VLM with Synthetic Data
See the difference between a generic open-source Visual Language Model and one fine-tuned on Simuletic's high-quality synthetic dataset.
Simuletic provides VLM-ready security data: synthetic images and video paired with natural language captions and grounded bounding-box annotations (in the JSONL conversation format shown below), built for fine-tuning vision-language models like Qwen2.5-VL and Llava on threat-detection and scene-understanding tasks.
Training data vs. evaluation scenarios
These are two different things. Training data — the annotated images, captions, and video sequences described on this page — is available today and used to fine-tune your own model. Model evaluation — repeatable testing of a trained model against controlled CCTV video scenarios, such as the live demo above — is in development and not yet a shipped product.
If you want early access to the model-evaluation workflow, join the waitlist by emailing contact@simuletic.com or using the contact form below. For ready-to-download training datasets, see our dataset catalog.
Live Video Feed
Generic 2B VLM
Open Source Model
Fine-tuned 2B VLM
Trained with Simuletic threat dataset
The Problem
Beyond Bounding Boxes
Traditional detection sees objects. VLMs understand intent.
Traditional Detection
YOLO / SSD
Confidence: 88%
VLM trained on synthetic data
Reasoning Engine
Subject crouching in blind spot, holding bladed weapon in reverse grip. Posture suggests ambush.
Sentry-Reasoning Dataset
A specialized fine-tuning corpus for real-time threat assessment. Every pixel synthetically generated with ground-truth physics and intent.
100% Privacy Compliant
No real faces. Zero GDPR/CCPA risks.
Hard Negatives Included
Trained to ignore cellphones, umbrellas, and tools.
Pixel-Perfect Grounding
Bounding boxes from the generation engine. No human labeling errors.
For Engineers
VLM-Ready Format
Pre-formatted for Qwen2.5-VL, Llava, and OpenVLA. Plug directly into LLaMA-Factory or MLX.
{
"id": "sim_threat_0921",
"image": "sentry_dataset/v2/train/0921.jpg",
"conversations": [
{
"role": "user",
"content": "Analyze the immediate safety of this scene."
},
{
"role": "assistant",
"content": "STATUS: DANGER.
Reasoning: A <ref>male subject</ref><box>[312, 105, 450, 620]</box>
is aggressively lunging towards the camera.
Object: <ref>combat knife</ref><box>[410, 320, 480, 390]</box>
Action: Immediate intervention required."
}
]
}Why Synthetic Data?
Label Accuracy
Traditional
~85% (Human Error)
Simuletic
100% (Programmatic)
Edge Cases
Traditional
Impossible to capture
Simuletic
Unlimited scenarios
Annotation Depth
Traditional
Bounding Box Only
Simuletic
Reasoning + Intent
Privacy Risk
Traditional
High (Real faces)
Simuletic
Zero
Embodied AI & Robotics Data
Expanding into Vision-Language-Action (VLA) datasets for the next generation of AI.
Failure Recovery
Robot collision and recovery reasoning
Drone Inspection
Structural defect reasoning
Stop training your AI to guess.
Start training it to think.