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)