The right Data

Data Generation

Our Data Generation, Dataset creation service offers different package sizes to meet your project needs, ensuring that success is not merely defined by the amount of image data provided. We are committed to delivering all necessary data for successful model training, even if it exceeds the initially defined scope.

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Comprehensive Data Generation Process

Detailed Assessment and Optimization Recommendations for Enhanced Data Quality

1 Specifications

We create a detailed specification document that outlines all aspects of the required data. This is based on your project's needs and the insights gathered from our prior Data Insight analysis.

2 Preliminary Data

We provide a sample set of data, allowing you to verify the details, classes, and realism of the images. This step ensures that the generated data aligns with your application requirements and integrates seamlessly with your existing models.

‍Advanced Techniques:
We utilize both 3D rendering and state-of-the-art generative AI approaches to produce high-quality images tailored to your specific needs.

3 Dataset

Upon your approval of the sample data, we deliver the full dataset. Additionally, we can schedule a meeting to provide training advice and ensure optimal use of the data.

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Real vs Synthetic Images

What are synthetic images?

Synthetic images are pictures generated using computer graphics, simulation methods and artificial intelligence (AI), to represent reality with high fidelity.
Synthetic images provide the opportunity to produce vast amounts of varied, high-quality vision data that are optimal for specific situations and edge cases that are challenging to gather in the real-world environment.

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Synthetic image data is annotated

Inconsistent labels and a resource intensive annotation process are a thing of the past.‍ With our method of generating synthetic images, you can completely skip the manual annotation process. We deliver images with pixel precise labels, based on your computer vision task.

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Real image with overlay of 3D bounding boxes of a bin on a conveyor belt with metal parts in it

Semantic Segmentation of a bin on a conveyor belt with metal parts in it

Instance Segmentation of a bin on a conveyor belt with metal parts in it

Depth Channel of a bin on a conveyor belt with metal parts in it

Normal Channel of a bin on a conveyor belt with metal parts in it

Additional Resources

Explore Key Concepts and Benefits of Synthetic Data and corresponding Annotations.

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What is Synthetic Data?

Learn about the fundamentals of synthetic data, its generation process, and its applications in various industries.

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Why Use Synthetic Data?

Understand the benefits of synthetic data, including enhanced model training, cost efficiency, and the ability to generate rare or hard-to-capture scenarios.

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How Real is Synthetic Data?

Explore the realism and accuracy of synthetic data compared to real-world data, and how it can be tailored to match specific use cases.

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Annotations

Explore the critical role of high-quality annotations in dataset preparation. Our synthetic image data comes fully annotated, as our generation process precisely tracks and identifies every element within each image, ensuring consistent and accurate labelling.

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3D Rendering

Our 3D rendering process leverages advanced computer graphics techniques to create highly realistic and detailed synthetic images. This approach allows us to simulate a wide range of scenarios and environments.

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Generative Approaches

Our generative data creation techniques use advanced AI models to enhance realism and add details to the 3D rendered synthetic image.

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Comprehensive Data Generation Process

1

Specifications

2

Preliminary Data

3

Dataset

What are synthetic images?

Synthetic image data is annotated

Additional Resources

What is Synthetic Data?

Why Use Synthetic Data?

How Real is Synthetic Data?

Annotations

3D Rendering

Generative Approaches

Leading us to the next Step

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