It’s estimated that about 80% of total project time for the creation of a computer vision application is spent on organizing and labeling data. Our managed data labeling services will help you get high-quality labels at an appropriate time scale and at the right cost, and our Quality Assurance team will help you ensure annotations are created to your liking before the end of the project.
Data labeling is the curation of data intended for machine learning and AI systems by humans. Data labeling is necessary because computers have limitations when it comes to interpreting image data, and a computer trained to recognize one object will not be able to recognize others unless trained to do so. Labeling data helps a computer vision system recognize the objects in the data faster and more efficiently. The job of a human data labeler is to supervise the training of a computer vision system and guide the computer towards recognizing the right objects.
Our expert annotators are able to tackle a wide variety of tasks, correctly recognizing and annotating even highly similar object classes. We will work with you to get you your annotated data in a timely fashion and make sure your images are annotated to your liking.
Poor quality image labels can result from improper labeling practices and use of the wrong labeling tools. Our trained labeling team uses the best tools for the job and annotates with skill and precision.
Creating annotations for visually similar classes can be a difficult and time-consuming task, requiring extreme precision. Our quality assurance process lets you investigate the labels for your classes and be sure that even the most similar objects are categorized correctly.
The vast majority of the time spent creating a computer vision application is spent collecting and labeling data. It’s important that this process is streamlined wherever possible. Our trained annotators can save you from having to deal with problems like using the wrong tools, labeling re-work, and collaboration problems.
Our data labeling process can save you a lot of money in addition to time. Instead of having to pay annotation teams for double time if a mistake is made, or paying data scientists high salaries to wrangle and transform data, our professional annotators can efficiently carry out these tasks at a fair price.