NVIDIA Omniverse Replicator

Synthetic Data Generation for Computer Vision Models

Train and deploy computer vision models in days instead of months with our synthetic data generation service

Data Monsters is your best choice

Data Monsters, a consulting company, is an NVIDIA Elite Partner who helps startups and enterprise ML teams to generate images and train CV models.

With over 15 years in data analytics, hundreds of completed projects, and our Elite NVIDIA expertise, we are ready to become your dedicated development team and accelerate the release of your AI product. If you are looking for experts to help you generate and train ML models for your computer vision product, we are at your service.

We will help you:

Analyze the use case and accuracy targets

Design the model and pipeline architecture

Define the required volume and variability of data

Create 3D scenes and randomize them

Render the data in necessary quantities with target variability using our GPU facilities

Train and test the models

Assess the results and design fine-tuning approach

Optimize the models for GPU inference with TensorRT

Configure and deploy DeepStream pipelines (video processing & Gstreamer)

Optimize performance (increase FPS) and accuracy (resolve edge cases)

Work with us

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What is NVIDIA Omniverse Replicator?

NVIDIA Omniverse Replicator is a powerful and versatile tool that allows us to customize the characteristics of the synthetic data, including the lighting, textures, materials, position of objects, and domain randomization, resulting in data that is more representative of the real world.

This helps you train models with more versatile data, resulting in better accuracy and ability to handle real-world conditions and variations.

Most popular NVIDIA Metropolis applications

Self-driving cars

Robotics

Industrial computer vision

Smart cities

Learn more

WHY NVIDIA Omniverse Replicator IS THE BEST IMAGE GENERATION SOFTWARE

The key benefits of Omniverse Replicator

Integrates Seamlessly with Existing Tools

Built on open-sourced standards like USD, PhysX, and MDL, Replicator easily integrates and connects to existing pipelines and can easily export-import assets to and from existing 3D DCC ecosystems.

Easily Extends and Customizes Workflows

Expose Replicator within Omniverse Code, or quickly and easily assemble your custom app to suit your workflow with simple-to-use Omni.UI features and Python scripting.

Helps Achieve Unprecedented Model Accuracy

Generate highly accurate, physically correct, photorealistic synthetic data, leading to more accurate, reliable, and optimized perception models.

Generate Quality Data Faster Than Ever

Replicator easily scales across multi-GPU and multi-node compute resources to easily batch massive synthetic data generation tasks.

Case with torque seal visual inspections (identifying if the bolt marker is intact or broken):

Note that the app is running in real-time at a high speed on an isolated Siemens IPC520A TensorBox (not connected to the internet). In the last part of the video, you can see how the system performs with exceptionally quick movements of the object.

Popular Applications

Self-driving cars

Synthetic data is used to train and validate the algorithms that enable self-driving cars to navigate roads and make decisions in real-time. By generating large quantities of synthetic data that mimics real-world driving conditions, self-driving car companies can train their algorithms on a wide range of scenarios and ensure that they perform accurately and safely on the roads.

Robotics

Synthetic data is used to train and validate the algorithms that control robots and enable them to perform tasks in a variety of environments. By generating synthetic data that mimics real-world scenarios, businesses and organizations can train their robots to perform tasks accurately and reliably in a range of conditions, including various lighting and weather conditions.

Industrial computer vision systems

Synthetic data is used to train and validate the algorithms that enable industrial computer vision systems to accurately identify and classify objects and make decisions based on that data. By generating synthetic data that mimics real-world conditions and variations, businesses can ensure that their industrial computer vision systems are reliable and accurate.

Smart cities

Synthetic data is used to train and validate the algorithms that enable smart cities to operate efficiently and effectively. By generating synthetic data that mimics real-world conditions and variations, businesses and organizations can train their algorithms to recognize and classify objects and make decisions based on that data. This can be used to improve traffic flow, optimize energy usage, and enable other smart city applications.

Data Monsters
is your best NVIDIA GPU-based Computer Vision implementation partner

Data Monsters, a Palo Alto-based AI consulting company, is an NVIDIA Elite Partner who helps funded startups and enterprise R&D teams design and implement Computer vision software and hardware solutions and products on NVIDIA GPUs. With our 15 years in AI, hundreds of completed projects, and Elite NVIDIA expertise, we are ready to become your trusted development team and accelerate releases of your AI product.

As an Elite Partner, Data Monsters has early and extended access to NVIDIA Computer vision frameworks: Metropolis, DeepStream, Triton, TAO, and others. We have the right hardware and software components to experiment with the latest NVIDIA frameworks several months before official public release. Our direct connection with the development team at NVIDIA helps to follow the best deployment practices, optimize configuration settings, calibrate deployed pipelines, and adapt real-time streaming to different GPU chips.

IF YOU TRAIN COMPUTER VISION MODELS AND NEED MORE DATA, CALL DATA MONSTERS

NVIDIA OMNIVERSE REPLICATOR is a software that requires special knowledge to deploy, set, and use. Your team may be spending too much time on experimentation and adaptation. Data Monsters can help you to accelerate your success.

Services we provide:

Analyze the use case and accuracy targets

We start by analyzing your specific use case and the target accuracy you need to achieve. This helps us understand the nuances of your data and how it needs to be generated.

Design the model and pipeline architecture

We can help you design the pipeline and choose the models that provide the best accuracy and performance for your individual case.

Define the required volume and variability of data

Based on your use case, architecture, and target accuracy, we determine the target volume and variability of the data that is required.

Create 3D scenes and randomize them

We build necessary 3D scenes based on your images and specifications, using CAD models if available or building the scene from scratch. We then introduce randomization factors such as variable lighting, camera position, object positions, textures, materials, and backgrounds to ensure that the data is representative of real-world conditions.

Render the data in necessary quantities with target variability

Using our GPU servers, we generate the synthetic data quickly and efficiently, ensuring that we can control the process and produce the data in the required quantities and with the desired variability.

Train and test the models

We can either provide you with the synthetic data or train and test the models using the data, providing you with turn-key models that are ready for integration into your product or solution.

Assess the results and design fine-tuning approach

If additional training is needed, we provide recommendations on how to fine-tune the models using real data or additional augmentations.

Optimize the models for GPU inference with TensorRT

TensorRT optimizes deep learning models for efficient deployment by optimizing their inference performance. Using TensorRT can significantly improve model performance, making it suitable for production environments.

Configure and deploy DeepStream pipelines

We can help you set up and optimize a stream processing system for deep learning applications, including designing the pipeline architecture, integrating components, and optimizing the pipeline for performance and scalability.

Optimize performance and accuracy

We work to optimize the performance and accuracy of your computer vision models by fine-tuning model hyperparameters, adjusting the architecture of the model, and using techniques such as quantization or pruning to make the model more efficient. We also address edge cases where the model performs poorly by analyzing and generating more data to improve the model's generalization capabilities or developing custom solutions for specific edge cases.

Don't go it alone - this work can take months of tinkering. Data Monsters has the relevant experience to help you design the system and accelerate your product releases.