Run Joke Model

Machine-Learned Humor

Training Data at the highest Quality

Maximum control
over every feature

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Ultra high quality

Just 1¢ per image

Generating synthetic training images is what we do.

Each image is photorealistic, physics-based, and training-ready. Starts at 1¢ per image. No manual data labeling. No long waits. Just results.

Proven across tough use cases: Rare & Edge Cases

  • Looking for moon rocks
  • Endangered species recognition
  • Military-grade targets with only a few public images

Difficult Problems Solved

  • Calculate rock tonnage per hour from belt footage
  • Identify every apple type regardless of state
  • Detect crop emergence across massive acreage
folder structure associated with synetic datasets
list of synetic dataset files

Is synthetic data actually good enough?

We believe the results speak for themselves. Explore the quality and precision of our synthetic data with these free sample datasets. Each is designed to tackle distinct computer vision challenges and accelerate your model development, from the farm to the warehouse.

Agricultural Defect Detection

Download our meticulously crafted apple dataset and see the difference for yourself. This sample showcases Synetic’s high-fidelity imagery and rich metadata, designed for training robust models in agricultural automation.

This free sample includes:

  • 3,300 high-resolution images of apples
  • Three distinct classes: Healthy, Defected, and Diseased
  • Comprehensive metadata and annotations

Logistics & Warehouse Automation

Tackle complex logistics challenges with our warehouse box dataset. It’s built to train powerful models for inventory management, robotic picking, and package tracking in cluttered, real-world environments.

This free sample includes:

  • 2,200 high-resolution images of varied boxes
  • Diverse conditions: Various sizes, damage states, and stacking
  • Precise metadata for object location and occlusion

Not your grandma’s training data

  • Bounding Boxes, Segmentation or KeyPoint annotations
  • RGB, IR, LiDAR and Stereo sensor types
  • Occlusion metadata
  • Camera intrinsics and extrinsics
  • Optional model training available
  • Optional SDK wrapper available
  • Fast: From use case to trained model in as little as under a day.
  • Cost: Synetic is so affordable, it’s creating a whole new economy for Computer Vision.
  • Performance: Able to run on existing architecture with better results.
  • Additional features: Go beyond the bounding boxes with ROI driving capabilities.
  • Accessible: No ML degree required.
  • Accurate computer vision systems, without the limitations of manual annotations and generative AI.

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

Grounding-friendly: referring expressions with coordinate maps

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

Use cases:

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

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]}

  ]

}

Let’s build something real.

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