Computer vision projects are reliant on steady streams of high quality training data. As companies grow, so do demands for annotated images and video. However, meeting this ever accelerating demand can be a challenge for annotators and annotation management.
Labeling thousands of frames of video involves an enormous investment of time, likewise precisely defining objects in varied image data is a painstaking task. Delays in this important work can result in loss of earnings and cost inefficiencies. For cutting edge projects to flourish it is essential that pixel perfect datasets are assembled as rapidly as possible.
To speed up image and video annotation work companies are turning to experienced annotation providers. By engaging the experts, AI developers can ensure that annotation keeps pace with ambitious development timelines.
This blog will look at three key areas in which outsourcing to professional providers can provide an edge when it comes to annotation speed.
Tools and Techniques
By outsourcing image and video annotation to dedicated services companies can gain the benefits of proprietary annotation platforms and techniques. Different annotation tools employ a variety of methods for speeding up labeling work. For example:
- Keyboard shortcuts make each operation more efficient for operators.
- Quick outlining functions can identify the shape of an object in a given image and create a polygon outline automatically.
- Linear interpolation technology can track objects across multiple frames of video. Annotators are only required to locate the object at the start and the end of the footage, an algorithm takes care of the intervening video.
- Augmented annotation can be incorporated into bespoke platforms. Machine learning can be applied to the annotation process, further accelerating dataset creation.
Management and Workflow
Gains in efficiency and speed can also be made via smart management and workflow organisation. Annotation provider Keymakr incorporates a range of innovative management options into their annotation platform. Analytics and efficient task distribution ensure that annotation projects meet demanding deadlines. Some of these features include:
- Linking annotators and verifiers so that they are working on the same task simultaneously. This allows for frictionless labeling and verification and increases annotation speed overall.
- Splitting longer videos into sections that are then annotated by different operators. These sections can then be merged back together seamlessly whilst preserving object tracking.
- Real time statistics allow managers to track performance metrics for all annotators. These detailed analytics allow tasks to be distributed to operators who are best suited for them/higher performing. This level of awareness and flexibility promotes efficiency and speedy annotation work.
As has been shown innovative annotation platforms and labeling technologies are vital for increasing the pace of dataset production. However, even these important systems are still reliant on the capacity and skill of the annotation workforce. Annotation services that use crowdsourcing or remote workers can suffer from a lack of cohesion and communication with annotators.
Remote workers may also be unfamiliar with specific annotation tools and are more likely to make errors when labeling. Services like Keymakr instead choose to employ in-house teams of skilled annotators, working together in one location. These annotation teams are overseen by experienced managers who are able to quickly communicate needs and troubleshoot as issues arise.
Annotation providers with in-house annotation teams are often best placed to respond to demands for faster annotation. With experienced workforces projects can be scaled up and changed quickly, and efficient management processes mean that usable data can be created as fast as models consume it.
Keymakr leverages skilled annotation teams, experienced managers, and innovative tools to provide fast annotation services to AI leaders. Contact a team member to book your personalized demo today.