What is image annotation?

Image annotation is the foundation of supervised learning (SL) artificial intelligence (AI) and the procedure of adding labels to images.
Labels are used to represent features that deep learning (DL) models need to recognize (e.g. Which objects do the image represent? Where are they located in the image?).
Usually, a data labeller uses tags or metadata to identify and define characteristics of the image data you want the computer vision (CV) model to learn to recognize. Accurate labelling is one of the foundations for a precise CV model.

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Yellow bin on a conveyor belt with pixelwise polygon annotation overlay

Example of Pixel Wise Polygon Annotation

Categories of image annotations

Different types of annotations are used to mark different features or properties of the image content. Depending on the use case and CV task at hand (object recognition, object detection, image segmentation), one of the following annotations is used to label the image dataset:

2D Bounding-box
Pixel wise polygons
Class segmentation
Instance segmentation
Points

In 3D computer vision applications, additional annotation channels are used, which would also be provided by a classic industrial 3Dsensor in a real environment:

Surface normal
XYZ-Depth information (point cloud)

Graphic Showing different annotation types.

Synthetic (computer generated) examples of common annotation types (Groundtruth, 3D bounding box, class segmentation, instance segmentation, normal, depth)

Challenges in common Annotation methods

Different types of annotations are used to mark different features or properties of the image content. Depending on the use case and CV task at hand (object recognition, object detection, image segmentation), one of the following annotations is used to label the image dataset:

Organizational overhead and costs
During the labelling process where people manually tag images, there are costs associated with man power and management required to annotate large datasets.
Human in the loop
When image annotation is done by humans, the dataset is prone to human error and bias, when it comes to the consistency of the
labels. Even in case of annotation software tools that are automated to a certain degree, the tools rely on human input as a basis.
Since errors from biased opinions can be costly, domain experts are sometimes the only source of high quality annotated image data.
Consistently high accuracy over the dataset
Common problems are inaccurate or missing labels – in both cases the computer vision (CV) algorithm would learn from misinformation
and never reach required precision levels. Such inaccuracy can be the source of issues such as false positives.

Synthetic image data as an alternative

Thanks to the creation process of synthetic data, no more manual labelling work is required. While generating synthetic data, the process for gathering the label data is completely different from manual annotation. Since the represented scene is simulated in 3D, information such as the positions of features or objects is known at all times. This information only needs to be transferred and stored in the relevant annotation channels. This approach allows us to consistently deliver high quality annotations through the whole dataset, with respect to your use case requirements.

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Additional Resources

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

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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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