Benchmark Vision-Language Models on indoor home safety hazard detection.
npx skills add https://github.com/SharpAI/DeepCamera --skill homesafe-bench
Please help me install this Agent Skill. Skill: HomeSafe-Bench Source: https://github.com/SharpAI/DeepCamera/tree/main/skills/analysis/homesafe-bench Command: npx skills add https://github.com/SharpAI/DeepCamera --skill homesafe-bench
This Skill automates the detection of common indoor safety hazards from camera frames, helping to identify potential risks before they cause harm.
Run the HomeSafe-Bench skill using your VLM server at http://localhost:5405.
High-intent search queries and answers about installing and using this skill.
You can evaluate Vision-Language Models for indoor safety hazard detection by benchmarking their performance on static camera frames across fire, electrical, trip/fall, child safety, and falling object categories using AI-generated fixture images.
VLMs can identify home safety hazards across five categories: Fire/Smoke, Electrical, Trip/Fall, Child Safety, and Falling Objects from static camera frames to help prevent potential risks.
Yes, you need a Node.js environment and a running VLM server to execute the safety hazard detection benchmark and evaluate model performance on camera frame analysis.
Yes, you can test VLMs on fire and electrical hazard detection by running the benchmark against your VLM server to evaluate scene analysis accuracy for safety-critical tasks.
The best way to benchmark VLM performance on child safety hazard detection is to evaluate models against AI-generated fixture images that simulate indoor hazard scenarios across multiple safety categories.
No, the hazard detection benchmark evaluates VLMs using static camera frames rather than real-time video streams, providing structured performance metrics for safety-critical scene analysis.
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