The data annotation process is the engine of any computer vision based AI project. In order to create cutting edge machine learning models with world changing applications, innovators need access to precise training data at scale. In order to provide this level of labeled image and video data companies and professional annotation services utilise annotation tools.
A data annotation tool is a piece of software that is used to create high-quality, labeled training images and video for machine learning. There are a wide range of annotation tools available to AI companies: from open source tools that can be modified by developers, to freeware applications that can be used at no cost.
However, there are also an increasing number of commercially produced data annotation tools, like Keylabs, that provide an all in one package for growing companies. When selecting between these commercially available products it is vital to know what qualities make for the best image and video annotation tools. This blog will identify five of these key areas and show how video and image annotation tool features can facilitate AI development.
Applying labels to images and video is, of course, the essential function of annotation tools for computer vision training data. There are a variety of annotation methods that can be employed for a variety of use cases. The core annotation methods include: Bounding boxes, Skeletal, Polygon, Segmentation, Point annotation and Polyline.
Not all annotation tools feature every type of annotation technique, choosing instead to optimize a particular labeling task. However, having access to the full range of labeling methods can be particularly helpful for projects that desire flexibility and the capacity to adapt to changing data needs.
To meet pressing development deadlines AI projects need accurate data to be delivered on time. This necessitates an annotation process that is streamlined for efficiency and expedited by automated annotation features. Keylabs is an example of a commercially produced annotation tool that speeds up annotation by utilising time-saving labeling aides.
Shape interpolation uses classless object tracking to localize objects across multiple frames of video rather than having an annotator manually label the object in each frame. Automatic object labeling also accelerates annotation by transforming box labels into polygon outlines.
Precision is essential for all computer vision based AI models. Accuracy of labeling in training data feeds leads to high levels of performance and safety in finished systems. Again, smart annotation tool features can significantly improve the precision of labeled images and video. Classification options make the annotation workflow considerably more efficient and precise by providing annotators with easy access to label maps, classes, attributes, and specific annotation types.
The best annotation tools have the capacity to ease the management burden of large annotation operations. Workflows can be made visible through user friendly interfaces where managers can track the progress of tasks and specific workers. Jobs can be assigned through the platform and detailed productivity metrics can provide a holistic view of the status of any part of the project.
Quality control and verification is the final guarantor of pixel-level precision in training data sets for computer vision. Tools like Keylabs allow managers to review annotations, flag errors, and grade accuracy. These grades can then be used to assign annotation tasks going forward. This process of real-time feedback ensures that performance levels across teams of annotators remain consistently high.
Transform Your Data Annotation Process with Keylabs
Keylabs is an annotation tool designed and built by annotation specialists. This state-of-the-art platform combines a user-friendly interface with high performance annotation features and management options. Contact a team member to book your personalized demo today.