mule-image

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Author	SHA1	Message	Date
dtoro	bbb8e4850c	feat: GPU acceleration support for ONNX Runtime inference Centralize execution provider selection in providers.py with auto-detection and graceful fallback. All ONNX sessions (embedder, detector, face processor, recognizer) now use the configured providers. - New VISION_EXECUTION_PROVIDERS env var: "auto" for GPU auto-detect, or explicit "CUDAExecutionProvider,CPUExecutionProvider" - Provider priority: CUDA > ROCm > OpenVINO > CPU (when set to "auto") - docker-compose.yml includes commented-out NVIDIA GPU deploy section - Supports onnxruntime-gpu as a drop-in replacement for onnxruntime Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-12 22:12:21 +02:00
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

Author

SHA1

Message

Date

dtoro

bbb8e4850c

feat: GPU acceleration support for ONNX Runtime inference

Centralize execution provider selection in providers.py with
auto-detection and graceful fallback. All ONNX sessions (embedder,
detector, face processor, recognizer) now use the configured providers.

- New VISION_EXECUTION_PROVIDERS env var: "auto" for GPU auto-detect,
  or explicit "CUDAExecutionProvider,CPUExecutionProvider"
- Provider priority: CUDA > ROCm > OpenVINO > CPU (when set to "auto")
- docker-compose.yml includes commented-out NVIDIA GPU deploy section
- Supports onnxruntime-gpu as a drop-in replacement for onnxruntime

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-12 22:12:21 +02:00

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

2 Commits