Synthetic data for fast AI development
Train and deploy a production ready computer vision model in no time. All you need is a simple 3D Model
How does it work?
Your custom trained computer vision model using synthetic data for fast AI development in enterprises, four simple steps
1. Upload CAD Data
Upload a CAD file or a 3D scan of the object you want use for training your computer vision model
2. Choose AI model
Depending on your application, choose the right AI model and architecture from our selection
By generating thousands of labelled images, we create the perfect training data set for your desired AI Model.
After your model is trained, download it from our site. Our easy Python examples will show you how to integrate it in minutes.
Creating computer vision models is time consuming and expensive. Not with us!
We use synthetic data for fast AI development in enterprises.
Easy to use & without prior knowledge
Upload your 3D / CAD data. Our automated pipeline takes care of the rest.
Absolutely perfectly labeled
Synthetic images come pre-labeled and annotated, reducing the potential for human error.
Unlimited data diversity
Generate training data capturing edge-case scenarios, what-if situations, environmental variations and more.
Up to 90% cost savings
Generate massive datasets without breaking your budget, at a fraction of the cost of real-world data collection.
Up to 30x faster model development
Shorten training iteration cycles and accelerate deployment of computer vision models.
Up to 30% more accurate detections
Training with purely synthetic images or augmenting with a small sample of real images greatly improves your model performance.
Supported AI Types
Computer Vision Task
With SYNIO, you will not only be able to solve any task your ML project requires but do so with speed and accuracy like never before. Our online platform is designed to house all cutting-edge AI models for every problem imaginable. Ultimately, we want to even the playing field when it comes to who has access to computer vision technology.
Industry Use Cases
Flexible Robotic Assembly
Robotic Picking, Sorting & Kitting
Warehouse & Retail
Questions? We have the answers.
If a question is not listed, please do not hesitate to contact us directly. The FAQs will be extended continuously.
Synthetic data is a cost-effective and efficient way to generate data for training machine learning models. Unlike real data, which can be expensive and time-consuming to collect, synthetic data is generated by algorithms and is therefore readily available. In addition, synthetic data is not subject to the same privacy concerns as real data, as it is fully anonymous and cannot be traced back to individuals. Synthetic data is also more flexible than real data, as it can be generated to include edge cases and rare events that might not be captured in real-world data sets. As a result, synthetic data help our machine learning models to achieve higher levels of accuracy.
We support the .OBJ (Wavefront) format. We plan to extend our support to multiple CAD formats. Most CAD software support export in .OBJ format. If you experience any trouble in conversion to .OBJ we will be happy to help – send an e-mail to our support and we will assist.
The supported models are YOLO5 and Mask R-CNN. We are working on adding support for additional popular models.
The trained models can be downloaded in PyTorch compatible format (.PT). Within the downloaded package you can find example code that demonstrates how to load the model and run inference.
In recent years, the field of machine learning has witnessed tremendous advancements, primarily driven by the availability of large-scale labeled datasets. However, gathering such datasets
You have CAD models instead of OBJ files? We’ll provide you with tips and tricks for converting your CAD models to OBJs so you can take advantage of all that Synio has to offer.
By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated Gartner The following quote from Gartner
What is AI training data? AI training data is a set of input data and corresponding annotations used to train machine learning models. The input