AI Verse - Resources
Our products create unbiased, labeled, synthetic datasets ideal for training top-performing Computer Vision AI models.
Why Pixel Perfect Labels Matter in Computer Vision Model Training
When it comes to training high-performing computer vision models, the phrase “garbage in, garbage out” couldn’t be more relevant. Among the many factors that influence a model’s performance, data annotation stands out. For applications like image classification, object detection, and semantic segmentation, pixel-perfect labels can mean the difference between mediocre and exceptional results. The Importance […]
AI Verse Joins DIANA’s 2025 Cohort: Advancing AI Training Across the NATO Alliance
AI Verse is proud to announce its selection to DIANA’s prestigious 2025 cohort, marking a significant milestone for the company. Out of over 2,600 applications from leading innovators across the NATO Alliance, AI Verse proudly stands among the 75 companies chosen to participate in this Accelerator Programme. DIANA, NATO’s Defense Innovation Accelerator for the North […]
Computer Vision Applications in Military
From boosting surveillance to powering autonomous drones, computer vision is creating a new frontier in defense. Add synthetic image generation to the mix, and you have an innovative combination. Let’s dive into its most impactful applications and how these technologies are reshaping military capabilities. Surveillance and Reconnaissance Effective surveillance forms the backbone of modern defense, […]
How to Plan Your Annual Budget to Accommodate Synthetic Data
As the year comes to a close, many organizations are deep into annual budget planning. This is the perfect opportunity to consider how synthetic images can play a role in your operations for the upcoming year. By offering data diversity, annotation accuracy, and scalability, synthetic images address many challenges faced by organizations relying solely on […]
Five Trends in Computer Vision for 2025
As we approach 2025, the computer vision landscape is being reshaped by advances in AI, hardware, and interdisciplinary integration unlocking new possibilities for optimizing model performance and addressing challenges once considered impossible. Here are five key trends to watch: 1. Edge AI The demand for real-time decision-making is driving the optimization of computer vision models […]
2025 Will Be the Year of Computer Vision for These Industries
As industries gear up for 2025, computer vision is emerging as a transformative force across sectors, with its ability to interpret and analyze visual data at unprecedented levels. From enhancing public safety to optimizing retail operations, this technology is driving innovation and efficiency in ways that were once unimaginable. Let’s explore industries that will be […]
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
