C.LABEL

A smart and efficient way to label your data

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Get high-quality labeled data in 2D and 3D, with reduced costs and time input, thanks to the automated technology of C.LABEL

 

High quality training and test data are essential for the development of algorithms and functions for various automotive and industrial applications.

C.LABEL is an easy-to-useflexible and extendable labeling software designed for the efficient annotation of data required for the development of autonomous driving algorithms and functions, as well as the testing and validation thereof.

Key Features:

  • Automatic prediction of labels supported by intelligent algorithms (Active Learning Loop - ALL)
  • Annotation of 2D and 3D sensor data
  • Annotation of key points and parts
  • Available labeling methods: bounding box, semantic and point cloud labeling
  • User-friendly, swift and sleek UI
  • Flexible definition of object categories and properties
  • Automatic calculation of KPIs in respect of labeling process, labeling quality and training
  • Support of various input formats

 

Active Learning Loop  User interface  Labeling methods

CAL – CMORE Automation Levels for Labeling

Efficient labeling comprises the labeling of the data itself and continual quality control as well as effective correction mechanisms.

Thanks to our intelligent processing strategies (e.g. deep learning, interpolation and extrapolation, or automatic object recognition) the manual labeling effort is reduced while the quality of the data in the labeling process is improved.

CAL is our classification scheme for levels of automation in labeling tools. Our development approach strives for fully automated labeling by continuously developing algorithms and functions which can perform on certain automation levels according to project requirements.

Smart Labeling with Active Learning Loop (ALL)

With ALL only a relatively small volume of data needs to be processed manually, rather than labeling the entire database.

Thanks to Active Learning, the user is assisted in the annotation of objects so that only minimal manual adaptions are required. The detector for automatic labeling is updated constantly using corrections provided by the user. This causes the algorithm to learn independently, therefore improving the accuracy of label prediction.

 

 

Highlights:

  • Optimization of the detection algorithm by training the object detector in parallel to manual labeling
  • Reduction and optimization of the training data set by recommendation of frames to be labeled subsequently
  • Increased label quality by correction of the recommendations and relearning
  • Identification and processing of false positive results during labeling

 

 

User-friendly, swift and sleek user interface

The user interface of C.LABEL is designed to minimize the effort of the user by providing special features and enabling a flexible configuration depending on individual needs.

Highlights:

  • Multi-View and tabbing functionality
  • Flexible configuration of object types
  • Definition of global group and class properties
  • Definition of mandatory and optional properties
  • Various element types (checkbox, drop-down list, number, etc.) for defining properties
  • Creation and visualization of comprehensive statistical reports

Multi-view and tabbing 

The multi-view and tabbing functionality allows the user to visualize several data streams simultaneously as well as to drag them freely into the tool frame or view on a second monitor.

Labeling Methods in C.LABEL

Bounding Box Labeling

Point Cloud Labeling

Semantisches Labeling

Benefits to your business

We can help you with the clarification of label specifications and project requirements, so that your ground truth data suits your needs.

We provide you with additional cost-effective solutions for large labeling projects encompassing project management, data management, workforce and much more.

Check out our page Turn-key Labeling Solutions for Automotive Data and learn more about how we can support you.

C.LABEL is part of C.IDS—CMORE Integrated Data Solutions.

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