This paper does not describe a novel method. Instead, it studies an essential foundation for reliable benchmarking and ultimately real-world application of AI-based image analysis: generating high-quality reference annotations. Previous research has focused on crowdsourcing as a means of outsourcing annotations. However, little attention has so far been given to annotation companies, specifically regarding their internal quality assurance (QA) processes. Therefore, our aim is to evaluate the influence of QA employed by annotation companies on annotation quality and devise methodologies for maximizing data annotation efficacy. Based on a total of 57,648 instance segmented images obtained from a total of 924 annotators and 34 QA workers from four annotation companies and Amazon Mechanical Turk (MTurk), we derived the following insights: (1) Annotation companies perform better both in terms of quantity and quality compared to the widely used platform MTurk. (2) Annotation companies' internal QA only provides marginal improvements, if any. However, improving labeling instructions instead of investing in QA can substantially boost annotation performance. (3) The benefit of internal QA depends on specific image characteristics. Our work could enable researchers to derive substantially more value from a fixed annotation budget and change the way annotation companies conduct internal QA.

Conquering AI horizons is now even harder than it was in the very beginning, when artificial intelligence was considered a science fiction. Sophisticated, AI-driven solutions are permeating nearly every aspect of our lives like Data Labeling. More AI, though, requires more data that underpins these tech solutions. Say you are working on the new project -- a face recognition system for a large enterprise. First, you need to train the model to recognize human faces by feeding it with a decent amount of labeled training data. Now the question is, where to find the most perfectly annotated datasets?

With a variety of businesses integrating AI technology and machine learning models into their business practices, AI has become less of a novelty and more mainstream over the past few years. With ever-growing amounts of data generated worldwide, you are likely already in possession of the data you need for your machine learning models and industry-specific use case. Cogito is one of the top data annotation companies with its wide array of data annotation and labeling services. As an industry leader in the AI and machine learning space and a premier AI training data procurer, it can be your true ally in integrating automation into your business processes. Getting us on board for annotating and labeling the raw & unstructured datasets and validating the training data can get you sorted for the automation goals.

If you've ever built a machine learning algorithm, you'll know that gathering labeled datasets is a tremendous undertaking. Trying to conduct data annotation in-house only distracts teams from what they do best: building a strong AI. Outsourcing data annotation services is a proven way for teams to boost productivity, decrease development time and stay ahead of the competition. Individuals, researchers, companies, and governments are increasingly turning to data annotation companies as a viable solution to obtain both crowdsourced annotators and off-the-shelf annotation tools. As the number of AI training data service providers grows, how do you decide which to trust?