NOT ENOUGH DATA TO TRAIN MODELS?

Training and testing computer vision models can be a time-consuming and resource-intensive process due to its challenges:

Lack of data
Underrepresentation of corner cases
Insufficient robustness and accuracy of models
Human errors in labeling
Undefined ground truth
RGBD camera depth layer labeling
Time and resources to collect the dataset
Organizational barriers to install cameras at the initial stages
Going all over again if camera or lighting setup changes

To overcome this challenge, synthetic data generation offers a cost-effective solution by creating artificially generated data that mimics the real world, enabling the training of accurate and robust computer vision models. Our synthetic data generation service at Data Monsters utilizes advanced techniques and tools, such as NVIDIA Omniverse Replicator, to generate high-quality synthetic data in large quantities. This allows you to build more accurate and reliable computer vision models without the challenges and limitations of real-world data collection.

Our team at Data Monsters consists of 3D artists, engineers, and data scientists who use your photos, images, process descriptions, and CAD models as input to create the synthetic data.

We also train, test, and optimize the models using our own GPU clusters.

In the end, we provide you with the necessary volumes of perfectly labeled data and pre-trained computer vision models that are ready for integration into your products and pipelines.

We will help you:

Analyse 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

Learn more about our services >

What is Synthetic Data Generation?

Synthetic data for computer vision systems is created using advanced techniques and tools, such as 3D rendering and image manipulation software, to mimic real-world data.

Synthetic data can be used to augment, supplement, or substitute real data sets and is particularly useful when real data is limited or when it is important to evaluate the model's performance under a wide range of conditions.

There are several benefits to using synthetic data for training and testing computer vision models:

Large quantities of data: Synthetic data can be created in large quantities, making it a useful option when real data is scarce or difficult to obtain.
Wide range of scenarios and conditions: Synthetic data can be easily controlled and varied to cover a wide range of scenarios and conditions, making it a useful tool for testing a model's performance under different conditions.
Specific features and annotations: Synthetic data can be created with specific features or annotations, such as object bounding boxes or segmentation masks, which can be used to train and evaluate object detection or segmentation models.
Improving generalization capabilities: Synthetic data can be used to augment real data sets, helping to improve the generalization capabilities of the model and reduce overfitting.
Cost-effective: Synthetic data can be generated quickly and efficiently, making it a cost-effective alternative to real-world data collection.
No human error: Synthetic data is free from human error in labeling, which can be a common issue with real data sets.
Flexible and scalable: Synthetic data can be easily modified to reflect changes in camera or lighting setups, making it a more flexible and scalable solution for training and testing computer vision models.
Testing extreme or unusual conditions: Synthetic data can be used to test the performance of a model in extreme or unusual conditions, helping to identify and address any weaknesses or limitations in the model.

Most popular NVIDIA Metropolis applications

Self-driving cars

Robotics

Industrial computer vision

Smart cities

Learn more

WHY you should choose Data Monsters for Synthetic Data generation

Why Choose Data Monsters for Synthetic Data Generation:

Experienced team

Our team includes experienced 3D artists, engineers, and data scientists who have the knowledge and expertise to generate high-quality synthetic data for a wide range of applications.

Great tools

We use advanced tools and techniques, including NVIDIA Omniverse, to generate synthetic data that is realistic and representative of real-world conditions.

Library of assets

We have a library of assets that we can use to generate synthetic data quickly and efficiently, while also ensuring the quality and accuracy of the data.

Mature process

We have a mature process for generating synthetic data, which allows us to deliver high-quality data in a timely manner.

Hardware

We have access to powerful hardware, including GPU servers, which enables us to generate large quantities of synthetic data in a short amount of time.

Price-performance ratio

Our synthetic data generation services offer an excellent price-performance ratio, making it a cost-effective solution for training and testing computer vision models.

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 different lighting and weather conditions.

Industrial computer vision

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.