Simuletic/Surveillance-VLM-Weapon-Knife-Detection-Dataset

This is an open-source dataset large sample of the comprehensive Simuletic Safety VLM Dataset.

Designed for Instruction Tuning Vision Language Models (VLMs), this dataset teaches models to locate weapons and knives, reason about threats, and avoid false positives.

> 🚀 Get the Full Dataset: The complete dataset with extensive scenarios and edge cases is available at Simuletic.com.

Dataset Content

This subset includes high-quality samples covering three critical categories:

1. Knife Detection: Grounded bounding boxes for individuals holding knives.
2. Firearm Detection: Grounded localization for handguns and rifles.
3. Safe / Negative Samples: Verified unarmed individuals to teach the model "Safe" states and reduce hallucinations.

Key Features

• Grounded Conversations: Teaches the VLM to output specific coordinates ([x1, y1, x2, y2]) alongside text descriptions.
• Normalized Coordinates: All boxes use the standard 0-1000 scale (compatible with Qwen-VL/LLaVA), making them resolution-independent.
• Chain-of-Thought Safety: Includes reasoning chains where the model must classify a scene as SAFE or DANGER based on visual evidence.
• VLM-Ready Format: Pre-formatted in standard JSON conversation format (user/vlm).

Data Structure

The dataset uses the industry-standard conversation format compatible with frameworks like LLaMA-Factory and Axolotl.

Sample JSON Entry:

{
    "id": "0",
    "image": "knifes_scenario_01.jpg",
    "conversations": [
        {
            "from": "human",
            "value": "Locate the knife. Output in [x1, y1, x2, y2] format (0-1000)."
        },
        {
            "from": "vlm",
            "value": "Knife detected at [313, 570, 445, 700]."
        },
        {
            "from": "human",
            "value": "Analyze the threat level."
        },
        {
            "from": "vlm",
            "value": "DANGER the individual is carrying a knife, representing an immediate threat."
        }
    ]
}


# Intended Use
Fine-tuning Multimodal LLMs (Qwen-VL, LLaVA, PaliGemma).

Benchmarking safety and grounding capabilities.

Researching autonomous surveillance agents.

### Ethics & License

Synthetic Nature: This data is computer-generated. No real humans were recorded or harmed. 

### License: CC BY 4.0. You are free to use and adapt this data, provided you give appropriate credit to Simuletic.

### Citation
If you use this dataset in your research, please cite:

@dataset{simuletic_fall_detection_2025,
  author = {Simuletic Team},
  title = {Simuletic Synthetic Fall & Incident Detection Dataset},
  year = {2025},
  url = {[https://simuletic.com](https://simuletic.com)}
}

> For commercial licensing and the full dataset featuring diverse environments and advanced edge cases, please visit https://simuletic.com.