Semi-Automated
Image Labeling

Create training data faster with AnnoWiz.

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High Speed Data Annotation

High-quality training data is the core value of AI projects

AnnoWiz helps create high-quality training data more efficiently based on Semi-Automated Data Labeling technology.

Now, start AI training data Annotation with AnnoWiz!

Auto-Annotation

In the process of pre-processing of well-known class, which is highly in demand in the industry, the processing time for AI training data can be significantly reduced to 1/10 by automating the bounding box and polygon segmentation work with 70%~99% accuracy depending on the class.

Annotator Metrics

AnnoWiz provides tools to quickly analyze annotators’ work situation. It is possible to set the authority divided into Admin (Administrator), Annotator (Annotator), and Observer (Inspector), and provides an analysis environment for real-time monitoring of task assignment and progress.

Review and QA

A full-time project manager who communicates with clients, conducts real-time monitoring, and project management, and a reviewer specialized in quality inspection conduct data inspection while being aware of issues. Through the AI ​​prediction function, the inspector’s work time and fatigue are lowered and processed into high-quality data.

Privacy & Security

Automated personal information de-identification is provided to overcome the limitations of data use due to privacy issues. In addition, for companies where data security is important, we provide an On Premise (server-installed) solution that can be operated in a closed network.

Find Your Ideal Training Data

Half of your AI’s success stems from the correct annotation of training data.
Use one of the tools below to delineate what your AI needs to learn in your images.

With AnnoWiz’s auto polygon annotation technology, object polygon annotation in BBOX is automatically created when the desired object is drawn in BBOX. The created polygon is created with only the optimum point and can be easily modified.

This is a function which annotates the boundary (contour) information of the object. It is for learning artificial intelligence models such as Instance Segmentation.

This is a function that annotates the class and bbox information of the object in the image. It is for learning artificial intelligence models such as object detection.

It defines a point which can represent an object or marker. Keypoint can be used alone or in combination to form a point map that defines the pose of an object.

Polyline is a function that annotates the line of desired ones. Polyline is used for model learning such as a lane detection in autonomous driving.

Cuboid is a function that annotates height, width, depth, rotation and relative position information. It is used in models such as 3D object detection.

A tag on an annotation. Attributes describe objects in greater detail than the class name itself. They can define discrete features like color, or continuous ones like age. Attributes help AI classify objects after detecting them.

Annotation AI in the NEWS

The history of Annotation AI begins now and does not stop.

Review

The construction of learning data for the development of self-driving AI models is most important because the lives of pedestrians and drivers are at stake.
Thanks to Annotation AI's detailed learning data construction, we are closer to the advancement of autonomous driving technology.
Sang Hyun Park
(Senior Researcher, Mobility Platform Research Center)
Korea Electronics Technology Institute (KETI)
If you are expecting reliable data preprocessing, Annotation AI is recommended. AnnoWiz accelerated the development of an automatic growth monitoring system for Korean cattle.



Jaehyun Ryu (AI Developer, CEO)
Zeron Tech
We are developing autonomous driving wheelchairs for people with disabilities. Annotation AI's Object Detection technology development collaboration enables the establishment of object recognition, judgment, and avoidance scenarios.


Dongguk Yoon
(AI Developer, Hyundai Motors Zero One Creator)
WizGene

Partners

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