SYNETIC:vision

“Just give me the images.”

You’ve got the pipeline. We’ve got the fuel. SYNETIC:vision delivers massive-scale, precision-labeled visual data for training both traditional CV models and multimodal LLMs
— no crawling, labeling, or wrangling required.

Faster. Cheaper. Smarter. Fully Controlled.

Compared to manual collection, our synthetic datasets cost 99.9975% less. Even versus other synthetic providers, we deliver at one-eighth the price and in hours instead of weeks.

Each image comes with:

Pixel-perfect annotations
Occlusion metadata — whether your target is visible, and by how much
Full camera intrinsics and extrinsics — including focal length, distortion, position, and orientation

Best of all? You control the camera.

Define the exact intrinsics and extrinsics for every shot — so your models don’t just learn what to see, they learn how you want to see it.

Traditional Computer Vision Training Images

Nothing Traditional About It!

What It Is

Photorealistic, domain-specific image datasets with pixel-perfect annotations. Designed for YOLO, RT-DETR, DINO, Detectron2, MMDetection, and custom CV pipelines.

Why it works:

Generated from simulation with total control: objects, lighting, angle, motion, occlusion

Auto-labeled: bounding boxes, masks, depth, normals, keypoints

Supports multi-sensor output: RGB, NIR, thermal, LiDAR, stereo pairs

Export formats: COCO, YOLO, Pascal VOC, custom JSON

Use Cases

Detection, segmentation, tracking
Model bootstrapping and pretraining
Long-tail edge case synthesis
Adversarial scene generation for robustness

Why it works:

Generated from simulation with total control: objects, lighting, angle, motion, occlusion

Auto-labeled: bounding boxes, masks, depth, normals, keypoints

Supports multi-sensor output: RGB, NIR, thermal, LiDAR, stereo pairs

Export formats: COCO, YOLO, Pascal VOC, custom JSON

Images for LLM Augmentation/VLM Training

What It Is

Scene-rich visual datasets tailored for vision-language models. Includes captions, descriptions, QA, and region-level grounding.

Why it works:

Natural language annotations: captions, scene descriptions, object relationships

VQA-ready: templated or dynamic question/answer pairs

Format-compatible: JSON, JSONL, TSV for LLaVA, GPT-4V, Flamingo, Kosmos, etc.

Export formats: COCO, YOLO, Pascal VOC, custom JSON

Use Cases

Multimodal LLM pretraining
Visual reasoning fine-tuning
Robotics and agent grounding
Domain-specific multimodal instruction tuning

Sample Output

Why it works:

Natural language annotations: captions, scene descriptions, object relationships

VQA-ready: templated or dynamic question/answer pairs

Grounding-friendly: referring expressions with coordinate maps

Format-compatible: JSON, JSONL, TSV for LLaVA, GPT-4V, Flamingo, Kosmos, etc.


{

  "image_id": "12345.png",

  "caption": "A piglet eats from a feeder while a sow sleeps in the background.",

  "qa": [

    {"q": "What is the piglet doing?", "a": "Eating from a feeder"},

    {"q": "What’s behind the piglet?", "a": "A sleeping sow"}

  ],

  "regions": [

    {"label": "piglet", "box": [42, 87, 120, 134]},

    {"label": "sow", "box": [200, 190, 350, 300]}

  ]

}

Benchmarks

Model	Pretraining Data	Improvement
LLaVA-style VLM	3M Synetic scenes	+13% on VQA accuracy
GPT-4V fine-tuning	Synetic scenes with captions	+11% grounding precision, +24% task success rate
Instruction Follower	With Synetic scenes	+24% task success rate

theoretical gains – for illustrative purposes

Configure Your Synthetic Image Order

Pricing

# Images Generated	Complexity	Images for LLMs (per Image)	Images for CV (per Image)
# Images Generated< 10,000 Images	ComplexitySimple	Images for LLMs (per Image)$0.15	Images for CV (per Image)$0.10
# Images Generated10,000 – 100,000 Images	ComplexityMedium	Images for LLMs (per Image)$0.10	Images for CV (per Image)$0.05
# Images Generated> 100,000 Images	ComplexityLarge	Images for LLMs (per Image)$0.05	Images for CV (per Image)$0.01

Machine-Learned Humor

SYNETIC:vision

“Just give me the images.”

Faster. Cheaper. Smarter. Fully Controlled.

Traditional Computer Vision Training Images

Nothing Traditional About It!

What It Is

Why it works:

Why it works:

Images for LLM Augmentation/VLM Training

What It Is

Why it works:

Why it works:

Camera Distance

Camera Yaw

Camera Pitch

Stereo Camera Separation

General Settings

Image Configuration

Model Training and SDK Options

Calculated Cost Output

Order Details

Pricing

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