dtoro
fa9b21856f
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>
61 lines
1.5 KiB
Plaintext
61 lines
1.5 KiB
Plaintext
# Core dependencies
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fastapi==0.109.0
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uvicorn[standard]==0.27.0
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python-multipart==0.0.6
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# Database
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sqlalchemy[asyncio]==2.0.25
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aiosqlite==0.19.0 # SQLite escape hatch (docker-compose.sqlite.yml override)
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asyncpg==0.29.0 # async Postgres driver (default)
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psycopg2-binary==2.9.9 # sync Postgres driver, used by Alembic CLI
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pgvector==0.2.5 # pgvector SQLAlchemy types
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alembic==1.13.1
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# Redis and Celery
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redis==5.0.1
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celery==5.3.6
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flower==2.0.1
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# Image processing
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# pyvips==2.2.1 # Optional - having compatibility issues, using Pillow as fallback
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# rawpy==0.19.0 # Optional - numpy compatibility issues, using Pillow as fallback
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pillow==10.2.0
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pillow-heif==0.15.0
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imagehash==4.3.1 # perceptual hash for duplicate detection
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imageio==2.33.1
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imageio-ffmpeg==0.4.9
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# Video processing
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ffmpeg-python==0.2.0
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# Metadata extraction
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pyexiftool==0.5.6
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# File watching
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watchfiles==0.21.0
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# Vision pipeline (ONNX Runtime CPU inference)
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onnxruntime==1.18.1
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open-clip-torch==2.24.0 # tokenizer + export helper; inference via ONNX
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rapidocr-onnxruntime==1.3.22
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scikit-learn==1.4.0 # DBSCAN for face clustering
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insightface>=0.7.3 # RetinaFace + ArcFace face detection/recognition
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numpy>=1.26.0,<2.0
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# Utilities
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pyyaml==6.0.1
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pydantic==2.5.3
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pydantic-settings==2.1.0
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python-dotenv==1.0.0
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httpx==0.26.0
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aiofiles==23.2.1
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# Security and authentication
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python-jose[cryptography]==3.3.0
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passlib[bcrypt]==1.7.4
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# Development
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pytest==7.4.4
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pytest-asyncio==0.23.3
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black==23.12.1
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ruff==0.1.11 |