranking (semantic-only for now; FTS leg added in PR5) - Stub ocr_photo, detect_objects, extract_faces tasks for later PRs Migration 0003 drops/recreates the embeddings table (was never populated). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-10 09:07:32 +02:00
dtoro	46a0d7aba8	feat: structure	2026-04-06 23:30:19 +02:00