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Expand HomeSec-Bench VLM Scene Analysis from 35 to 47 tests with a new Indoor Safety Hazards category covering fire, electrical, trip/fall, child safety, and blocked exit scenarios using AI-generated indoor security camera frames. New test scenarios: - Stove smoke, candle near curtain, space heater near drapes, iron left on - Overloaded power strip, frayed electrical cord - Toys on stairs, wet floor - Person fallen, items on high shelf - Open cabinet with chemicals - Cluttered/blocked exit Total benchmark: 131 → 143 tests VLM suite: 35 → 47 tests Version: 2.0.0 → 2.1.0
New DeepCamera analysis skill wrapping the SmartHome-Bench dataset for evaluating VLM performance on video anomaly detection. - SKILL.md with YAML manifest, params, and protocol docs - config.yaml with mode/maxVideos/categories params - run-benchmark.cjs: video download (yt-dlp), frame sampling (ffmpeg), multi-image VLM evaluation, binary anomaly scoring, JSONL protocol - generate-report.cjs: HTML report with confusion matrix, per-category metrics (accuracy/precision/recall/F1), model comparison - fixtures/annotations.json: 99 curated clips across 7 categories (Wildlife, Senior Care, Baby Monitoring, Pet Monitoring, Home Security, Package Delivery, General Activity) - deploy.sh: system dep checks + npm install - Added to skills.json registry and README catalog
- New HomeSafe-Bench skill: 40 indoor safety VLM tests across 5 categories (fire/smoke, electrical, trip/fall, child safety, falling objects) - 26/40 AI-generated fixture frames (remaining pending image gen quota) - Runtime disk space pre-check in SmartHome-Bench (15GB full / 2GB subset) - Register homesafe-bench in skills.json - All datasets download at runtime, not during deployment
Add remaining 14 AI-generated frames via Vertex AI Imagen 3: - child_01, child_04-08 (child safety category) - falling_01-08 (falling objects category) All 40 test scenarios now have matching fixture images: 8 fire/smoke, 8 electrical, 8 trip/fall, 8 child safety, 8 falling objects
numpy 2.x breaks coremltools PyTorch→MIL converter with: 'only 0-dimensional arrays can be converted to Python scalars' - Pin numpy>=1.24.0,<2.0.0 in requirements.txt and requirements_mps.txt - Add runtime numpy version guard in env_config.py export_model() that detects numpy 2.x and gracefully skips CoreML export - Track homesafe-bench package-lock.json
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