AI Verse - Resources
Our products create unbiased, labeled, synthetic datasets ideal for training top-performing Computer Vision AI models.
Common Myths About Synthetic Images – Debunked
Despite the rapid advances in generative AI and simulation technologies, synthetic images are still misunderstood across research and computer vision industry. For computer vision scientists focused on accuracy, scalability, and ethical AI model training, it’s essential to separate facts from fiction. We work with organizations that depend on data precision—from defense and security applications to […]
A Practical Guide to Labels Behind Computer Vision Models
In defense and security applications, where precision, reliability, and situational awareness are critical, the performance of computer vision models depends in 80% on the inputted labeled data. Annotation is the process of adding structured information to raw image or video data so that AI systems can learn to interpret the visual world. It enables models […]
How to Build Accurate Computer Vision Models
Computer vision is the field of AI that enables machines to interpret and make decisions based on visual input. Tasks range from classifying images and detecting objects to understanding spatial context and tracking motion over time. But the success of a computer vision model hinges on its ability to generalize across varied, real-world scenarios. Model’s […]
How to Convince Your Team to Invest in Synthetic Image Datasets
Transitioning from real-world data to synthetic datasets isn’t always easy, especially for teams that have relied on conventional methods for years. The most common objections include: The Case for Synthetic Data 1. Faster, Cost-Effective Data Generation Real-world data collection is slow and costly, often requiring extensive fieldwork and manual annotation. Synthetic datasets, on the other […]
How Synthetic Images Reduce False Positives in AI Training
False positives—incorrect detections in AI models—can significantly impact performance, particularly in critical applications such as security, surveillance, and autonomous systems. Synthetic images provide a powerful solution to reduce false positives by offering controlled, high-quality, and diverse training data that enhances model robustness. This article explores how synthetic images can help mitigate false positives and improve […]
Reducing Technical Debt in Your Computer Vision Pipeline with Synthetic Data
Technical debt is a persistent challenge in computer vision development. While quick fixes and short-term optimizations may help deliver models faster, they can lead to inefficiencies and limitations down the road. Understanding different types of technical debt in computer vision projects is crucial for maintaining scalable, efficient, and high-performing AI systems. One powerful way to […]
Synthetic
Datasets
Real World
Images
Speed, Cost, Flexibility
You can build a synthetic dataset for a fraction of the cost of a real-world image dataset. A 3D scene and a fully labeled image matching your use case are produced in seconds. Easily extend your dataset to match each new edge case throughout your development cycle.
Data Collection
Even if possible, in most cases, collecting real-world images is a daunting task. Privacy issues may also complicate the process. Procedural generation of synthetic datasets is a game changer. You create your own images in a few clicks and avoid any privacy issues.
Labelling
You can build a synthetic dataset for a fraction of the cost of a real-world image dataset. A 3D scene and a fully labeled image matching your use case are produced in seconds. Easily extend your dataset to match each new edge case throughout your development cycle.
Optimization
Even if possible, in most cases, collecting real-world images is a daunting task. Privacy issues may also complicate the process. Procedural generation of synthetic datasets is a game changer. You create your own images in a few clicks and avoid any privacy issues.
Winner:
Synthetic Datasets!
The Benchmarks prove it
Research Summary
To evaluate the efficiency of synthetic datasets to train a model, we conducted a series of benchmarks, comparing trainings done with synthetic images against trainings done with real-world images (COCO dataset). As of today, the results were established for two different models (Yolo V5 and Mask R CNN), for three different tasks of increasing difficulty (sofa, bed and potted plant detection). We conducted these tests with a 1000 assets in our database.
Procedure
Real-world image training datasets were extracted from MS Coco (HERE) for each class of interest. We obtained 3682 images containing the label “bed”, 4618 containing the label “couch” and 4624 images containing the label “potted plant” from MS Coco.
For each test, we used our procedural engine to generate a synthetic dataset. For “beds” detection, we used a 63k synthetic dataset, for “couches”, 72k synthetic images and for “potted plants”, 99k images.
We also used Imagenet (HERE) for pre-training models in several experiments.
Validation Datasets were constructed for each class of interest from OpenImage (HERE). We extracted 199 images containing the label “bed”, 799 images for the label “couch” and 1533 images for the label “plant”.
Conclusions
The domain gap between training sets and validation sets or live images is not exclusive to synthetic datasets. It is a general issue which also exists from real images to real images.
In fact, synthetic images are generally more efficient than real images for training models. This might seem counter intuitive because synthetic images are less realistic than real images.
However, image realism is not key to train a model due to the domain gap. Variance and distribution of the parameters are the crucial factors to obtain a model which generalizes well.
Variance and distribution of parameters are not easily controllable with real images.
Models may be successfully pre-trained on synthetic images and fine-tuned on real images or the other way round. It depends on the task and on the model.
BEDS
AI Verse Synthetic Dataset Sample Images
Bed: RCNN
Bed: YOLO
PLANTS & COUCHES
AI Verse Synthetic Dataset Sample Images
Potted Plants: RCNN
Potted Plants: YOLO
Couch: RCNN
Couch: YOLO
