AI Verse - Resources
Our products create unbiased, labeled, synthetic datasets ideal for training top-performing Computer Vision AI models.
How Automated Annotation with Synthetic Data Elevates Model Training in Computer Vision
In contemporary computer vision development, the shortage of accurately labeled data remains one of the most persistent bottlenecks. Manual annotation is costly, slow, and prone to inconsistency, consuming over 90% of many project resources. Synthetic image generation combined with automated annotation offers a powerful solution by producing massive volumes of precisely labeled images. This accelerates […]
Real-Life Data vs. Synthetic Data: How Computer Vision Engineers Allocate Their Time
Computer vision engineers are at the forefront of teaching machines to “see” and understand the world. Their daily practices, and ultimately the pace of AI innovation, are shaped by the kind of data they use—either real-life imagery painstakingly collected from the physical world, or synthetic data generated by advanced simulation engines. Let’s examine how these […]
Building Better Drone Models with Synthetic Images
Developing autonomous drones that can perceive, navigate, and act in complex, unstructured environments relies on one critical asset: high-quality, labeled training data. In drone-based vision systems—whether for surveillance, object detection, terrain mapping, or BVLOS operations—the robustness of the model is directly correlated with the quality of the dataset. However, sourcing real-world aerial imagery poses challenges: […]
How Synthetic Images Power Edge Case Accuracy in Computer Vision
In computer vision, the greatest challenge often lies in the unseen. Edge cases—rare, unpredictable, or safety-critical scenarios—are where even state-of-the-art AI models struggle. Whether it’s a jaywalker emerging under low light, a military vehicle camouflaged in complex terrain, or an anomaly appearing in thermal drone footage, these moments can derail performance when not represented in […]
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 […]
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
